From f2068641d4a1c549dc3807c0b74d35f07688224e Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Thu, 26 Feb 2026 13:46:39 -0600
Subject: [PATCH 01/12] v1

---
 amber/src/main/python/pyamber/__init__.py     |   2 +
 .../main/python/pytexera/udf/udf_operator.py  |  88 ++++-
 .../texera/amber/core/tuple/Attribute.java    |   6 +-
 .../DualInputPortsPythonUDFOpDescV2.scala     |   9 +-
 .../udf/python/PythonUDFOpDescV2.scala        |   9 +-
 .../python/PythonUdfUiParameterInjector.scala | 159 +++++++++
 .../operator/udf/python/UiUDFParameter.scala  |  41 +++
 .../source/PythonUDFSourceOpDescV2.scala      |   8 +-
 .../PythonUdfUiParameterInjectorSpec.scala    |  56 ++++
 frontend/src/app/app.module.ts                |   2 +
 .../src/app/common/formly/formly-config.ts    |   2 +
 .../code-editor.component.ts                  |  33 +-
 ...perator-property-edit-frame.component.scss |  14 +
 .../operator-property-edit-frame.component.ts |  58 +++-
 .../ui-udf-parameters.component.html          |  39 +++
 .../ui-udf-parameters.component.scss          |  20 ++
 .../ui-udf-parameters.component.ts            |  71 ++++
 .../ui-udf-parameters-parser.service.spec.ts  |  79 +++++
 .../ui-udf-parameters-parser.service.ts       | 308 ++++++++++++++++++
 .../ui-udf-parameters-sync.service.ts         | 130 ++++++++
 .../types/workflow-compiling.interface.ts     |  18 +-
 21 files changed, 1131 insertions(+), 21 deletions(-)
 create mode 100644 common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
 create mode 100644 common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/UiUDFParameter.scala
 create mode 100644 common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
 create mode 100644 frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
 create mode 100644 frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
 create mode 100644 frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
 create mode 100644 frontend/src/app/workspace/service/code-editor/ui-udf-parameters-parser.service.spec.ts
 create mode 100644 frontend/src/app/workspace/service/code-editor/ui-udf-parameters-parser.service.ts
 create mode 100644 frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts

diff --git a/amber/src/main/python/pyamber/__init__.py b/amber/src/main/python/pyamber/__init__.py
index 01ee5e08279..7a3d45c2ff9 100644
--- a/amber/src/main/python/pyamber/__init__.py
+++ b/amber/src/main/python/pyamber/__init__.py
@@ -27,6 +27,7 @@
     SourceOperator,
     TupleOperatorV2,
     State,
+    AttributeType,
 )
 
 __all__ = [
@@ -41,4 +42,5 @@
     "TupleOperatorV2",
     "SourceOperator",
     "State",
+    "AttributeType",
 ]
diff --git a/amber/src/main/python/pytexera/udf/udf_operator.py b/amber/src/main/python/pytexera/udf/udf_operator.py
index 003225c75c3..59af65f3b86 100644
--- a/amber/src/main/python/pytexera/udf/udf_operator.py
+++ b/amber/src/main/python/pytexera/udf/udf_operator.py
@@ -15,13 +15,87 @@
 # specific language governing permissions and limitations
 # under the License.
 
+import datetime
 from abc import abstractmethod
-from typing import Iterator, Optional, Union
-
-from pyamber import *
+from typing import Any, Dict, Iterator, Optional, Union
 
+import functools
+import datetime
+from abc import abstractmethod
+from typing import Any, Dict, Iterator, Optional, Union
 
-class UDFOperatorV2(TupleOperatorV2):
+from pyamber import *
+from core.models.schema.attribute_type import AttributeType, TO_PYOBJECT_MAPPING
+
+class _UiParameterSupport:
+    _ui_parameter_injected_values: Dict[str, Any] = {}
+    _ui_parameter_name_types: Dict[str, AttributeType] = {}
+
+    # Reserved hook name. Backend injector will generate this in the user's class.
+    def _texera_injected_ui_parameters(self) -> Dict[str, Any]:
+        return {}
+
+    def _texera_apply_injected_ui_parameters(self) -> None:
+        values = self._texera_injected_ui_parameters()
+        # Write to base class storage (not cls) because UiParameter reads from _UiParameterSupport directly
+        _UiParameterSupport._ui_parameter_injected_values = dict(values or {})
+        _UiParameterSupport._ui_parameter_name_types = {}
+
+    def __init_subclass__(cls, **kwargs):
+        super().__init_subclass__(**kwargs)
+
+        # Wrap only methods defined on this class (not inherited ones)
+        original_open = getattr(cls, "open", None)
+        if original_open is None:
+            return
+
+        # Avoid double wrapping
+        if getattr(original_open, "__texera_ui_params_wrapped__", False):
+            return
+
+        @functools.wraps(original_open)
+        def wrapped_open(self, *args, **kwargs):
+            self._texera_apply_injected_ui_parameters()
+            return original_open(self, *args, **kwargs)
+
+        setattr(wrapped_open, "__texera_ui_params_wrapped__", True)
+        cls.open = wrapped_open
+
+    class UiParameter:
+        def __init__(self, name: str, type: AttributeType):
+            if not isinstance(type, AttributeType):
+                raise TypeError(
+                    f"UiParameter.type must be an AttributeType, got {type!r}."
+                )
+
+            existing_type = _UiParameterSupport._ui_parameter_name_types.get(name)
+            if existing_type is not None and existing_type != type:
+                raise ValueError(
+                    f"Duplicate UiParameter name '{name}' with conflicting types: "
+                    f"{existing_type.name} vs {type.name}."
+                )
+
+            _UiParameterSupport._ui_parameter_name_types[name] = type
+            raw_value = _UiParameterSupport._ui_parameter_injected_values.get(name)
+            self.name = name
+            self.type = type
+            self.value = _UiParameterSupport._parse(raw_value, type)
+
+    @classmethod
+    def set_injected_ui_parameters(cls, values: Dict[str, Any]) -> None:
+        # keep for backward compatibility if anything else calls it
+        _UiParameterSupport._ui_parameter_injected_values = dict(values or {})
+        _UiParameterSupport._ui_parameter_name_types = {}
+
+    @staticmethod
+    def _parse(value: Any, attr_type: AttributeType) -> Any:
+        if value is None:
+            return None
+
+        py_type = TO_PYOBJECT_MAPPING.get(attr_type)
+        return py_type(value)
+
+class UDFOperatorV2(_UiParameterSupport, TupleOperatorV2):
     """
     Base class for tuple-oriented user-defined operators. A concrete implementation must
     be provided upon using.
@@ -65,7 +139,7 @@ def close(self) -> None:
         pass
 
 
-class UDFSourceOperator(SourceOperator):
+class UDFSourceOperator(_UiParameterSupport, SourceOperator):
     def open(self) -> None:
         """
         Open a context of the operator. Usually can be used for loading/initiating some
@@ -90,7 +164,7 @@ def close(self) -> None:
         pass
 
 
-class UDFTableOperator(TableOperator):
+class UDFTableOperator(_UiParameterSupport, TableOperator):
     """
     Base class for table-oriented user-defined operators. A concrete implementation must
     be provided upon using.
@@ -123,7 +197,7 @@ def close(self) -> None:
         pass
 
 
-class UDFBatchOperator(BatchOperator):
+class UDFBatchOperator(_UiParameterSupport, BatchOperator):
     """
     Base class for batch-oriented user-defined operators. A concrete implementation must
     be provided upon using.
diff --git a/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java b/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
index 84d52fddced..40373dcbaca 100644
--- a/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
+++ b/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
@@ -21,6 +21,8 @@
 
 import com.fasterxml.jackson.annotation.JsonCreator;
 import com.fasterxml.jackson.annotation.JsonProperty;
+import org.apache.texera.amber.pybuilder.EncodableStringAnnotation;
+import org.apache.texera.amber.pybuilder.PyStringTypes;
 
 import javax.validation.constraints.NotBlank;
 import javax.validation.constraints.NotNull;
@@ -33,7 +35,7 @@
  */
 public class Attribute implements Serializable {
 
-    private final String attributeName;
+    private final @EncodableStringAnnotation String attributeName;
     private final AttributeType attributeType;
 
     @JsonCreator
@@ -49,7 +51,7 @@ public Attribute(
 
     @JsonProperty(value = "attributeName", required = true)
     @NotBlank(message = "Attribute name is required")
-    public String getName() {
+    public @EncodableStringAnnotation String getName() {
         return attributeName;
     }
 
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
index 3f056c96055..a7a02560901 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
@@ -77,6 +77,11 @@ class DualInputPortsPythonUDFOpDescV2 extends LogicalOp {
   )
   var outputColumns: List[Attribute] = List()
 
+  @JsonProperty
+  @JsonSchemaTitle("Parameters")
+  @JsonPropertyDescription("Parameters inferred from self.UiParameter(...) in Python script")
+  var uiParameters: List[UiUDFParameter] = List()
+
   override def getPhysicalOp(
       workflowId: WorkflowIdentity,
       executionId: ExecutionIdentity
@@ -88,7 +93,7 @@ class DualInputPortsPythonUDFOpDescV2 extends LogicalOp {
           workflowId,
           executionId,
           operatorIdentifier,
-          OpExecWithCode(code, "python")
+          OpExecWithCode(PythonUdfUiParameterInjector.inject(code, uiParameters), "python")
         )
         .withParallelizable(true)
         .withSuggestedWorkerNum(workers)
@@ -98,7 +103,7 @@ class DualInputPortsPythonUDFOpDescV2 extends LogicalOp {
           workflowId,
           executionId,
           operatorIdentifier,
-          OpExecWithCode(code, "python")
+          OpExecWithCode(PythonUdfUiParameterInjector.inject(code, uiParameters), "python")
         )
         .withParallelizable(false)
     }
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
index ef4da06cef9..efac09d63b4 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
@@ -79,6 +79,11 @@ class PythonUDFOpDescV2 extends LogicalOp {
   )
   var outputColumns: List[Attribute] = List()
 
+  @JsonProperty
+  @JsonSchemaTitle("Parameters")
+  @JsonPropertyDescription("Parameters inferred from self.UiParameter(...) in Python script")
+  var uiParameters: List[UiUDFParameter] = List()
+
   override def getPhysicalOp(
       workflowId: WorkflowIdentity,
       executionId: ExecutionIdentity
@@ -118,7 +123,7 @@ class PythonUDFOpDescV2 extends LogicalOp {
           workflowId,
           executionId,
           operatorIdentifier,
-          OpExecWithCode(code, "python")
+          OpExecWithCode(PythonUdfUiParameterInjector.inject(code, uiParameters), "python")
         )
         .withParallelizable(true)
         .withSuggestedWorkerNum(workers)
@@ -128,7 +133,7 @@ class PythonUDFOpDescV2 extends LogicalOp {
           workflowId,
           executionId,
           operatorIdentifier,
-          OpExecWithCode(code, "python")
+          OpExecWithCode(PythonUdfUiParameterInjector.inject(code, uiParameters), "python")
         )
         .withParallelizable(false)
     }
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
new file mode 100644
index 00000000000..e6e181aad29
--- /dev/null
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
@@ -0,0 +1,159 @@
+package org.apache.texera.amber.operator.udf.python
+
+import org.apache.texera.amber.pybuilder.PyStringTypes.{EncodableString, EncodableStringFactory}
+import org.apache.texera.amber.pybuilder.PythonTemplateBuilder
+import org.apache.texera.amber.pybuilder.PythonTemplateBuilder.PythonTemplateBuilderStringContext
+
+import scala.util.matching.Regex
+
+object PythonUdfUiParameterInjector {
+
+  private val ReservedHookMethod = "_texera_injected_ui_parameters"
+
+  // Match user-facing UDF classes (the ones users write)
+  private val SupportedUserClassRegex: Regex =
+    """(?m)^([ \t]*)class\s+(ProcessTupleOperator|ProcessBatchOperator|ProcessTableOperator|GenerateOperator)\s*\([^)]*\)\s*:\s*(?:#.*)?$""".r
+
+  private def validate(uiParameters: List[UiUDFParameter]): Unit = {
+    uiParameters.foreach { parameter =>
+      if (parameter.attribute == null) {
+        throw new RuntimeException("UiParameter attribute is required.")
+      }
+    }
+
+    val grouped = uiParameters.groupBy(_.attribute.getName)
+    grouped.foreach {
+      case (key, values) =>
+        val typeSet = values.map(_.attribute.getType).toSet
+        if (typeSet.size > 1) {
+          throw new RuntimeException(
+            s"UiParameter key '$key' has multiple types: ${typeSet.map(_.name()).mkString(",")}."
+          )
+        }
+    }
+  }
+
+  private def buildInjectedParametersMap(uiParameters: List[UiUDFParameter]): PythonTemplateBuilder = {
+    val entries = uiParameters.map { parameter =>
+      val key: EncodableString = EncodableStringFactory(parameter.attribute.getName)
+      val value: EncodableString = EncodableStringFactory(parameter.value)
+      pyb"$key: $value"
+    }
+
+    entries.reduceOption((acc, entry) => acc + pyb", " + entry).getOrElse(pyb"")
+  }
+
+  private def buildInjectedHookMethod(uiParameters: List[UiUDFParameter]): String = {
+    val injectedParametersMap = buildInjectedParametersMap(uiParameters)
+
+    // unindented method; we indent it when inserting into the class body
+    (pyb"""def """ + pyb"$ReservedHookMethod" + pyb"""(self):
+                                                     |    return {""" +
+      injectedParametersMap +
+      pyb"""}
+           |""").encode
+  }
+
+  private def indentBlock(block: String, indent: String): String = {
+    block
+      .split("\n", -1)
+      .map { line =>
+        if (line.nonEmpty) indent + line else line
+      }
+      .mkString("\n")
+  }
+
+  private def lineEndIndex(text: String, from: Int): Int = {
+    val idx = text.indexOf('\n', from)
+    if (idx < 0) text.length else idx
+  }
+
+  private def detectClassBlockEnd(code: String, classHeaderStart: Int, classIndent: String): Int = {
+    val classLineEnd = lineEndIndex(code, classHeaderStart)
+    var pos = if (classLineEnd < code.length) classLineEnd + 1 else code.length
+
+    while (pos < code.length) {
+      val end = lineEndIndex(code, pos)
+      val line = code.substring(pos, end)
+
+      val trimmed = line.trim
+      val isBlank = trimmed.isEmpty
+
+      // a top-level (or same/lower-indented) non-blank line ends the class block
+      val currentIndentLen = line.prefixLength(ch => ch == ' ' || ch == '\t')
+      val classIndentLen = classIndent.length
+
+      if (!isBlank && currentIndentLen <= classIndentLen) {
+        return pos
+      }
+
+      pos = if (end < code.length) end + 1 else code.length
+    }
+
+    code.length
+  }
+
+  private def containsReservedHook(classBlock: String): Boolean = {
+    val hookRegex = ("""(?m)^[ \t]+def\s+""" + Regex.quote(ReservedHookMethod) + """\s*\(""").r
+    hookRegex.findFirstIn(classBlock).isDefined
+  }
+
+  private def findInsertionPointInsideClass(classBlock: String, classIndent: String): Int = {
+    // Insert before the first method definition in the class body.
+    // This preserves existing open() and also preserves class docstrings if present.
+    val methodRegex = """(?m)^[ \t]+def\s+\w+\s*\(""".r
+    methodRegex.findFirstMatchIn(classBlock).map(_.start).getOrElse(classBlock.length)
+  }
+
+  private def injectHookIntoUserClass(encodedUserCode: String, hookMethod: String): String = {
+    val m = SupportedUserClassRegex.findFirstMatchIn(encodedUserCode).getOrElse {
+      return encodedUserCode
+    }
+
+    val classHeaderStart = m.start
+    val classIndent = m.group(1)
+    val classBlockEnd = detectClassBlockEnd(encodedUserCode, classHeaderStart, classIndent)
+
+    val classBlock = encodedUserCode.substring(classHeaderStart, classBlockEnd)
+
+    if (containsReservedHook(classBlock)) {
+      throw new RuntimeException(
+        s"Reserved method '$ReservedHookMethod' is already defined in the UDF class. Please rename your method."
+      )
+    }
+
+    val bodyIndent = inferClassBodyIndent(classBlock, classIndent).getOrElse(classIndent + "    ")
+    val indentedHook = indentBlock((if (classBlock.endsWith("\n")) "" else "\n") + hookMethod.trim + "\n", bodyIndent)
+
+    encodedUserCode.substring(0, classBlockEnd) +
+      indentedHook +
+      encodedUserCode.substring(classBlockEnd)
+  }
+  private def inferClassBodyIndent(classBlock: String, classIndent: String): Option[String] = {
+    val lines = classBlock.split("\n", -1).toList.drop(1) // skip class header line
+
+    lines.collectFirst {
+      case line if line.trim.nonEmpty =>
+        val leading = line.takeWhile(ch => ch == ' ' || ch == '\t')
+        if (leading.length > classIndent.length) leading else classIndent + "    "
+    }
+  }
+  def inject(code: String, uiParameters: List[UiUDFParameter]): String = {
+    val params = Option(uiParameters).getOrElse(List.empty)
+    validate(params)
+
+    // Let pyb encode the user's source normally
+    val encodedUserCode = pyb"$code".encode
+
+    // If there are no UI params, return unchanged code (no hook injection needed)
+    if (params.isEmpty) {
+      return encodedUserCode
+    }
+
+    // Build encoded hook method (contains self.decode_python_template(...))
+    val hookMethod = buildInjectedHookMethod(params)
+
+    // Inject hook into the UDF class body; Python base class will auto-call it before open()
+    injectHookIntoUserClass(encodedUserCode, hookMethod)
+  }
+}
\ No newline at end of file
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/UiUDFParameter.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/UiUDFParameter.scala
new file mode 100644
index 00000000000..71ce2596788
--- /dev/null
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/UiUDFParameter.scala
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.udf.python
+
+import com.fasterxml.jackson.annotation.JsonProperty
+import com.kjetland.jackson.jsonSchema.annotations.JsonSchemaTitle
+import org.apache.texera.amber.core.tuple.Attribute
+import org.apache.texera.amber.pybuilder.PyStringTypes.EncodableString
+
+import javax.validation.Valid
+import javax.validation.constraints.NotNull
+
+class UiUDFParameter {
+
+  @JsonProperty(required = true)
+  @JsonSchemaTitle("Attribute")
+  @Valid
+  @NotNull(message = "Attribute is required")
+  var attribute: Attribute = _
+
+  @JsonProperty()
+  @JsonSchemaTitle("Value")
+  var value: EncodableString = ""
+}
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
index b575612d884..a845f17ad3a 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
@@ -27,6 +27,7 @@ import org.apache.texera.amber.core.virtualidentity.{ExecutionIdentity, Workflow
 import org.apache.texera.amber.core.workflow.{OutputPort, PhysicalOp, SchemaPropagationFunc}
 import org.apache.texera.amber.operator.metadata.{OperatorGroupConstants, OperatorInfo}
 import org.apache.texera.amber.operator.source.SourceOperatorDescriptor
+import org.apache.texera.amber.operator.udf.python.{PythonUdfUiParameterInjector, UiUDFParameter}
 
 class PythonUDFSourceOpDescV2 extends SourceOperatorDescriptor {
 
@@ -54,13 +55,18 @@ class PythonUDFSourceOpDescV2 extends SourceOperatorDescriptor {
   @JsonPropertyDescription("The columns of the source")
   var columns: List[Attribute] = List.empty
 
+  @JsonProperty
+  @JsonSchemaTitle("Parameters")
+  @JsonPropertyDescription("Parameters inferred from self.UiParameter(...) in Python script")
+  var uiParameters: List[UiUDFParameter] = List()
+
   override def getPhysicalOp(
       workflowId: WorkflowIdentity,
       executionId: ExecutionIdentity
   ): PhysicalOp = {
     require(workers >= 1, "Need at least 1 worker.")
     val physicalOp = PhysicalOp
-      .sourcePhysicalOp(workflowId, executionId, operatorIdentifier, OpExecWithCode(code, "python"))
+      .sourcePhysicalOp(workflowId, executionId, operatorIdentifier, OpExecWithCode(PythonUdfUiParameterInjector.inject(code, uiParameters), "python"))
       .withInputPorts(operatorInfo.inputPorts)
       .withOutputPorts(operatorInfo.outputPorts)
       .withIsOneToManyOp(true)
diff --git a/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
new file mode 100644
index 00000000000..ba8c2335c44
--- /dev/null
+++ b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.udf.python
+
+import org.apache.texera.amber.core.tuple.{Attribute, AttributeType}
+import org.scalatest.flatspec.AnyFlatSpec
+import org.scalatest.matchers.should.Matchers
+
+class PythonUdfUiParameterInjectorSpec extends AnyFlatSpec with Matchers {
+
+  private def createParameter(key: String, attributeType: AttributeType, value: String): UiUDFParameter = {
+    val parameter = new UiUDFParameter
+    parameter.attribute = new Attribute(key, attributeType)
+    parameter.value = value
+    parameter
+  }
+
+  it should "inject ui parameter prelude through PythonTemplateBuilder" in {
+    val injectedCode = PythonUdfUiParameterInjector.inject(
+      "print('done')",
+      List(createParameter("date", AttributeType.TIMESTAMP, "2024-01-01T00:00:00Z"))
+    )
+
+    injectedCode should include("UDFOperatorV2.set_injected_ui_parameters({")
+    injectedCode should include("self.decode_python_template")
+    injectedCode should include("print('done')")
+  }
+
+  it should "throw when a key is declared with conflicting types" in {
+    val conflictingParameters = List(
+      createParameter("date", AttributeType.DATE, "2024-01-01"),
+      createParameter("date", AttributeType.TIMESTAMP, "2024-01-01T00:00:00Z")
+    )
+
+    assertThrows[RuntimeException] {
+      PythonUdfUiParameterInjector.inject("print('done')", conflictingParameters)
+    }
+  }
+}
diff --git a/frontend/src/app/app.module.ts b/frontend/src/app/app.module.ts
index b41b1f80b73..d0acf5e5f35 100644
--- a/frontend/src/app/app.module.ts
+++ b/frontend/src/app/app.module.ts
@@ -153,6 +153,7 @@ import { NzTreeViewModule } from "ng-zorro-antd/tree-view";
 import { NzNoAnimationModule } from "ng-zorro-antd/core/no-animation";
 import { TreeModule } from "@ali-hm/angular-tree-component";
 import { FileSelectionComponent } from "./workspace/component/file-selection/file-selection.component";
+import { UiUdfParametersComponent } from "./workspace/component/ui-udf-parameters/ui-udf-parameters.component";
 import { ResultExportationComponent } from "./workspace/component/result-exportation/result-exportation.component";
 import { ReportGenerationService } from "./workspace/service/report-generation/report-generation.service";
 import { SearchBarComponent } from "./dashboard/component/user/search-bar/search-bar.component";
@@ -257,6 +258,7 @@ registerLocaleData(en);
     AgentRegistrationComponent,
     InputAutoCompleteComponent,
     FileSelectionComponent,
+    UiUdfParametersComponent,
     CollabWrapperComponent,
     AboutComponent,
     UserWorkflowListItemComponent,
diff --git a/frontend/src/app/common/formly/formly-config.ts b/frontend/src/app/common/formly/formly-config.ts
index d950bd3690c..f2fa38ab493 100644
--- a/frontend/src/app/common/formly/formly-config.ts
+++ b/frontend/src/app/common/formly/formly-config.ts
@@ -27,6 +27,7 @@ import { PresetWrapperComponent } from "./preset-wrapper/preset-wrapper.componen
 import { InputAutoCompleteComponent } from "../../workspace/component/input-autocomplete/input-autocomplete.component";
 import { CollabWrapperComponent } from "./collab-wrapper/collab-wrapper/collab-wrapper.component";
 import { FormlyRepeatDndComponent } from "./repeat-dnd/repeat-dnd.component";
+import { UiUdfParametersComponent } from "../../workspace/component/ui-udf-parameters/ui-udf-parameters.component";
 
 /**
  * Configuration for using Json Schema with Formly.
@@ -78,6 +79,7 @@ export const TEXERA_FORMLY_CONFIG = {
     { name: "codearea", component: CodeareaCustomTemplateComponent },
     { name: "inputautocomplete", component: InputAutoCompleteComponent, wrappers: ["form-field"] },
     { name: "repeat-section-dnd", component: FormlyRepeatDndComponent },
+    { name: "ui-udf-parameters", component: UiUdfParametersComponent, wrappers: ["form-field"] },
   ],
   wrappers: [
     { name: "preset-wrapper", component: PresetWrapperComponent },
diff --git a/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts b/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
index 18ebfc59a1f..74afaae7a52 100644
--- a/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
+++ b/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
@@ -47,11 +47,12 @@ import "@codingame/monaco-vscode-python-default-extension";
 import "@codingame/monaco-vscode-r-default-extension";
 import "@codingame/monaco-vscode-java-default-extension";
 import { isDefined } from "../../../common/util/predicate";
-import { filter, switchMap } from "rxjs/operators";
+import { debounceTime, filter, switchMap } from "rxjs/operators";
 import { BreakpointConditionInputComponent } from "./breakpoint-condition-input/breakpoint-condition-input.component";
 import { CodeDebuggerComponent } from "./code-debugger.component";
 import { MonacoEditor } from "monaco-breakpoints/dist/types";
 import { GuiConfigService } from "src/app/common/service/gui-config.service";
+import { UiUdfParametersSyncService } from "../../service/code-editor/ui-udf-parameters-sync.service";
 
 export const LANGUAGE_SERVER_CONNECTION_TIMEOUT_MS = 1000;
 
@@ -102,6 +103,7 @@ export class CodeEditorComponent implements AfterViewInit, SafeStyle, OnDestroy
   private isMultipleVariables: boolean = false;
   public codeDebuggerComponent!: Type<any> | null;
   public editorToPass!: MonacoEditor;
+  // private readonly pythonCodeChangeSubject = new Subject<string>();
 
   private generateLanguageTitle(language: string): string {
     return `${language.charAt(0).toUpperCase()}${language.slice(1)} UDF`;
@@ -118,7 +120,8 @@ export class CodeEditorComponent implements AfterViewInit, SafeStyle, OnDestroy
     private workflowVersionService: WorkflowVersionService,
     public coeditorPresenceService: CoeditorPresenceService,
     private aiAssistantService: AIAssistantService,
-    private config: GuiConfigService
+    private config: GuiConfigService,
+    private uiUdfParametersSyncService: UiUdfParametersSyncService
   ) {
     this.currentOperatorId = this.workflowActionService.getJointGraphWrapper().getCurrentHighlightedOperatorIDs()[0];
     const operatorType = this.workflowActionService.getTexeraGraph().getOperator(this.currentOperatorId).operatorType;
@@ -143,9 +146,11 @@ export class CodeEditorComponent implements AfterViewInit, SafeStyle, OnDestroy
         .get("operatorProperties") as YType<Readonly<{ [key: string]: any }>>
     ).get("code") as YText;
   }
-
+  private detachYCodeListener?: () => void;
   ngAfterViewInit() {
-    // hacky solution to reset view after view is rendered.
+    // this.pythonCodeChangeSubject
+    //    .pipe(debounceTime(250), untilDestroyed(this))
+    //    .subscribe(code => this.uiUdfParametersSyncService.syncStructureFromCode(this.currentOperatorId, code));    // hacky solution to reset view after view is rendered.
     const style = localStorage.getItem(this.currentOperatorId);
     if (style) this.containerElement.nativeElement.style.cssText = style;
 
@@ -176,6 +181,9 @@ export class CodeEditorComponent implements AfterViewInit, SafeStyle, OnDestroy
       this.workflowVersionStreamSubject.next();
       this.workflowVersionStreamSubject.complete();
     }
+    if (this.detachYCodeListener) {
+      this.detachYCodeListener();
+    }
   }
 
   /**
@@ -273,6 +281,23 @@ export class CodeEditorComponent implements AfterViewInit, SafeStyle, OnDestroy
         );
         this.setupAIAssistantActions(editor);
         this.initCodeDebuggerComponent(editor);
+        if (this.detachYCodeListener) {
+          this.detachYCodeListener();
+        }
+
+        if (this.code) {
+          this.detachYCodeListener =
+            this.uiUdfParametersSyncService.attachToYCode(
+              this.currentOperatorId,
+              this.code
+            );
+        }
+        // editor.onDidChangeModelContent(() => {
+        //   const latestCode = editor.getModel()?.getValue();
+        //   if (isDefined(latestCode)) {
+        //     this.pythonCodeChangeSubject.next(latestCode);
+        //   }
+        // });
       });
   }
 
diff --git a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.scss b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.scss
index 4126a9ee1ce..aa32d22b4aa 100644
--- a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.scss
+++ b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.scss
@@ -73,3 +73,17 @@
     margin-bottom: 0;
   }
 }
+
+/* ================================
+   Style ONLY the UDF Parameters field
+   ================================ */
+
+:host ::ng-deep label[for*="ui-udf-parameters"] {
+  font-weight: 700;
+}
+
+:host ::ng-deep nz-form-item:has(label[for*="ui-udf-parameters"]) {
+  border-top: 1.5px solid #d1d1d1;
+  padding-top: 12px;
+  margin-top: 8px;
+}
diff --git a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
index 5d457e9050e..56b0395fc6e 100644
--- a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
+++ b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
@@ -67,7 +67,7 @@ import * as Y from "yjs";
 import { OperatorSchema } from "src/app/workspace/types/operator-schema.interface";
 import { AttributeType, PortSchema } from "../../../types/workflow-compiling.interface";
 import { GuiConfigService } from "../../../../common/service/gui-config.service";
-
+import { UiUdfParametersSyncService } from "../../../service/code-editor/ui-udf-parameters-sync.service";
 Quill.register("modules/cursors", QuillCursors);
 
 /**
@@ -155,7 +155,8 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
     private changeDetectorRef: ChangeDetectorRef,
     private workflowVersionService: WorkflowVersionService,
     private workflowStatusSerivce: WorkflowStatusService,
-    private config: GuiConfigService
+    private config: GuiConfigService,
+    private uiUdfParametersSyncService: UiUdfParametersSyncService
   ) {}
 
   ngOnChanges(changes: SimpleChanges): void {
@@ -193,6 +194,54 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
           this.currentOperatorStatus = update[this.currentOperatorId];
         }
       });
+
+    this.uiUdfParametersSyncService.uiParametersChanged$
+      .pipe(untilDestroyed(this))
+      .subscribe(({ operatorId, parameters }) => {
+        if (operatorId !== this.currentOperatorId) return;
+
+        const readCurrentUiParams = () =>
+          this.workflowActionService
+            .getTexeraGraph()
+            .getOperator(operatorId)
+            .operatorProperties?.uiParameters ?? [];
+
+        // initial read
+        let currentUiParams = readCurrentUiParams();
+
+        // max attempts = abs(prevLen - newLen), with sane bounds
+        const diffLen = Math.abs((currentUiParams?.length ?? 0) - (parameters?.length ?? 0));
+        const MAX_ATTEMPTS = Math.min(Math.max(diffLen, 1), 20);
+
+        let attempts = 0;
+
+        while (!isEqual(currentUiParams, parameters) && attempts < MAX_ATTEMPTS) {
+          const currentOperator =
+            this.workflowActionService
+              .getTexeraGraph()
+              .getOperator(operatorId);
+
+          const newModel = {
+            ...cloneDeep(currentOperator.operatorProperties),
+            uiParameters: cloneDeep(parameters),
+          };
+
+          this.listeningToChange = false;
+          this.workflowActionService.setOperatorProperty(operatorId, newModel);
+          this.listeningToChange = true;
+
+          // re-read after mutation
+          currentUiParams = readCurrentUiParams();
+          attempts++;
+        }
+
+        if (!isEqual(currentUiParams, parameters)) {
+          console.warn(
+            `uiParameters did not converge after ${attempts}/${MAX_ATTEMPTS} attempts`,
+            { currentLen: currentUiParams.length, targetLen: parameters.length }
+          );
+        }
+      });
   }
 
   async ngOnDestroy() {
@@ -331,6 +380,7 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
         this.listeningToChange = false;
         this.typeInferenceOnLambdaFunction(formData);
         this.workflowActionService.setOperatorProperty(this.currentOperatorId, cloneDeep(formData));
+        //const normalizedFormData = this.uiUdfParametersSyncService.syncStructureFromCode(this.currentOperatorId, formData.code)
         this.listeningToChange = true;
       }
     });
@@ -453,6 +503,10 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
         mappedField.type = "inputautocomplete";
       }
 
+      if (mappedField.key === "uiParameters") {
+        mappedField.type = "ui-udf-parameters";
+      }
+
       // if the title is python script (for Python UDF), then make this field a custom template 'codearea'
       if (mapSource?.description?.toLowerCase() === "input your code here") {
         if (mappedField.type) {
diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
new file mode 100644
index 00000000000..16e859d79c8
--- /dev/null
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
@@ -0,0 +1,39 @@
+<div class="ui-udf-param-list" *ngIf="(model?.length ?? 0) > 0">
+
+  <!-- Optional header row -->
+  <div class="ui-udf-param-row header">
+    <div class="field-cell"><span class="col-title">Value</span></div>
+    <div class="field-cell"><span class="col-title">Name</span></div>
+    <div class="field-cell"><span class="col-title">Type</span></div>
+  </div>
+
+  <div
+    class="ui-udf-param-row"
+    *ngFor="let param of (model || []); let i = index; trackBy: trackByParamName"
+  >
+    <ng-container *ngIf="field.fieldGroup?.[i] as rowField">
+
+      <!-- Value -->
+      <div class="field-cell">
+        <ng-container *ngIf="getValueField(rowField) as valueField">
+          <formly-field [field]="valueField"></formly-field>
+        </ng-container>
+      </div>
+
+      <!-- Name -->
+      <div class="field-cell">
+        <ng-container *ngIf="getNameField(rowField) as nameField">
+          <formly-field [field]="nameField"></formly-field>
+        </ng-container>
+      </div>
+
+      <!-- Type -->
+      <div class="field-cell">
+        <ng-container *ngIf="getTypeField(rowField) as typeField">
+          <formly-field [field]="typeField"></formly-field>
+        </ng-container>
+      </div>
+
+    </ng-container>
+  </div>
+</div>
diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
new file mode 100644
index 00000000000..a56edd4753e
--- /dev/null
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
@@ -0,0 +1,20 @@
+.ui-udf-param-row {
+  display: grid;
+  grid-template-columns: 250px 250px 1fr;
+  gap: 12px;
+  align-items: start;
+}
+
+.field-cell {
+  min-width: 0;
+}
+
+/* Remove Formly/Ant label spacing */
+:host ::ng-deep .ant-form-item {
+  margin-bottom: 0;
+}
+
+/* Hide Formly labels since you already have a header row */
+:host ::ng-deep .ant-form-item-label {
+  display: none;
+}
diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
new file mode 100644
index 00000000000..2ca3ffb0a3a
--- /dev/null
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
@@ -0,0 +1,71 @@
+import { Component } from "@angular/core";
+import { FieldArrayType, FormlyFieldConfig } from "@ngx-formly/core";
+
+@Component({
+  selector: "texera-ui-udf-parameters",
+  templateUrl: "./ui-udf-parameters.component.html",
+  styleUrls: ["./ui-udf-parameters.component.scss"],
+})
+export class UiUdfParametersComponent extends FieldArrayType {
+  private getField(rowField: FormlyFieldConfig, key: string): FormlyFieldConfig | undefined {
+    return rowField.fieldGroup?.find(f => f.key === key);
+  }
+
+  private getAttributeChild(rowField: FormlyFieldConfig, childKey: string): FormlyFieldConfig | undefined {
+    const attributeGroup = this.getField(rowField, "attribute");
+    return attributeGroup?.fieldGroup?.find(f => f.key === childKey);
+  }
+
+  private setDisabled(field: FormlyFieldConfig | undefined, disabled: boolean): FormlyFieldConfig | undefined {
+    if (!field) return undefined;
+
+    // 1) Modern Formly
+    field.props = { ...(field.props ?? {}), disabled };
+
+    // 2) Compatibility for templates/wrappers still using templateOptions
+    // (use `as any` so you don't get nagged by the @deprecated JSDoc)
+    (field as any).templateOptions = { ...((field as any).templateOptions ?? {}), disabled };
+
+    // 3) Enforce at the reactive form level
+    if (field.formControl) {
+      disabled
+          ? field.formControl.disable({ emitEvent: false })
+          : field.formControl.enable({ emitEvent: false });
+    } else {
+      // If control isn't created yet, disable it at init time.
+      const prevOnInit = field.hooks?.onInit;
+      field.hooks = {
+        ...(field.hooks ?? {}),
+        onInit: f => {
+          prevOnInit?.(f);
+          if (disabled) {
+            f.formControl?.disable({ emitEvent: false });
+          } else {
+            f.formControl?.enable({ emitEvent: false });
+          }
+        },
+      };
+    }
+
+    return field;
+  }
+
+  // Disable Name
+  getNameField(rowField: FormlyFieldConfig): FormlyFieldConfig | undefined {
+    return this.setDisabled(this.getAttributeChild(rowField, "attributeName"), true);
+  }
+
+  // Disable Type (set to false if you want it editable)
+  getTypeField(rowField: FormlyFieldConfig): FormlyFieldConfig | undefined {
+    return this.setDisabled(this.getAttributeChild(rowField, "attributeType"), true);
+  }
+
+  // Value editable (set to true to disable)
+  getValueField(rowField: FormlyFieldConfig): FormlyFieldConfig | undefined {
+    return this.setDisabled(this.getField(rowField, "value"), false);
+  }
+
+  trackByParamName = (index: number, param: any): string | number => {
+    return param?.attribute?.attributeName ?? index;
+  };
+}
diff --git a/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-parser.service.spec.ts b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-parser.service.spec.ts
new file mode 100644
index 00000000000..d64c81ef463
--- /dev/null
+++ b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-parser.service.spec.ts
@@ -0,0 +1,79 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+import { UiUdfParametersParserService } from "./ui-udf-parameters-parser.service";
+
+describe("UiUdfParametersParserService", () => {
+  let service: UiUdfParametersParserService;
+
+  beforeEach(() => {
+    service = new UiUdfParametersParserService();
+  });
+
+  it("should parse positional and name-based arguments", () => {
+    const code = `
+      class ProcessTupleOperator(UDFOperatorV2):
+          def open(self):
+              self.UiParameter(AttributeType.INT, "count")
+              self.UiParameter(type=AttributeType.STRING, name="name")
+              self.UiParameter(name="age", type=AttributeType.LONG)
+              self.UiParameter(AttributeType.DOUBLE, name="score")
+              self.UiParameter("created_at", type=AttributeType.TIMESTAMP)
+    `;
+
+    expect(service.parse(code)).toEqual([
+      { attribute: { attributeName: "count", attributeType: "INT" }, value: "" },
+      { attribute: { attributeName: "name", attributeType: "STRING" }, value: "" },
+      { attribute: { attributeName: "age", attributeType: "LONG" }, value: "" },
+      { attribute: { attributeName: "score", attributeType: "DOUBLE" }, value: "" },
+      { attribute: { attributeName: "created_at", attributeType: "TIMESTAMP" }, value: "" },
+    ]);
+  });
+
+  it("should ignore calls where name or type is missing", () => {
+    const code = `
+      class ProcessTupleOperator(UDFOperatorV2):
+          def open(self):
+              self.UiParameter(name="a")
+              self.UiParameter(type=AttributeType.DOUBLE)
+    `;
+
+    expect(service.parse(code)).toEqual([]);
+  });
+
+  it("should ignore legacy key= named argument", () => {
+    const code = `
+      class ProcessTupleOperator(UDFOperatorV2):
+          def open(self):
+              self.UiParameter(type=AttributeType.DOUBLE, key="a")
+    `;
+
+    expect(service.parse(code)).toEqual([]);
+  });
+
+  it("should ignore unsupported classes", () => {
+    const code = `
+      class RandomClass(ABC):
+          def open(self):
+              self.UiParameter(type=AttributeType.DOUBLE, name="a")
+    `;
+
+    expect(service.parse(code)).toEqual([]);
+  });
+});
diff --git a/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-parser.service.ts b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-parser.service.ts
new file mode 100644
index 00000000000..bf080bb5fee
--- /dev/null
+++ b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-parser.service.ts
@@ -0,0 +1,308 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+import { Injectable } from "@angular/core";
+import {
+  AttributeType,
+  JavaAttributeTypeName,
+  PythonAttributeTypeName,
+  SchemaAttribute,
+  JAVA_ATTRIBUTE_TYPE_NAMES,
+  PYTHON_ATTRIBUTE_TYPE_NAMES,
+} from "../../types/workflow-compiling.interface";
+
+export interface UiUdfParameter {
+  attribute: SchemaAttribute;
+  value: string;
+}
+
+type ParserAttributeTypeToken = JavaAttributeTypeName | PythonAttributeTypeName;
+
+const ATTRIBUTE_TYPE_TOKEN_TO_CANONICAL: Readonly<Record<ParserAttributeTypeToken, AttributeType>> = {
+  STRING: "string",
+  INTEGER: "integer",
+  INT: "integer",
+  LONG: "long",
+  DOUBLE: "double",
+  BOOLEAN: "boolean",
+  BOOL: "boolean",
+  TIMESTAMP: "timestamp",
+  BINARY: "binary",
+  LARGE_BINARY: "large_binary",
+};
+
+const JAVA_ATTRIBUTE_TYPE_NAME_SET = new Set<string>(JAVA_ATTRIBUTE_TYPE_NAMES);
+const PYTHON_ATTRIBUTE_TYPE_NAME_SET = new Set<string>(PYTHON_ATTRIBUTE_TYPE_NAMES);
+
+@Injectable({ providedIn: "root" })
+export class UiUdfParametersParserService {
+  private static readonly SUPPORTED_CLASSES = [
+    "ProcessTupleOperator",
+    "ProcessBatchOperator",
+    "ProcessTableOperator",
+    "GenerateOperator",
+  ];
+
+  parse(code: string): UiUdfParameter[] {
+    if (!code) {
+      return [];
+    }
+
+    const classPattern = UiUdfParametersParserService.SUPPORTED_CLASSES.join("|");
+    const classRegex = new RegExp(
+      `class\\s+(${classPattern})\\s*\\([^)]*\\)\\s*:[\\s\\S]*?(?=\\nclass\\s+\\w+\\s*\\(|$)`,
+      "g"
+    );
+
+    const parsed: UiUdfParameter[] = [];
+    const existingNames = new Set<string>();
+
+    let classMatch: RegExpExecArray | null;
+    while ((classMatch = classRegex.exec(code)) !== null) {
+      const classBlock = classMatch[0];
+
+      for (const args of this.extractUiParameterArgumentLists(classBlock)) {
+        const argumentTokens = this.tokenizeArguments(args);
+        const extracted = this.extractParameter(argumentTokens);
+        if (!extracted || existingNames.has(extracted.attribute.attributeName)) {
+          continue;
+        }
+
+        existingNames.add(extracted.attribute.attributeName);
+        parsed.push(extracted);
+      }
+    }
+
+    return parsed;
+  }
+
+  /**
+   * Extract argument strings from self.UiParameter(...)
+   * More robust than regex when there are nested parentheses.
+   */
+  private extractUiParameterArgumentLists(code: string): string[] {
+    const result: string[] = [];
+    const needle = "self.UiParameter(";
+    let index = 0;
+
+    while (index < code.length) {
+      const start = code.indexOf(needle, index);
+      if (start === -1) {
+        break;
+      }
+
+      const openParenIndex = start + needle.length - 1;
+      const closeParenIndex = this.findMatchingParen(code, openParenIndex);
+      if (closeParenIndex === -1) {
+        break;
+      }
+
+      result.push(code.slice(openParenIndex + 1, closeParenIndex));
+      index = closeParenIndex + 1;
+    }
+
+    return result;
+  }
+
+  /**
+   * Find matching ')' for a '(' while ignoring quoted strings.
+   */
+  private findMatchingParen(text: string, openIndex: number): number {
+    let depth = 0;
+    let inSingle = false;
+    let inDouble = false;
+    let escaped = false;
+
+    for (let i = openIndex; i < text.length; i++) {
+      const ch = text[i];
+
+      if (escaped) {
+        escaped = false;
+        continue;
+      }
+
+      if ((inSingle || inDouble) && ch === "\\") {
+        escaped = true;
+        continue;
+      }
+
+      if (!inDouble && ch === "'") {
+        inSingle = !inSingle;
+        continue;
+      }
+
+      if (!inSingle && ch === "\"") {
+        inDouble = !inDouble;
+        continue;
+      }
+
+      if (inSingle || inDouble) {
+        continue;
+      }
+
+      if (ch === "(") {
+        depth++;
+      } else if (ch === ")") {
+        depth--;
+        if (depth === 0) {
+          return i;
+        }
+      }
+    }
+
+    return -1;
+  }
+
+  /**
+   * Split on top-level commas only (ignores commas inside strings / nested calls).
+   */
+  private tokenizeArguments(argumentList: string): string[] {
+    const tokens: string[] = [];
+    let current = "";
+    let depth = 0;
+    let inSingle = false;
+    let inDouble = false;
+    let escaped = false;
+
+    for (let i = 0; i < argumentList.length; i++) {
+      const ch = argumentList[i];
+
+      if (escaped) {
+        current += ch;
+        escaped = false;
+        continue;
+      }
+
+      if ((inSingle || inDouble) && ch === "\\") {
+        current += ch;
+        escaped = true;
+        continue;
+      }
+
+      if (!inDouble && ch === "'") {
+        inSingle = !inSingle;
+        current += ch;
+        continue;
+      }
+
+      if (!inSingle && ch === "\"") {
+        inDouble = !inDouble;
+        current += ch;
+        continue;
+      }
+
+      if (!inSingle && !inDouble) {
+        if (ch === "(") {
+          depth++;
+          current += ch;
+          continue;
+        }
+
+        if (ch === ")") {
+          depth = Math.max(0, depth - 1);
+          current += ch;
+          continue;
+        }
+
+        if (ch === "," && depth === 0) {
+          const token = current.trim();
+          if (token.length > 0) {
+            tokens.push(token);
+          }
+          current = "";
+          continue;
+        }
+      }
+
+      current += ch;
+    }
+
+    const tail = current.trim();
+    if (tail.length > 0) {
+      tokens.push(tail);
+    }
+
+    return tokens;
+  }
+
+  private extractParameter(tokens: string[]): UiUdfParameter | undefined {
+    let namedName: string | undefined;
+    let namedType: AttributeType | undefined;
+    let positionalName: string | undefined;
+    let positionalType: AttributeType | undefined;
+
+    for (const token of tokens) {
+      const namedNameMatch = token.match(/^name\s*=\s*["']([^"']+)["']$/);
+      if (namedNameMatch) {
+        namedName = namedNameMatch[1].trim();
+        continue;
+      }
+
+      const namedTypeMatch = token.match(/^type\s*=\s*AttributeType\.([A-Za-z_][A-Za-z0-9_]*)$/);
+      if (namedTypeMatch) {
+        namedType = this.normalizeAttributeType(namedTypeMatch[1]);
+        continue;
+      }
+
+      const positionalTypeMatch = token.match(/^AttributeType\.([A-Za-z_][A-Za-z0-9_]*)$/);
+      if (positionalTypeMatch && !positionalType) {
+        positionalType = this.normalizeAttributeType(positionalTypeMatch[1]);
+        continue;
+      }
+
+      const positionalNameMatch = token.match(/^["']([^"']+)["']$/);
+      if (positionalNameMatch && !positionalName) {
+        positionalName = positionalNameMatch[1].trim();
+      }
+    }
+
+    const attributeName = namedName ?? positionalName;
+    const attributeType = namedType ?? positionalType;
+
+    if (!attributeName || !attributeType) {
+      return undefined;
+    }
+
+    return {
+      attribute: {
+        attributeName,
+        attributeType,
+      },
+      value: "",
+    };
+  }
+
+  /**
+   * Convert Java/Python enum tokens into canonical schema names.
+   * Examples:
+   *   STRING -> string
+   *   INTEGER -> integer
+   *   INT -> integer
+   *   BOOL -> boolean
+   */
+  private normalizeAttributeType(token: string): AttributeType | undefined {
+    const normalized = token.trim().toUpperCase();
+
+    if (!JAVA_ATTRIBUTE_TYPE_NAME_SET.has(normalized) && !PYTHON_ATTRIBUTE_TYPE_NAME_SET.has(normalized)) {
+      return undefined;
+    }
+
+    return ATTRIBUTE_TYPE_TOKEN_TO_CANONICAL[normalized as ParserAttributeTypeToken];
+  }
+}
diff --git a/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts
new file mode 100644
index 00000000000..cae70645aac
--- /dev/null
+++ b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts
@@ -0,0 +1,130 @@
+import { Injectable } from "@angular/core";
+import { isEqual } from "lodash-es";
+import { ReplaySubject } from "rxjs";
+import { Subject } from "rxjs";
+import { WorkflowActionService } from "../workflow-graph/model/workflow-action.service";
+import { UiUdfParameter, UiUdfParametersParserService } from "./ui-udf-parameters-parser.service";
+import { isDefined } from "../../../common/util/predicate";
+import {
+  DUAL_INPUT_PORTS_PYTHON_UDF_V2_OP_TYPE,
+  PYTHON_UDF_SOURCE_V2_OP_TYPE,
+  PYTHON_UDF_V2_OP_TYPE,
+} from "../workflow-graph/model/workflow-graph";
+import { YType } from "../../types/shared-editing.interface";
+import { YText } from "yjs/dist/src/types/YText";
+
+@Injectable({ providedIn: "root" })
+export class UiUdfParametersSyncService {
+
+  private readonly uiParametersChangedSubject =
+    new ReplaySubject<{ operatorId: string; parameters: UiUdfParameter[] }>(1);
+
+
+  readonly uiParametersChanged$ =
+    this.uiParametersChangedSubject.asObservable();
+
+  constructor(
+    private workflowActionService: WorkflowActionService,
+    private uiUdfParametersParserService: UiUdfParametersParserService
+  ) {}
+
+  /**
+   * Attach directly to YText and sync whenever it changes
+   */
+  attachToYCode(operatorId: string, yCode: YText): () => void {
+    const handler = () => {
+      const latestCode = yCode.toString();
+      this.syncStructureFromCode(operatorId, latestCode);
+    };
+
+    yCode.observe(handler);
+
+    handler();
+
+    // return cleanup function
+    return () => yCode.unobserve(handler);
+  }
+
+  syncStructureFromCode(operatorId: string, codeFromEditor?: string): void {
+    const operator = this.workflowActionService
+      .getTexeraGraph()
+      .getOperator(operatorId);
+
+    if (!operator || !this.isSupportedPythonUdfType(operator.operatorType)) {
+      return;
+    }
+
+    const code = codeFromEditor ?? this.getSharedCode(operatorId);
+    if (!isDefined(code)) {
+      return;
+    }
+
+    const existingParameters = operator.operatorProperties?.uiParameters ?? [];
+    const mergedUiParameters =
+      this.buildParsedShapeWithPreservedValues(code, existingParameters);
+
+    if (isEqual(existingParameters, mergedUiParameters)) {
+      return;
+    }
+
+    // Emit event so UI updates
+    this.uiParametersChangedSubject.next({
+      operatorId,
+      parameters: mergedUiParameters,
+    });
+
+    // optionally persist here if desired
+    // this.workflowActionService.setOperatorProperty(...)
+  }
+
+  private buildParsedShapeWithPreservedValues(
+    code: string,
+    existingParameters: any[]
+  ): UiUdfParameter[] {
+    const parsedParameters =
+      this.uiUdfParametersParserService.parse(code);
+
+    const existingValues = new Map<string, string>();
+    existingParameters.forEach((parameter: any) => {
+      const parameterName =
+        parameter?.attribute?.attributeName ??
+        parameter?.attribute?.name;
+
+      if (isDefined(parameterName) && isDefined(parameter?.value)) {
+        existingValues.set(parameterName, parameter.value);
+      }
+    });
+
+    return parsedParameters.map(parameter => ({
+      ...parameter,
+      value:
+        existingValues.get(parameter.attribute.attributeName) ?? "",
+    }));
+  }
+
+  private getSharedCode(operatorId: string): string | undefined {
+    try {
+      const sharedOperatorType =
+        this.workflowActionService
+          .getTexeraGraph()
+          .getSharedOperatorType(operatorId);
+
+      const operatorProperties =
+        sharedOperatorType.get("operatorProperties") as
+          YType<Readonly<{ [key: string]: any }>>;
+
+      const yCode = operatorProperties.get("code") as YText;
+      return yCode?.toString();
+    } catch {
+      return undefined;
+    }
+  }
+
+  private isSupportedPythonUdfType(operatorType: string): boolean {
+    return [
+      PYTHON_UDF_V2_OP_TYPE,
+      PYTHON_UDF_SOURCE_V2_OP_TYPE,
+      DUAL_INPUT_PORTS_PYTHON_UDF_V2_OP_TYPE,
+    ].includes(operatorType);
+  }
+}
diff --git a/frontend/src/app/workspace/types/workflow-compiling.interface.ts b/frontend/src/app/workspace/types/workflow-compiling.interface.ts
index 8c4499d3d4c..1849552c0ab 100644
--- a/frontend/src/app/workspace/types/workflow-compiling.interface.ts
+++ b/frontend/src/app/workspace/types/workflow-compiling.interface.ts
@@ -70,8 +70,24 @@ export type CompilationStateInfo = Readonly<
       operatorErrors: Readonly<Record<string, WorkflowFatalError>>;
     }
 >;
+
 // possible types of an attribute
-export type AttributeType = "string" | "integer" | "double" | "boolean" | "long" | "timestamp" | "binary"; // schema: an array of attribute names and types
+// Canonical frontend / JSON schema names
+export type AttributeType = "string" | "integer" | "long" | "double" | "boolean" | "timestamp" | "binary" | "large_binary";
+
+// Java enum constant names (AttributeType.java)
+export const JAVA_ATTRIBUTE_TYPE_NAMES = ["STRING", "INTEGER", "LONG", "DOUBLE", "BOOLEAN", "TIMESTAMP", "BINARY", "LARGE_BINARY",] as const;
+
+export type JavaAttributeTypeName = (typeof JAVA_ATTRIBUTE_TYPE_NAMES)[number];
+
+// Python enum constant names (core.models.AttributeType)
+export const PYTHON_ATTRIBUTE_TYPE_NAMES = ["STRING", "INT", "LONG", "DOUBLE", "BOOL", "TIMESTAMP", "BINARY", "LARGE_BINARY",] as const;
+
+export type PythonAttributeTypeName = (typeof PYTHON_ATTRIBUTE_TYPE_NAMES)[number];
+
+// Useful when parsing code from either side
+export type AttributeTypeToken = AttributeType | JavaAttributeTypeName | PythonAttributeTypeName;
+
 export interface SchemaAttribute
   extends Readonly<{
     attributeName: string;

From 5b29ad86bbf5759b4195ee7c81820b2c18480d2e Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Fri, 27 Feb 2026 18:02:31 -0600
Subject: [PATCH 02/12] v2

---
 amber/src/main/python/pytexera/udf/udf_operator.py       | 6 ------
 .../org/apache/texera/amber/core/tuple/Attribute.java    | 6 ++++--
 .../code-editor-dialog/code-editor.component.ts          | 9 ---------
 .../operator-property-edit-frame.component.ts            | 1 -
 .../ui-udf-parameters/ui-udf-parameters.component.scss   | 2 +-
 5 files changed, 5 insertions(+), 19 deletions(-)

diff --git a/amber/src/main/python/pytexera/udf/udf_operator.py b/amber/src/main/python/pytexera/udf/udf_operator.py
index 59af65f3b86..3c8e2d22291 100644
--- a/amber/src/main/python/pytexera/udf/udf_operator.py
+++ b/amber/src/main/python/pytexera/udf/udf_operator.py
@@ -81,12 +81,6 @@ def __init__(self, name: str, type: AttributeType):
             self.type = type
             self.value = _UiParameterSupport._parse(raw_value, type)
 
-    @classmethod
-    def set_injected_ui_parameters(cls, values: Dict[str, Any]) -> None:
-        # keep for backward compatibility if anything else calls it
-        _UiParameterSupport._ui_parameter_injected_values = dict(values or {})
-        _UiParameterSupport._ui_parameter_name_types = {}
-
     @staticmethod
     def _parse(value: Any, attr_type: AttributeType) -> Any:
         if value is None:
diff --git a/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java b/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
index 40373dcbaca..730ab0fb8ec 100644
--- a/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
+++ b/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
@@ -23,6 +23,8 @@
 import com.fasterxml.jackson.annotation.JsonProperty;
 import org.apache.texera.amber.pybuilder.EncodableStringAnnotation;
 import org.apache.texera.amber.pybuilder.PyStringTypes;
+import org.apache.texera.amber.pybuilder.PyStringTypes.EncodableStringFactory$;
+
 
 import javax.validation.constraints.NotBlank;
 import javax.validation.constraints.NotNull;
@@ -35,7 +37,7 @@
  */
 public class Attribute implements Serializable {
 
-    private final @EncodableStringAnnotation String attributeName;
+    private final String attributeName;
     private final AttributeType attributeType;
 
     @JsonCreator
@@ -52,7 +54,7 @@ public Attribute(
     @JsonProperty(value = "attributeName", required = true)
     @NotBlank(message = "Attribute name is required")
     public @EncodableStringAnnotation String getName() {
-        return attributeName;
+        return PyStringTypes.EncodableStringFactory$.MODULE$.apply(attributeName);
     }
 
     @JsonProperty(value = "attributeType", required = true)
diff --git a/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts b/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
index 74afaae7a52..6f999a770f1 100644
--- a/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
+++ b/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
@@ -148,9 +148,6 @@ export class CodeEditorComponent implements AfterViewInit, SafeStyle, OnDestroy
   }
   private detachYCodeListener?: () => void;
   ngAfterViewInit() {
-    // this.pythonCodeChangeSubject
-    //    .pipe(debounceTime(250), untilDestroyed(this))
-    //    .subscribe(code => this.uiUdfParametersSyncService.syncStructureFromCode(this.currentOperatorId, code));    // hacky solution to reset view after view is rendered.
     const style = localStorage.getItem(this.currentOperatorId);
     if (style) this.containerElement.nativeElement.style.cssText = style;
 
@@ -292,12 +289,6 @@ export class CodeEditorComponent implements AfterViewInit, SafeStyle, OnDestroy
               this.code
             );
         }
-        // editor.onDidChangeModelContent(() => {
-        //   const latestCode = editor.getModel()?.getValue();
-        //   if (isDefined(latestCode)) {
-        //     this.pythonCodeChangeSubject.next(latestCode);
-        //   }
-        // });
       });
   }
 
diff --git a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
index 56b0395fc6e..6e71b076e22 100644
--- a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
+++ b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
@@ -380,7 +380,6 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
         this.listeningToChange = false;
         this.typeInferenceOnLambdaFunction(formData);
         this.workflowActionService.setOperatorProperty(this.currentOperatorId, cloneDeep(formData));
-        //const normalizedFormData = this.uiUdfParametersSyncService.syncStructureFromCode(this.currentOperatorId, formData.code)
         this.listeningToChange = true;
       }
     });
diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
index a56edd4753e..5b7e498731e 100644
--- a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
@@ -14,7 +14,7 @@
   margin-bottom: 0;
 }
 
-/* Hide Formly labels since you already have a header row */
+/* Hide Formly labels*/
 :host ::ng-deep .ant-form-item-label {
   display: none;
 }

From 3550ccd48c7e0ad38090ab4ae98bc5a9486a1634 Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Mon, 2 Mar 2026 14:45:44 -0600
Subject: [PATCH 03/12] v2

---
 .../core/models/schema/attribute_type.py      |  12 +-
 .../main/python/pytexera/udf/udf_operator.py  |   8 +-
 .../web/service/ExecutionResultService.scala  |   3 +-
 .../amber/pybuilder/EncodableInspector.scala  |  16 +-
 .../pybuilder/EncodableStringAnnotation.java  |   3 +-
 .../texera/amber/core/tuple/Attribute.java    |   5 +-
 .../DualInputPortsPythonUDFOpDescV2.scala     |   1 +
 .../python/PythonLambdaFunctionOpDesc.scala   |   1 +
 .../udf/python/PythonTableReducerOpDesc.scala |   1 +
 .../udf/python/PythonUDFOpDescV2.scala        |   1 +
 .../python/PythonUdfUiParameterInjector.scala |  16 +-
 .../source/PythonUDFSourceOpDescV2.scala      |  22 ++-
 .../PythonUdfUiParameterInjectorSpec.scala    | 165 +++++++++++++++---
 .../collab-wrapper.component.css              |   2 +-
 .../code-editor.component.ts                  |   6 +-
 .../coeditor-user-icon.component.css          |   1 -
 .../operator-property-edit-frame.component.ts |  18 +-
 .../ui-udf-parameters.component.html          |  10 +-
 .../ui-udf-parameters.component.ts            |   8 +-
 .../ui-udf-parameters-sync.service.ts         |  51 ++----
 .../types/workflow-compiling.interface.ts     |  32 +++-
 21 files changed, 258 insertions(+), 124 deletions(-)

diff --git a/amber/src/main/python/core/models/schema/attribute_type.py b/amber/src/main/python/core/models/schema/attribute_type.py
index 24d0745f41e..0e56ec10fb6 100644
--- a/amber/src/main/python/core/models/schema/attribute_type.py
+++ b/amber/src/main/python/core/models/schema/attribute_type.py
@@ -22,7 +22,6 @@
 from pyarrow import lib
 from core.models.type.large_binary import largebinary
 
-
 class AttributeType(Enum):
     """
     Types supported by PyTexera & PyAmber.
@@ -78,6 +77,17 @@ class AttributeType(Enum):
 }
 
 
+FROM_STRING_PARSER_MAPPING = {
+    AttributeType.STRING: str,
+    AttributeType.INT: int,
+    AttributeType.LONG: int,
+    AttributeType.DOUBLE: float,
+    AttributeType.BOOL: lambda v: str(v).strip().lower() in ("true", "1", "yes"),
+    AttributeType.BINARY: lambda v: v if isinstance(v, bytes) else str(v).encode(),
+    AttributeType.TIMESTAMP: lambda v: datetime.datetime.fromisoformat(v),
+    AttributeType.LARGE_BINARY: largebinary,
+}
+
 # Only single-directional mapping.
 TO_PYOBJECT_MAPPING = {
     AttributeType.STRING: str,
diff --git a/amber/src/main/python/pytexera/udf/udf_operator.py b/amber/src/main/python/pytexera/udf/udf_operator.py
index 3c8e2d22291..25c7f0695e6 100644
--- a/amber/src/main/python/pytexera/udf/udf_operator.py
+++ b/amber/src/main/python/pytexera/udf/udf_operator.py
@@ -15,17 +15,13 @@
 # specific language governing permissions and limitations
 # under the License.
 
-import datetime
 from abc import abstractmethod
 from typing import Any, Dict, Iterator, Optional, Union
 
 import functools
-import datetime
-from abc import abstractmethod
-from typing import Any, Dict, Iterator, Optional, Union
 
 from pyamber import *
-from core.models.schema.attribute_type import AttributeType, TO_PYOBJECT_MAPPING
+from core.models.schema.attribute_type import AttributeType, FROM_STRING_PARSER_MAPPING
 
 class _UiParameterSupport:
     _ui_parameter_injected_values: Dict[str, Any] = {}
@@ -86,7 +82,7 @@ def _parse(value: Any, attr_type: AttributeType) -> Any:
         if value is None:
             return None
 
-        py_type = TO_PYOBJECT_MAPPING.get(attr_type)
+        py_type = FROM_STRING_PARSER_MAPPING.get(attr_type)
         return py_type(value)
 
 class UDFOperatorV2(_UiParameterSupport, TupleOperatorV2):
diff --git a/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala b/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
index 285c836b607..3f0362f8242 100644
--- a/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
+++ b/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
@@ -23,8 +23,7 @@ import org.apache.pekko.actor.Cancellable
 import com.fasterxml.jackson.annotation.{JsonTypeInfo, JsonTypeName}
 import com.fasterxml.jackson.databind.node.ObjectNode
 import com.typesafe.scalalogging.LazyLogging
-import org.apache.texera.amber.config.{ApplicationConfig, StorageConfig}
-import org.apache.texera.amber.core.storage.DocumentFactory.ICEBERG
+import org.apache.texera.amber.config.ApplicationConfig
 import org.apache.texera.amber.core.storage.model.VirtualDocument
 import org.apache.texera.amber.core.storage.result._
 import org.apache.texera.amber.core.storage.{DocumentFactory, VFSURIFactory}
diff --git a/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableInspector.scala b/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableInspector.scala
index 58bdcb649ec..9362016c72f 100644
--- a/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableInspector.scala
+++ b/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableInspector.scala
@@ -46,7 +46,7 @@ final class EncodableInspector[C <: blackbox.Context](val c: C) {
 
   // Keep this as a string so it also works if the annotation is referenced indirectly.
   private val encodableStringAnnotationFqn =
-    "org.apache.texera.amber.EncodableStringAnn"
+    "org.apache.texera.amber.pybuilder.EncodableStringAnnotation"
 
   /**
    * If we are pointing at a getter/accessor, hop to its accessed field symbol when possible.
@@ -110,10 +110,20 @@ final class EncodableInspector[C <: blackbox.Context](val c: C) {
     val symHasAnn =
       rawSym != null && rawSym != NoSymbol && {
         val accessed = safeAccessed(rawSym)
-        accessed != null && accessed != NoSymbol && accessed.annotations.exists(annIsEncodableString)
+        accessed != null && accessed != NoSymbol &&
+          accessed.annotations.exists(annIsEncodableString)
       }
 
-    symHasAnn || (tree.tpe != null && typeHasEncodableString(tree.tpe))
+    val methodReturnHasAnn =
+      rawSym != null && rawSym != NoSymbol && (rawSym match {
+        case m: MethodSymbol =>
+          typeHasEncodableString(m.typeSignature.finalResultType)
+        case _ =>
+          false
+      })
+
+    symHasAnn || methodReturnHasAnn ||
+      (tree.tpe != null && typeHasEncodableString(tree.tpe))
   }
 
   def isPythonTemplateBuilderArg(argExpr: c.Expr[Any]): Boolean = {
diff --git a/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableStringAnnotation.java b/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableStringAnnotation.java
index ea17e6d0130..f94c01f680a 100644
--- a/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableStringAnnotation.java
+++ b/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableStringAnnotation.java
@@ -29,6 +29,7 @@
     ElementType.FIELD,
     ElementType.PARAMETER,
     ElementType.TYPE_USE,
-    ElementType.LOCAL_VARIABLE
+    ElementType.LOCAL_VARIABLE,
+    ElementType.METHOD
 })
 public @interface EncodableStringAnnotation {}
\ No newline at end of file
diff --git a/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java b/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
index 730ab0fb8ec..dc812a55991 100644
--- a/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
+++ b/common/workflow-core/src/main/scala/org/apache/texera/amber/core/tuple/Attribute.java
@@ -53,8 +53,9 @@ public Attribute(
 
     @JsonProperty(value = "attributeName", required = true)
     @NotBlank(message = "Attribute name is required")
-    public @EncodableStringAnnotation String getName() {
-        return PyStringTypes.EncodableStringFactory$.MODULE$.apply(attributeName);
+    @EncodableStringAnnotation
+    public String getName() {
+        return attributeName;
     }
 
     @JsonProperty(value = "attributeType", required = true)
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
index a7a02560901..975ff0d7edd 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
@@ -36,6 +36,7 @@ class DualInputPortsPythonUDFOpDescV2 extends LogicalOp {
       "# Choose from the following templates:\n" +
         "# \n" +
         "# from pytexera import *\n" +
+        "# from core.models.schema.attribute_type import *\n" +
         "# \n" +
         "# class ProcessTupleOperator(UDFOperatorV2):\n" +
         "#     \n" +
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala
index 9b66dde0759..c18ed1ee8be 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala
@@ -78,6 +78,7 @@ class PythonLambdaFunctionOpDesc extends PythonOperatorDescriptor {
     // build the python udf code
     var code: String =
       "from pytexera import *\n" +
+        "from core.models.schema.attribute_type import *\n" +
         "class ProcessTupleOperator(UDFOperatorV2):\n" +
         "    @overrides\n" +
         "    def process_tuple(self, tuple_: Tuple, port: int) -> Iterator[Optional[TupleLike]]:\n"
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonTableReducerOpDesc.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonTableReducerOpDesc.scala
index 0c2bb07ff13..2221c6a1fa2 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonTableReducerOpDesc.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonTableReducerOpDesc.scala
@@ -57,6 +57,7 @@ class PythonTableReducerOpDesc extends PythonOperatorDescriptor {
 
     s"""
        |from pytexera import *
+       |from core.models.schema.attribute_type import *"+
        |class ProcessTableOperator(UDFTableOperator):
        |
        |    @overrides
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
index efac09d63b4..dcac72cc900 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
@@ -38,6 +38,7 @@ class PythonUDFOpDescV2 extends LogicalOp {
       "# Choose from the following templates:\n" +
         "# \n" +
         "# from pytexera import *\n" +
+        "# from core.models.schema.attribute_type import *\n" +
         "# \n" +
         "# class ProcessTupleOperator(UDFOperatorV2):\n" +
         "#     \n" +
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
index e6e181aad29..76bf11048e4 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
@@ -1,6 +1,5 @@
 package org.apache.texera.amber.operator.udf.python
 
-import org.apache.texera.amber.pybuilder.PyStringTypes.{EncodableString, EncodableStringFactory}
 import org.apache.texera.amber.pybuilder.PythonTemplateBuilder
 import org.apache.texera.amber.pybuilder.PythonTemplateBuilder.PythonTemplateBuilderStringContext
 
@@ -33,11 +32,11 @@ object PythonUdfUiParameterInjector {
     }
   }
 
-  private def buildInjectedParametersMap(uiParameters: List[UiUDFParameter]): PythonTemplateBuilder = {
+  private def buildInjectedParametersMap(
+      uiParameters: List[UiUDFParameter]
+  ): PythonTemplateBuilder = {
     val entries = uiParameters.map { parameter =>
-      val key: EncodableString = EncodableStringFactory(parameter.attribute.getName)
-      val value: EncodableString = EncodableStringFactory(parameter.value)
-      pyb"$key: $value"
+      pyb"${parameter.attribute.getName()}: ${parameter.value}"
     }
 
     entries.reduceOption((acc, entry) => acc + pyb", " + entry).getOrElse(pyb"")
@@ -123,7 +122,10 @@ object PythonUdfUiParameterInjector {
     }
 
     val bodyIndent = inferClassBodyIndent(classBlock, classIndent).getOrElse(classIndent + "    ")
-    val indentedHook = indentBlock((if (classBlock.endsWith("\n")) "" else "\n") + hookMethod.trim + "\n", bodyIndent)
+    val indentedHook = indentBlock(
+      (if (classBlock.endsWith("\n")) "" else "\n") + hookMethod.trim + "\n",
+      bodyIndent
+    )
 
     encodedUserCode.substring(0, classBlockEnd) +
       indentedHook +
@@ -156,4 +158,4 @@ object PythonUdfUiParameterInjector {
     // Inject hook into the UDF class body; Python base class will auto-call it before open()
     injectHookIntoUserClass(encodedUserCode, hookMethod)
   }
-}
\ No newline at end of file
+}
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
index a845f17ad3a..3a9eddd465e 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
@@ -33,13 +33,14 @@ class PythonUDFSourceOpDescV2 extends SourceOperatorDescriptor {
 
   @JsonProperty(
     required = true,
-    defaultValue = "# from pytexera import *\n" +
-      "# class GenerateOperator(UDFSourceOperator):\n" +
-      "# \n" +
-      "#     @overrides\n" +
-      "#     \n" +
-      "#     def produce(self) -> Iterator[Union[TupleLike, TableLike, None]]:\n" +
-      "#         yield\n"
+    defaultValue =
+      "# from pytexera import *\n" + "# from core.models.schema.attribute_type import *\n" +
+        "# class GenerateOperator(UDFSourceOperator):\n" +
+        "# \n" +
+        "#     @overrides\n" +
+        "#     \n" +
+        "#     def produce(self) -> Iterator[Union[TupleLike, TableLike, None]]:\n" +
+        "#         yield\n"
   )
   @JsonSchemaTitle("Python script")
   @JsonPropertyDescription("Input your code here")
@@ -66,7 +67,12 @@ class PythonUDFSourceOpDescV2 extends SourceOperatorDescriptor {
   ): PhysicalOp = {
     require(workers >= 1, "Need at least 1 worker.")
     val physicalOp = PhysicalOp
-      .sourcePhysicalOp(workflowId, executionId, operatorIdentifier, OpExecWithCode(PythonUdfUiParameterInjector.inject(code, uiParameters), "python"))
+      .sourcePhysicalOp(
+        workflowId,
+        executionId,
+        operatorIdentifier,
+        OpExecWithCode(PythonUdfUiParameterInjector.inject(code, uiParameters), "python")
+      )
       .withInputPorts(operatorInfo.inputPorts)
       .withOutputPorts(operatorInfo.outputPorts)
       .withIsOneToManyOp(true)
diff --git a/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
index ba8c2335c44..e4946c1b0f6 100644
--- a/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
+++ b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
@@ -1,22 +1,3 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
 package org.apache.texera.amber.operator.udf.python
 
 import org.apache.texera.amber.core.tuple.{Attribute, AttributeType}
@@ -25,32 +6,158 @@ import org.scalatest.matchers.should.Matchers
 
 class PythonUdfUiParameterInjectorSpec extends AnyFlatSpec with Matchers {
 
-  private def createParameter(key: String, attributeType: AttributeType, value: String): UiUDFParameter = {
+  private def createParameter(
+      key: String,
+      attributeType: AttributeType,
+      value: String
+  ): UiUDFParameter = {
     val parameter = new UiUDFParameter
     parameter.attribute = new Attribute(key, attributeType)
     parameter.value = value
     parameter
   }
 
-  it should "inject ui parameter prelude through PythonTemplateBuilder" in {
+  private val baseUdfCode: String =
+    """from pytexera import *
+      |
+      |class ProcessTupleOperator(UDFOperatorV2):
+      |    @overrides
+      |    def open(self):
+      |        print("open")
+      |
+      |    @overrides
+      |    def process_tuple(self, tuple_: Tuple, port: int):
+      |        yield tuple_
+      |""".stripMargin
+
+  it should "return encoded user code unchanged when there are no ui parameters" in {
+    val injectedCode = PythonUdfUiParameterInjector.inject(baseUdfCode, Nil)
+
+    injectedCode should include("class ProcessTupleOperator(UDFOperatorV2):")
+    injectedCode should include("""print("open")""")
+    injectedCode should not include ("_texera_injected_ui_parameters")
+    injectedCode should not include ("self.decode_python_template")
+  }
+
+  it should "inject ui parameter hook into supported UDF class" in {
     val injectedCode = PythonUdfUiParameterInjector.inject(
-      "print('done')",
-      List(createParameter("date", AttributeType.TIMESTAMP, "2024-01-01T00:00:00Z"))
+      baseUdfCode,
+      List(
+        createParameter("date", AttributeType.TIMESTAMP, "2024-01-01T00:00:00Z")
+      )
     )
 
-    injectedCode should include("UDFOperatorV2.set_injected_ui_parameters({")
+    injectedCode should include("class ProcessTupleOperator(UDFOperatorV2):")
+    injectedCode should include("def _texera_injected_ui_parameters(self):")
+    injectedCode should include("return {")
     injectedCode should include("self.decode_python_template")
-    injectedCode should include("print('done')")
+    injectedCode should include("""print("open")""")
+  }
+
+  it should "inject the reserved hook before the first method definition" in {
+    val injectedCode = PythonUdfUiParameterInjector.inject(
+      baseUdfCode,
+      List(createParameter("k", AttributeType.STRING, "v"))
+    )
+
+    val hookIndex = injectedCode.indexOf("def _texera_injected_ui_parameters(self):")
+    val openIndex = injectedCode.indexOf("def open(self):")
+
+    hookIndex should be >= 0
+    openIndex should be > hookIndex
   }
 
-  it should "throw when a key is declared with conflicting types" in {
+  it should "preserve multiple ui parameters in the injected map" in {
+    val injectedCode = PythonUdfUiParameterInjector.inject(
+      baseUdfCode,
+      List(
+        createParameter("param1", AttributeType.DOUBLE, "12.5"),
+        createParameter("param2", AttributeType.INTEGER, "1"),
+        createParameter("param3", AttributeType.STRING, "Hola"),
+        createParameter("param4", AttributeType.TIMESTAMP, "2026-02-28T03:15:00Z")
+      )
+    )
+
+    injectedCode should include("def _texera_injected_ui_parameters(self):")
+    injectedCode should include("self.decode_python_template")
+    injectedCode.count(_ == ':') should be > 0
+  }
+
+  it should "throw when a parameter attribute is missing" in {
+    val invalidParameter = new UiUDFParameter
+    invalidParameter.attribute = null
+    invalidParameter.value = "anything"
+
+    val exception = the[RuntimeException] thrownBy {
+      PythonUdfUiParameterInjector.inject(baseUdfCode, List(invalidParameter))
+    }
+
+    exception.getMessage should include("UiParameter attribute is required")
+  }
+
+  it should "throw when a key is declared with conflicting attribute types" in {
     val conflictingParameters = List(
-      createParameter("date", AttributeType.DATE, "2024-01-01"),
+      createParameter("date", AttributeType.STRING, "2024-01-01"),
+      createParameter("date", AttributeType.TIMESTAMP, "2024-01-01T00:00:00Z")
+    )
+
+    val exception = the[RuntimeException] thrownBy {
+      PythonUdfUiParameterInjector.inject(baseUdfCode, conflictingParameters)
+    }
+
+    exception.getMessage should include("UiParameter key 'date' has multiple types")
+  }
+
+  it should "allow duplicate keys when the attribute type is the same" in {
+    val sameTypeParameters = List(
+      createParameter("date", AttributeType.TIMESTAMP, "2024-01-01"),
       createParameter("date", AttributeType.TIMESTAMP, "2024-01-01T00:00:00Z")
     )
 
-    assertThrows[RuntimeException] {
-      PythonUdfUiParameterInjector.inject("print('done')", conflictingParameters)
+    noException should be thrownBy {
+      PythonUdfUiParameterInjector.inject(baseUdfCode, sameTypeParameters)
+    }
+  }
+
+  it should "throw when the reserved hook is already defined by the user" in {
+    val udfWithReservedHook =
+      """from pytexera import *
+        |
+        |class ProcessTupleOperator(UDFOperatorV2):
+        |    def _texera_injected_ui_parameters(self):
+        |        return {}
+        |
+        |    def open(self):
+        |        pass
+        |""".stripMargin
+
+    val exception = the[RuntimeException] thrownBy {
+      PythonUdfUiParameterInjector.inject(
+        udfWithReservedHook,
+        List(createParameter("k", AttributeType.STRING, "v"))
+      )
     }
+
+    exception.getMessage should include(
+      "Reserved method '_texera_injected_ui_parameters' is already defined"
+    )
+  }
+
+  it should "leave code unchanged when no supported user class is present" in {
+    val nonSupportedCode =
+      """from pytexera import *
+        |
+        |class SomethingElse:
+        |    def open(self):
+        |        pass
+        |""".stripMargin
+
+    val injectedCode = PythonUdfUiParameterInjector.inject(
+      nonSupportedCode,
+      List(createParameter("k", AttributeType.STRING, "v"))
+    )
+
+    injectedCode should not include ("_texera_injected_ui_parameters")
+    injectedCode should include("class SomethingElse:")
   }
 }
diff --git a/frontend/src/app/common/formly/collab-wrapper/collab-wrapper/collab-wrapper.component.css b/frontend/src/app/common/formly/collab-wrapper/collab-wrapper/collab-wrapper.component.css
index 5f583284045..500e875dfa7 100644
--- a/frontend/src/app/common/formly/collab-wrapper/collab-wrapper/collab-wrapper.component.css
+++ b/frontend/src/app/common/formly/collab-wrapper/collab-wrapper/collab-wrapper.component.css
@@ -25,7 +25,7 @@
   outline: transparent;
   overflow: visible;
   position: relative;
-  : 1pt;
+  :1pt;
 }
 
 :host ::ng-deep .ql-editor > p {
diff --git a/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts b/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
index 6f999a770f1..81c8db5abe9 100644
--- a/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
+++ b/frontend/src/app/workspace/component/code-editor-dialog/code-editor.component.ts
@@ -283,11 +283,7 @@ export class CodeEditorComponent implements AfterViewInit, SafeStyle, OnDestroy
         }
 
         if (this.code) {
-          this.detachYCodeListener =
-            this.uiUdfParametersSyncService.attachToYCode(
-              this.currentOperatorId,
-              this.code
-            );
+          this.detachYCodeListener = this.uiUdfParametersSyncService.attachToYCode(this.currentOperatorId, this.code);
         }
       });
   }
diff --git a/frontend/src/app/workspace/component/menu/coeditor-user-icon/coeditor-user-icon.component.css b/frontend/src/app/workspace/component/menu/coeditor-user-icon/coeditor-user-icon.component.css
index 826a2e3e64f..51da6c0f2bb 100644
--- a/frontend/src/app/workspace/component/menu/coeditor-user-icon/coeditor-user-icon.component.css
+++ b/frontend/src/app/workspace/component/menu/coeditor-user-icon/coeditor-user-icon.component.css
@@ -16,4 +16,3 @@
  * specific language governing permissions and limitations
  * under the License.
  */
-
diff --git a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
index 6e71b076e22..300c5bfcbab 100644
--- a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
+++ b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
@@ -201,10 +201,7 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
         if (operatorId !== this.currentOperatorId) return;
 
         const readCurrentUiParams = () =>
-          this.workflowActionService
-            .getTexeraGraph()
-            .getOperator(operatorId)
-            .operatorProperties?.uiParameters ?? [];
+          this.workflowActionService.getTexeraGraph().getOperator(operatorId).operatorProperties?.uiParameters ?? [];
 
         // initial read
         let currentUiParams = readCurrentUiParams();
@@ -216,10 +213,7 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
         let attempts = 0;
 
         while (!isEqual(currentUiParams, parameters) && attempts < MAX_ATTEMPTS) {
-          const currentOperator =
-            this.workflowActionService
-              .getTexeraGraph()
-              .getOperator(operatorId);
+          const currentOperator = this.workflowActionService.getTexeraGraph().getOperator(operatorId);
 
           const newModel = {
             ...cloneDeep(currentOperator.operatorProperties),
@@ -236,10 +230,10 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
         }
 
         if (!isEqual(currentUiParams, parameters)) {
-          console.warn(
-            `uiParameters did not converge after ${attempts}/${MAX_ATTEMPTS} attempts`,
-            { currentLen: currentUiParams.length, targetLen: parameters.length }
-          );
+          console.warn(`uiParameters did not converge after ${attempts}/${MAX_ATTEMPTS} attempts`, {
+            currentLen: currentUiParams.length,
+            targetLen: parameters.length,
+          });
         }
       });
   }
diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
index 16e859d79c8..8b9ffecdb2f 100644
--- a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
@@ -1,5 +1,6 @@
-<div class="ui-udf-param-list" *ngIf="(model?.length ?? 0) > 0">
-
+<div
+  class="ui-udf-param-list"
+  *ngIf="(model?.length ?? 0) > 0">
   <!-- Optional header row -->
   <div class="ui-udf-param-row header">
     <div class="field-cell"><span class="col-title">Value</span></div>
@@ -9,10 +10,8 @@
 
   <div
     class="ui-udf-param-row"
-    *ngFor="let param of (model || []); let i = index; trackBy: trackByParamName"
-  >
+    *ngFor="let param of (model || []); let i = index; trackBy: trackByParamName">
     <ng-container *ngIf="field.fieldGroup?.[i] as rowField">
-
       <!-- Value -->
       <div class="field-cell">
         <ng-container *ngIf="getValueField(rowField) as valueField">
@@ -33,7 +32,6 @@
           <formly-field [field]="typeField"></formly-field>
         </ng-container>
       </div>
-
     </ng-container>
   </div>
 </div>
diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
index 2ca3ffb0a3a..85972be9e76 100644
--- a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
@@ -28,9 +28,11 @@ export class UiUdfParametersComponent extends FieldArrayType {
 
     // 3) Enforce at the reactive form level
     if (field.formControl) {
-      disabled
-          ? field.formControl.disable({ emitEvent: false })
-          : field.formControl.enable({ emitEvent: false });
+      if (disabled) {
+        field.formControl.disable({ emitEvent: false });
+      } else {
+        field.formControl.enable({ emitEvent: false });
+      }
     } else {
       // If control isn't created yet, disable it at init time.
       const prevOnInit = field.hooks?.onInit;
diff --git a/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts
index cae70645aac..c28742977ff 100644
--- a/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts
+++ b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts
@@ -15,13 +15,11 @@ import { YText } from "yjs/dist/src/types/YText";
 
 @Injectable({ providedIn: "root" })
 export class UiUdfParametersSyncService {
+  private readonly uiParametersChangedSubject = new ReplaySubject<{ operatorId: string; parameters: UiUdfParameter[] }>(
+    1
+  );
 
-  private readonly uiParametersChangedSubject =
-    new ReplaySubject<{ operatorId: string; parameters: UiUdfParameter[] }>(1);
-
-
-  readonly uiParametersChanged$ =
-    this.uiParametersChangedSubject.asObservable();
+  readonly uiParametersChanged$ = this.uiParametersChangedSubject.asObservable();
 
   constructor(
     private workflowActionService: WorkflowActionService,
@@ -46,9 +44,7 @@ export class UiUdfParametersSyncService {
   }
 
   syncStructureFromCode(operatorId: string, codeFromEditor?: string): void {
-    const operator = this.workflowActionService
-      .getTexeraGraph()
-      .getOperator(operatorId);
+    const operator = this.workflowActionService.getTexeraGraph().getOperator(operatorId);
 
     if (!operator || !this.isSupportedPythonUdfType(operator.operatorType)) {
       return;
@@ -60,8 +56,7 @@ export class UiUdfParametersSyncService {
     }
 
     const existingParameters = operator.operatorProperties?.uiParameters ?? [];
-    const mergedUiParameters =
-      this.buildParsedShapeWithPreservedValues(code, existingParameters);
+    const mergedUiParameters = this.buildParsedShapeWithPreservedValues(code, existingParameters);
 
     if (isEqual(existingParameters, mergedUiParameters)) {
       return;
@@ -77,18 +72,12 @@ export class UiUdfParametersSyncService {
     // this.workflowActionService.setOperatorProperty(...)
   }
 
-  private buildParsedShapeWithPreservedValues(
-    code: string,
-    existingParameters: any[]
-  ): UiUdfParameter[] {
-    const parsedParameters =
-      this.uiUdfParametersParserService.parse(code);
+  private buildParsedShapeWithPreservedValues(code: string, existingParameters: any[]): UiUdfParameter[] {
+    const parsedParameters = this.uiUdfParametersParserService.parse(code);
 
     const existingValues = new Map<string, string>();
     existingParameters.forEach((parameter: any) => {
-      const parameterName =
-        parameter?.attribute?.attributeName ??
-        parameter?.attribute?.name;
+      const parameterName = parameter?.attribute?.attributeName ?? parameter?.attribute?.name;
 
       if (isDefined(parameterName) && isDefined(parameter?.value)) {
         existingValues.set(parameterName, parameter.value);
@@ -97,21 +86,17 @@ export class UiUdfParametersSyncService {
 
     return parsedParameters.map(parameter => ({
       ...parameter,
-      value:
-        existingValues.get(parameter.attribute.attributeName) ?? "",
+      value: existingValues.get(parameter.attribute.attributeName) ?? "",
     }));
   }
 
   private getSharedCode(operatorId: string): string | undefined {
     try {
-      const sharedOperatorType =
-        this.workflowActionService
-          .getTexeraGraph()
-          .getSharedOperatorType(operatorId);
+      const sharedOperatorType = this.workflowActionService.getTexeraGraph().getSharedOperatorType(operatorId);
 
-      const operatorProperties =
-        sharedOperatorType.get("operatorProperties") as
-          YType<Readonly<{ [key: string]: any }>>;
+      const operatorProperties = sharedOperatorType.get("operatorProperties") as YType<
+        Readonly<{ [key: string]: any }>
+      >;
 
       const yCode = operatorProperties.get("code") as YText;
       return yCode?.toString();
@@ -121,10 +106,8 @@ export class UiUdfParametersSyncService {
   }
 
   private isSupportedPythonUdfType(operatorType: string): boolean {
-    return [
-      PYTHON_UDF_V2_OP_TYPE,
-      PYTHON_UDF_SOURCE_V2_OP_TYPE,
-      DUAL_INPUT_PORTS_PYTHON_UDF_V2_OP_TYPE,
-    ].includes(operatorType);
+    return [PYTHON_UDF_V2_OP_TYPE, PYTHON_UDF_SOURCE_V2_OP_TYPE, DUAL_INPUT_PORTS_PYTHON_UDF_V2_OP_TYPE].includes(
+      operatorType
+    );
   }
 }
diff --git a/frontend/src/app/workspace/types/workflow-compiling.interface.ts b/frontend/src/app/workspace/types/workflow-compiling.interface.ts
index 1849552c0ab..a81d6f844cd 100644
--- a/frontend/src/app/workspace/types/workflow-compiling.interface.ts
+++ b/frontend/src/app/workspace/types/workflow-compiling.interface.ts
@@ -73,15 +73,41 @@ export type CompilationStateInfo = Readonly<
 
 // possible types of an attribute
 // Canonical frontend / JSON schema names
-export type AttributeType = "string" | "integer" | "long" | "double" | "boolean" | "timestamp" | "binary" | "large_binary";
+export type AttributeType =
+  | "string"
+  | "integer"
+  | "long"
+  | "double"
+  | "boolean"
+  | "timestamp"
+  | "binary"
+  | "large_binary";
 
 // Java enum constant names (AttributeType.java)
-export const JAVA_ATTRIBUTE_TYPE_NAMES = ["STRING", "INTEGER", "LONG", "DOUBLE", "BOOLEAN", "TIMESTAMP", "BINARY", "LARGE_BINARY",] as const;
+export const JAVA_ATTRIBUTE_TYPE_NAMES = [
+  "STRING",
+  "INTEGER",
+  "LONG",
+  "DOUBLE",
+  "BOOLEAN",
+  "TIMESTAMP",
+  "BINARY",
+  "LARGE_BINARY",
+] as const;
 
 export type JavaAttributeTypeName = (typeof JAVA_ATTRIBUTE_TYPE_NAMES)[number];
 
 // Python enum constant names (core.models.AttributeType)
-export const PYTHON_ATTRIBUTE_TYPE_NAMES = ["STRING", "INT", "LONG", "DOUBLE", "BOOL", "TIMESTAMP", "BINARY", "LARGE_BINARY",] as const;
+export const PYTHON_ATTRIBUTE_TYPE_NAMES = [
+  "STRING",
+  "INT",
+  "LONG",
+  "DOUBLE",
+  "BOOL",
+  "TIMESTAMP",
+  "BINARY",
+  "LARGE_BINARY",
+] as const;
 
 export type PythonAttributeTypeName = (typeof PYTHON_ATTRIBUTE_TYPE_NAMES)[number];
 

From 7a210a7f152e4e190afb054f3775d2bdbfed6ec1 Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Wed, 4 Mar 2026 02:10:02 -0600
Subject: [PATCH 04/12] py2udf

---
 .../src/main/scala/py2udf/catalog.json        | 2889 ++++++++++
 .../main/scala/py2udf/clean_sp500_2000.csv    | 5106 +++++++++++++++++
 .../demo_texera_workflow_vx2.workflow.json    |  400 ++
 ...emo_texera_workflow_vx2.workflow.meta.json |  398 ++
 .../src/main/scala/py2udf/py2udf.ipynb        | 2028 +++++++
 5 files changed, 10821 insertions(+)
 create mode 100644 common/workflow-operator/src/main/scala/py2udf/catalog.json
 create mode 100644 common/workflow-operator/src/main/scala/py2udf/clean_sp500_2000.csv
 create mode 100644 common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.json
 create mode 100644 common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.meta.json
 create mode 100644 common/workflow-operator/src/main/scala/py2udf/py2udf.ipynb

diff --git a/common/workflow-operator/src/main/scala/py2udf/catalog.json b/common/workflow-operator/src/main/scala/py2udf/catalog.json
new file mode 100644
index 00000000000..a65f77859a7
--- /dev/null
+++ b/common/workflow-operator/src/main/scala/py2udf/catalog.json
@@ -0,0 +1,2889 @@
+{
+  "operators": [
+    {
+      "operatorID": "ArrowSource-operator-2953ddfa-3b7c-4b40-be61-e86af2d5e4fa",
+      "operatorType": "ArrowSource",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "fileName": ""
+      },
+      "inputPorts": [],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Arrow File Scan",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "CSVFileScan-operator-7a2892e1-8812-41d9-80aa-273aa68cb8cc",
+      "operatorType": "CSVFileScan",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "fileEncoding": "UTF_8",
+        "customDelimiter": ",",
+        "hasHeader": true,
+        "fileName": ""
+      },
+      "inputPorts": [],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "CSV File Scan",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "FileScan-operator-a71dc68f-95db-433a-b927-4aa1748d6ee6",
+      "operatorType": "FileScan",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "encoding": "UTF_8",
+        "extract": false,
+        "outputFileName": false,
+        "attributeType": "string",
+        "attributeName": "line",
+        "fileName": ""
+      },
+      "inputPorts": [],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": " File Scan",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "JSONLFileScan-operator-fa519958-7a70-4761-a579-aa2192e026d9",
+      "operatorType": "JSONLFileScan",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "fileEncoding": "UTF_8",
+        "fileName": ""
+      },
+      "inputPorts": [],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "JSONL File Scan",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "TextInput-operator-c922ef7e-d321-495c-9517-25ebe6669d86",
+      "operatorType": "TextInput",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "attributeType": "string",
+        "attributeName": "line",
+        "textInput": ""
+      },
+      "inputPorts": [],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Text Input",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "DictionaryMatcher-operator-433f9c67-db8c-4682-8181-d7ee21dc446f",
+      "operatorType": "DictionaryMatcher",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "result attribute": "matched",
+        "Dictionary": "",
+        "Attribute": "",
+        "Matching type": ""
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Dictionary matcher",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "KeywordSearch-operator-f4d943e2-f145-483c-9b73-301201fbebc4",
+      "operatorType": "KeywordSearch",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "attribute": "",
+        "keyword": ""
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Keyword Search",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "Regex-operator-a822d4bb-174d-4cda-a282-5915d5bc5f22",
+      "operatorType": "Regex",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "caseInsensitive": false,
+        "attribute": "",
+        "regex": ""
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Regular Expression",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "SubstringSearch-operator-22333ad4-cf2c-4bdf-a204-a6ab2ab8e826",
+      "operatorType": "SubstringSearch",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "isCaseSensitive": false,
+        "attribute": "",
+        "substring": ""
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Substring Search",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "CartesianProduct-operator-98d5fc05-8542-43fc-97f3-4ffeee552a8b",
+      "operatorType": "CartesianProduct",
+      "operatorVersion": "N/A",
+      "operatorProperties": {},
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "left",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        },
+        {
+          "portID": "input-1",
+          "displayName": "right",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": [
+            {
+              "id": 0,
+              "internal": false
+            }
+          ]
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Cartesian Product",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "HashJoin-operator-6be1c0e0-4ffc-4b3a-9921-652089668587",
+      "operatorType": "HashJoin",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "joinType": "inner",
+        "buildAttributeName": "",
+        "probeAttributeName": ""
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "left",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        },
+        {
+          "portID": "input-1",
+          "displayName": "right",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": [
+            {
+              "id": 0,
+              "internal": false
+            }
+          ]
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Hash Join",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "IntervalJoin-operator-94ce5ee3-888d-4194-8750-6af5eafb3bcd",
+      "operatorType": "IntervalJoin",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "constant": 10,
+        "includeLeftBound": true,
+        "includeRightBound": true,
+        "timeIntervalType": "day",
+        "leftAttributeName": "",
+        "rightAttributeName": ""
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "left table",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        },
+        {
+          "portID": "input-1",
+          "displayName": "right table",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": [
+            {
+              "id": 0,
+              "internal": false
+            }
+          ]
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Interval Join",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "Aggregate-operator-fbcc0b1a-d5bc-42c7-89d9-5f99a98d0569",
+      "operatorType": "Aggregate",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "aggregations": [
+          {
+            "aggFunction": "",
+            "attribute": "",
+            "result attribute": ""
+          }
+        ]
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Aggregate",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "Distinct-operator-e55deaf5-0898-4af9-8195-965899c8ea9b",
+      "operatorType": "Distinct",
+      "operatorVersion": "N/A",
+      "operatorProperties": {},
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Distinct",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "Sort-operator-0ed0e580-1853-46a1-aca8-95c338099e8b",
+      "operatorType": "Sort",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "attributes": []
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Sort",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "Sort-operator-6ed8c337-70be-4e3a-b931-73aa0ecf0d42",
+      "operatorType": "Sort",
+      "operatorVersion": "N/A",
+      "operatorProperties": {},
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Sort",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
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+    {
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+    },
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+    },
+    {
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+      }
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+    {
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+      },
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+    },
+    {
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+      }
+    },
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+      }
+    },
+    {
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+      }
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+      }
+    },
+    {
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+    {
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+    },
+    {
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+      "source": {
+        "operatorID": "FunnelPlot-operator-1ace8f58-05f7-4fae-8b7a-48fa05ca869f",
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+      },
+      "target": {
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+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-46c41f5d-4114-4f2d-9f9a-f00c8b373466",
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+        "operatorID": "WaterfallChart-operator-e51ec00c-1815-4c16-b50e-2292e5780461",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "GaugeChart-operator-bdfb4bfd-51db-4300-a76b-919b66acbd34",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-1b06e764-16df-46b7-aac4-63d3b172abbe",
+      "source": {
+        "operatorID": "HTMLVisualizer-operator-c815a669-81af-4adb-865b-d9f2404cc249",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "CandlestickChart-operator-7f934e53-f4e0-4cb5-a0dc-db96f7d3de47",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-d216ac60-ff87-4c08-9ad2-db305e60420a",
+      "source": {
+        "operatorID": "ImageVisualizer-operator-569d29a2-d6d7-408d-8b3e-1920ae25fac3",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "FunnelPlot-operator-1ace8f58-05f7-4fae-8b7a-48fa05ca869f",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-135a2eee-804c-4997-8aba-3bdebd1747d8",
+      "source": {
+        "operatorID": "URLVisualizer-operator-187113ba-5c92-4e55-beff-8c23923cae3d",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "ImageVisualizer-operator-569d29a2-d6d7-408d-8b3e-1920ae25fac3",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-d2392f2a-c338-4f1d-b611-07f5eb36f16a",
+      "source": {
+        "operatorID": "ChoroplethMap-operator-e0b89bb9-e0f9-45eb-91e9-681d8ea4d5f0",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "URLVisualizer-operator-187113ba-5c92-4e55-beff-8c23923cae3d",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-a291b356-364d-4ab7-a969-3aca0d4404ce",
+      "source": {
+        "operatorID": "Scatter3DChart-operator-929fb224-6e2f-4917-84ed-b04c361424c7",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "NestedTable-operator-b107f3d0-9a4c-4118-8a5b-836bb94dc2a2",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-78af87f8-94d7-4d5a-9ca8-1373ef7113ad",
+      "source": {
+        "operatorID": "NestedTable-operator-b107f3d0-9a4c-4118-8a5b-836bb94dc2a2",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "WordCloud-operator-83815e13-64dc-4cb1-be4c-295d1f93fee6",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-968b2431-f25e-4de4-bc28-4cf692ccac35",
+      "source": {
+        "operatorID": "WordCloud-operator-83815e13-64dc-4cb1-be4c-295d1f93fee6",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "If-operator-8febb7e9-56b7-41c3-ae3e-b3dab428a7cd",
+        "portID": "input-0"
+      }
+    }
+  ],
+  "commentBoxes": [],
+  "settings": {
+    "dataTransferBatchSize": 400,
+    "executionMode": "PIPELINED"
+  }
+}
\ No newline at end of file
diff --git a/common/workflow-operator/src/main/scala/py2udf/clean_sp500_2000.csv b/common/workflow-operator/src/main/scala/py2udf/clean_sp500_2000.csv
new file mode 100644
index 00000000000..b1e782376af
--- /dev/null
+++ b/common/workflow-operator/src/main/scala/py2udf/clean_sp500_2000.csv
@@ -0,0 +1,5106 @@
+date,open,high,low,close,volume,daily_return,ma_30
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diff --git a/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.json b/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.json
new file mode 100644
index 00000000000..e17e1172872
--- /dev/null
+++ b/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.json
@@ -0,0 +1,400 @@
+{
+  "operators": [
+    {
+      "operatorID": "CSVFileScan-operator-52a5c4c1-6d4c-4ce6-8d58-c69e991b2b15",
+      "operatorType": "CSVFileScan",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "fileEncoding": "UTF_8",
+        "customDelimiter": ",",
+        "hasHeader": true,
+        "fileName": "https://raw.githubusercontent.com/vega/vega-datasets/main/data/sp500-2000.csv"
+      },
+      "inputPorts": [],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "CSV File Scan",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false
+    },
+    {
+      "operatorID": "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34",
+      "operatorType": "PythonUDFV2",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "code": "from typing import Iterator, Optional, Union\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pytexera import *\nfrom core.models.schema.attribute_type import AttributeType\nclass ProcessTableOperator(UDFTableOperator):\n\n    def open(self) -> None:\n        pass\n\n    @overrides\n    def process_table(self, table: Table, port: int) -> Iterator[Optional[TableLike]]:\n        selected = table[['date', 'open', 'high', 'low', 'close', 'volume']].copy()\n        print('\\nStep 2: Select columns')\n        print(selected.head())\n        yield selected\n        return\n\n    def close(self) -> None:\n        pass\n",
+        "workers": 1,
+        "retainInputColumns": false,
+        "outputColumns": [
+          {
+            "attributeName": "date",
+            "attributeType": "string"
+          },
+          {
+            "attributeName": "open",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "high",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "low",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "close",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "volume",
+            "attributeType": "long"
+          }
+        ]
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": true,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Select Columns",
+      "dynamicInputPorts": true,
+      "dynamicOutputPorts": true
+    },
+    {
+      "operatorID": "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf",
+      "operatorType": "PythonUDFV2",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "code": "from typing import Iterator, Optional, Union\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pytexera import *\nfrom core.models.schema.attribute_type import AttributeType\nclass ProcessTableOperator(UDFTableOperator):\n\n    def open(self) -> None:\n        pass\n\n    @overrides\n    def process_table(self, table: Table, port: int) -> Iterator[Optional[TableLike]]:\n        cleaned = table.dropna().copy()\n        print('\\nStep 3: Clean data')\n        print(cleaned.head())\n        yield cleaned\n        return\n\n    def close(self) -> None:\n        pass\n",
+        "workers": 1,
+        "retainInputColumns": false,
+        "outputColumns": [
+          {
+            "attributeName": "date",
+            "attributeType": "string"
+          },
+          {
+            "attributeName": "open",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "high",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "low",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "close",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "volume",
+            "attributeType": "long"
+          }
+        ]
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": true,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Clean Data",
+      "dynamicInputPorts": true,
+      "dynamicOutputPorts": true
+    },
+    {
+      "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
+      "operatorType": "PythonUDFV2",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "code": "from typing import Iterator, Optional, Union\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pytexera import *\nfrom core.models.schema.attribute_type import AttributeType\nclass ProcessTableOperator(UDFTableOperator):\n\n    def open(self) -> None:\n        pass\n\n    @overrides\n    def process_table(self, table: Table, port: int) -> Iterator[Optional[TableLike]]:\n        transformed = table.copy()\n        transformed['date'] = pd.to_datetime(transformed['date'], errors='coerce')\n        transformed = transformed.dropna(subset=['date']).copy()\n        transformed = transformed.sort_values('date')\n        transformed['daily_return'] = transformed['close'].pct_change() * 100\n        transformed['ma_30'] = transformed['close'].rolling(window=30).mean()\n        print('\\nStep 4: Transform data')\n        print(transformed.head())\n        yield transformed\n        return\n\n    def close(self) -> None:\n        pass\n",
+        "workers": 1,
+        "retainInputColumns": false,
+        "outputColumns": [
+          {
+            "attributeName": "date",
+            "attributeType": "timestamp"
+          },
+          {
+            "attributeName": "open",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "high",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "low",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "close",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "volume",
+            "attributeType": "long"
+          },
+          {
+            "attributeName": "daily_return",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "ma_30",
+            "attributeType": "double"
+          }
+        ]
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": true,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Transform Data",
+      "dynamicInputPorts": true,
+      "dynamicOutputPorts": true
+    },
+    {
+      "operatorID": "PythonUDFV2-operator-f45cfd8f-0f39-42c6-a97a-35a0094359da",
+      "operatorType": "PythonUDFV2",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "code": "from typing import Iterator, Optional, Union\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pytexera import *\nfrom core.models.schema.attribute_type import AttributeType\nclass ProcessTableOperator(UDFTableOperator):\n\n    def open(self) -> None:\n        self.output_file = 'clean_sp500_2000.csv'\n        # Temporary hardcoded default for UI parameter 'output_file' from the source script.\n        # self.UiParameter('output_file', AttributeType.STRING)  # UiParameter disabled for now\n\n    @overrides\n    def process_table(self, table: Table, port: int) -> Iterator[Optional[TableLike]]:\n        table.to_csv(self.output_file, index=False)\n        print(f'\\nStep 5: Load data')\n        print(f'Saved cleaned data to: {self.output_file}')\n        yield table\n\n    def close(self) -> None:\n        pass\n",
+        "workers": 1,
+        "retainInputColumns": false,
+        "outputColumns": [
+          {
+            "attributeName": "date",
+            "attributeType": "timestamp"
+          },
+          {
+            "attributeName": "open",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "high",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "low",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "close",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "volume",
+            "attributeType": "long"
+          },
+          {
+            "attributeName": "daily_return",
+            "attributeType": "double"
+          },
+          {
+            "attributeName": "ma_30",
+            "attributeType": "double"
+          }
+        ]
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": true,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Load Data",
+      "dynamicInputPorts": true,
+      "dynamicOutputPorts": true
+    },
+    {
+      "operatorID": "TimeSeriesPlot-operator-4c61e814-c060-4a03-8170-2bd0c441ac8e",
+      "operatorType": "TimeSeriesPlot",
+      "operatorVersion": "N/A",
+      "operatorProperties": {
+        "timeColumn": "date",
+        "valueColumn": "close",
+        "line": "close",
+        "facetColumn": "No Selection",
+        "categoryColumn": "No Selection",
+        "slider": false
+      },
+      "inputPorts": [
+        {
+          "portID": "input-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false,
+          "dependencies": []
+        }
+      ],
+      "outputPorts": [
+        {
+          "portID": "output-0",
+          "displayName": "",
+          "allowMultiInputs": false,
+          "isDynamicPort": false
+        }
+      ],
+      "showAdvanced": false,
+      "isDisabled": false,
+      "customDisplayName": "Plot Data",
+      "dynamicInputPorts": false,
+      "dynamicOutputPorts": false,
+      "viewResult": true
+    }
+  ],
+  "operatorPositions": {
+    "CSVFileScan-operator-52a5c4c1-6d4c-4ce6-8d58-c69e991b2b15": {
+      "x": 240,
+      "y": 180
+    },
+    "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34": {
+      "x": 540,
+      "y": 180
+    },
+    "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf": {
+      "x": 840,
+      "y": 180
+    },
+    "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798": {
+      "x": 1140,
+      "y": 180
+    },
+    "PythonUDFV2-operator-f45cfd8f-0f39-42c6-a97a-35a0094359da": {
+      "x": 1440,
+      "y": 180
+    },
+    "TimeSeriesPlot-operator-4c61e814-c060-4a03-8170-2bd0c441ac8e": {
+      "x": 1440,
+      "y": 360
+    }
+  },
+  "links": [
+    {
+      "linkID": "link-84bd3626-9908-4fb1-b746-ad9da043aa92",
+      "source": {
+        "operatorID": "CSVFileScan-operator-52a5c4c1-6d4c-4ce6-8d58-c69e991b2b15",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-49eb65dd-4c68-4df6-a245-aedb508b7857",
+      "source": {
+        "operatorID": "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-bf345bd5-6196-482e-bda1-bba512abf7da",
+      "source": {
+        "operatorID": "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-6031e10a-0ac6-42af-9321-2782a2be6d8e",
+      "source": {
+        "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "PythonUDFV2-operator-f45cfd8f-0f39-42c6-a97a-35a0094359da",
+        "portID": "input-0"
+      }
+    },
+    {
+      "linkID": "link-9b1eacbb-1a02-47b7-b14e-6c1d41964c8e",
+      "source": {
+        "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
+        "portID": "output-0"
+      },
+      "target": {
+        "operatorID": "TimeSeriesPlot-operator-4c61e814-c060-4a03-8170-2bd0c441ac8e",
+        "portID": "input-0"
+      }
+    }
+  ],
+  "commentBoxes": [],
+  "settings": {
+    "dataTransferBatchSize": 400
+  }
+}
diff --git a/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.meta.json b/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.meta.json
new file mode 100644
index 00000000000..8136a9df4bc
--- /dev/null
+++ b/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.meta.json
@@ -0,0 +1,398 @@
+{
+  "entry": "main",
+  "catalog_path": "catalog.json",
+  "stages": [
+    {
+      "stage_id": "extract_data__0",
+      "function": "extract_data",
+      "operatorID": "CSVFileScan-operator-52a5c4c1-6d4c-4ce6-8d58-c69e991b2b15",
+      "operator_type": "CSVFileScan",
+      "native_operator_type": "CSVFileScan",
+      "family": "table",
+      "base_class": null,
+      "method": null,
+      "is_source": true,
+      "is_sink": true,
+      "recursive": false,
+      "has_loop": false,
+      "assigned_to": [
+        "raw_df"
+      ],
+      "data_inputs": [],
+      "ui_parameters": [
+        {
+          "name": "url",
+          "attr_type": "AttributeType.STRING",
+          "default": "'https://raw.githubusercontent.com/vega/vega-datasets/main/data/sp500-2000.csv'"
+        }
+      ],
+      "source_columns": [
+        {
+          "attributeName": "date",
+          "attributeType": "string"
+        },
+        {
+          "attributeName": "open",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "high",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "low",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "close",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "adjclose",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "volume",
+          "attributeType": "long"
+        }
+      ],
+      "output_columns": [
+        {
+          "attributeName": "date",
+          "attributeType": "string"
+        },
+        {
+          "attributeName": "open",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "high",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "low",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "close",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "adjclose",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "volume",
+          "attributeType": "long"
+        }
+      ],
+      "retain_input_columns": false,
+      "native_notes": [
+        "Matched pandas.read_csv to CSVFileScan"
+      ]
+    },
+    {
+      "stage_id": "select_columns__1",
+      "function": "select_columns",
+      "operatorID": "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34",
+      "operator_type": "PythonUDFV2",
+      "native_operator_type": null,
+      "family": "table",
+      "base_class": "UDFTableOperator",
+      "method": "process_table",
+      "is_source": false,
+      "is_sink": true,
+      "recursive": false,
+      "has_loop": false,
+      "assigned_to": [
+        "selected_df"
+      ],
+      "data_inputs": [
+        {
+          "param_name": "df",
+          "upstream_stage_id": "extract_data__0",
+          "upstream_var": "raw_df",
+          "input_port": 0
+        }
+      ],
+      "ui_parameters": [],
+      "source_columns": [],
+      "output_columns": [
+        {
+          "attributeName": "date",
+          "attributeType": "string"
+        },
+        {
+          "attributeName": "open",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "high",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "low",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "close",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "volume",
+          "attributeType": "long"
+        }
+      ],
+      "retain_input_columns": false,
+      "native_notes": []
+    },
+    {
+      "stage_id": "clean_data__2",
+      "function": "clean_data",
+      "operatorID": "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf",
+      "operator_type": "PythonUDFV2",
+      "native_operator_type": null,
+      "family": "table",
+      "base_class": "UDFTableOperator",
+      "method": "process_table",
+      "is_source": false,
+      "is_sink": true,
+      "recursive": false,
+      "has_loop": false,
+      "assigned_to": [
+        "cleaned_df"
+      ],
+      "data_inputs": [
+        {
+          "param_name": "df",
+          "upstream_stage_id": "select_columns__1",
+          "upstream_var": "selected_df",
+          "input_port": 0
+        }
+      ],
+      "ui_parameters": [],
+      "source_columns": [],
+      "output_columns": [
+        {
+          "attributeName": "date",
+          "attributeType": "string"
+        },
+        {
+          "attributeName": "open",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "high",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "low",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "close",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "volume",
+          "attributeType": "long"
+        }
+      ],
+      "retain_input_columns": false,
+      "native_notes": []
+    },
+    {
+      "stage_id": "transform_data__3",
+      "function": "transform_data",
+      "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
+      "operator_type": "PythonUDFV2",
+      "native_operator_type": null,
+      "family": "table",
+      "base_class": "UDFTableOperator",
+      "method": "process_table",
+      "is_source": false,
+      "is_sink": true,
+      "recursive": false,
+      "has_loop": false,
+      "assigned_to": [
+        "transformed_df"
+      ],
+      "data_inputs": [
+        {
+          "param_name": "df",
+          "upstream_stage_id": "clean_data__2",
+          "upstream_var": "cleaned_df",
+          "input_port": 0
+        }
+      ],
+      "ui_parameters": [],
+      "source_columns": [],
+      "output_columns": [
+        {
+          "attributeName": "date",
+          "attributeType": "timestamp"
+        },
+        {
+          "attributeName": "open",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "high",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "low",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "close",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "volume",
+          "attributeType": "long"
+        },
+        {
+          "attributeName": "daily_return",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "ma_30",
+          "attributeType": "double"
+        }
+      ],
+      "retain_input_columns": false,
+      "native_notes": []
+    },
+    {
+      "stage_id": "load_data__4",
+      "function": "load_data",
+      "operatorID": "PythonUDFV2-operator-f45cfd8f-0f39-42c6-a97a-35a0094359da",
+      "operator_type": "PythonUDFV2",
+      "native_operator_type": null,
+      "family": "table",
+      "base_class": "UDFTableOperator",
+      "method": "process_table",
+      "is_source": false,
+      "is_sink": true,
+      "recursive": false,
+      "has_loop": false,
+      "assigned_to": [],
+      "data_inputs": [
+        {
+          "param_name": "df",
+          "upstream_stage_id": "transform_data__3",
+          "upstream_var": "transformed_df",
+          "input_port": 0
+        }
+      ],
+      "ui_parameters": [
+        {
+          "name": "output_file",
+          "attr_type": "AttributeType.STRING",
+          "default": "'clean_sp500_2000.csv'"
+        }
+      ],
+      "source_columns": [],
+      "output_columns": [
+        {
+          "attributeName": "date",
+          "attributeType": "timestamp"
+        },
+        {
+          "attributeName": "open",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "high",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "low",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "close",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "volume",
+          "attributeType": "long"
+        },
+        {
+          "attributeName": "daily_return",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "ma_30",
+          "attributeType": "double"
+        }
+      ],
+      "retain_input_columns": false,
+      "native_notes": []
+    },
+    {
+      "stage_id": "plot_data__5",
+      "function": "plot_data",
+      "operatorID": "TimeSeriesPlot-operator-4c61e814-c060-4a03-8170-2bd0c441ac8e",
+      "operator_type": "TimeSeriesPlot",
+      "native_operator_type": "TimeSeriesPlot",
+      "family": "table",
+      "base_class": null,
+      "method": null,
+      "is_source": false,
+      "is_sink": true,
+      "recursive": false,
+      "has_loop": false,
+      "assigned_to": [],
+      "data_inputs": [
+        {
+          "param_name": "df",
+          "upstream_stage_id": "transform_data__3",
+          "upstream_var": "transformed_df",
+          "input_port": 0
+        }
+      ],
+      "ui_parameters": [],
+      "source_columns": [],
+      "output_columns": [
+        {
+          "attributeName": "date",
+          "attributeType": "timestamp"
+        },
+        {
+          "attributeName": "open",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "high",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "low",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "close",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "volume",
+          "attributeType": "long"
+        },
+        {
+          "attributeName": "daily_return",
+          "attributeType": "double"
+        },
+        {
+          "attributeName": "ma_30",
+          "attributeType": "double"
+        }
+      ],
+      "retain_input_columns": true,
+      "native_notes": [
+        "Matched line/time-series plot on x='date', y='close'"
+      ]
+    }
+  ]
+}
diff --git a/common/workflow-operator/src/main/scala/py2udf/py2udf.ipynb b/common/workflow-operator/src/main/scala/py2udf/py2udf.ipynb
new file mode 100644
index 00000000000..b5125c6b556
--- /dev/null
+++ b/common/workflow-operator/src/main/scala/py2udf/py2udf.ipynb
@@ -0,0 +1,2028 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T07:52:53.906136Z",
+     "start_time": "2026-03-04T07:52:52.994657Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import ast\n",
+    "import copy\n",
+    "import json\n",
+    "import uuid\n",
+    "from dataclasses import dataclass, field\n",
+    "from pathlib import Path\n",
+    "from typing import Any, Optional\n",
+    "\n",
+    "\n",
+    "ATTRIBUTE_TYPE_FOR_PY = {\n",
+    "    \"str\": \"AttributeType.STRING\",\n",
+    "    \"int\": \"AttributeType.INTEGER\",\n",
+    "    \"float\": \"AttributeType.DOUBLE\",\n",
+    "    \"bool\": \"AttributeType.BOOLEAN\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "@dataclass\n",
+    "class FunctionFacts:\n",
+    "    name: str\n",
+    "    params: list[str]\n",
+    "    returns_annotation: str | None\n",
+    "    called_functions: set[str] = field(default_factory=set)\n",
+    "    has_loop: bool = False\n",
+    "    recursive: bool = False\n",
+    "    family: str = \"table\"  # source | table | batch | tuple\n",
+    "    is_sink: bool = False\n",
+    "    globals_used: set[str] = field(default_factory=set)\n",
+    "\n",
+    "\n",
+    "@dataclass\n",
+    "class UiParamSpec:\n",
+    "    name: str\n",
+    "    attr_type: str\n",
+    "    default_ast: ast.AST | None = None\n",
+    "\n",
+    "\n",
+    "@dataclass\n",
+    "class DataInputSpec:\n",
+    "    param_name: str\n",
+    "    upstream_stage_id: str\n",
+    "    upstream_var: str\n",
+    "    input_port: int\n",
+    "\n",
+    "\n",
+    "@dataclass\n",
+    "class StageInvocation:\n",
+    "    stage_id: str\n",
+    "    function_name: str\n",
+    "    family: str\n",
+    "    is_source: bool\n",
+    "    is_sink: bool\n",
+    "    recursive: bool\n",
+    "    has_loop: bool\n",
+    "    assigned_to: list[str]\n",
+    "    data_inputs: list[DataInputSpec] = field(default_factory=list)\n",
+    "    ui_params: list[UiParamSpec] = field(default_factory=list)\n",
+    "    operator_id: str = \"\"\n",
+    "    display_name: str = \"\"\n",
+    "    code: str = \"\"\n",
+    "    operator_type: str = \"PythonUDFV2\"\n",
+    "    source_columns: list[dict[str, str]] = field(default_factory=list)\n",
+    "    output_columns: list[dict[str, str]] = field(default_factory=list)\n",
+    "    retain_input_columns: bool = True\n",
+    "\n",
+    "\n",
+    "class SourceModuleParser:\n",
+    "    def parse(self, source: str) -> ast.Module:\n",
+    "        return ast.parse(source)\n",
+    "\n",
+    "\n",
+    "class ImportCollector:\n",
+    "    def collect_nodes(self, tree: ast.Module) -> list[ast.stmt]:\n",
+    "        return [n for n in tree.body if isinstance(n, (ast.Import, ast.ImportFrom))]\n",
+    "\n",
+    "    def collect_names(self, tree: ast.Module) -> set[str]:\n",
+    "        names = set()\n",
+    "        for node in self.collect_nodes(tree):\n",
+    "            if isinstance(node, ast.Import):\n",
+    "                for alias in node.names:\n",
+    "                    names.add(alias.asname or alias.name.split(\".\")[0])\n",
+    "            else:\n",
+    "                for alias in node.names:\n",
+    "                    names.add(alias.asname or alias.name)\n",
+    "        return names\n",
+    "\n",
+    "\n",
+    "class FunctionIndex:\n",
+    "    def collect(self, tree: ast.Module) -> dict[str, ast.FunctionDef]:\n",
+    "        return {n.name: n for n in tree.body if isinstance(n, ast.FunctionDef)}\n",
+    "\n",
+    "\n",
+    "class CallCollector(ast.NodeVisitor):\n",
+    "    def __init__(self):\n",
+    "        self.calls: list[ast.Call] = []\n",
+    "\n",
+    "    def visit_Call(self, node: ast.Call):\n",
+    "        self.calls.append(node)\n",
+    "        self.generic_visit(node)\n",
+    "\n",
+    "\n",
+    "class CallGraphBuilder:\n",
+    "    def build(self, functions: dict[str, ast.FunctionDef]) -> dict[str, set[str]]:\n",
+    "        graph = {name: set() for name in functions}\n",
+    "        for name, fn in functions.items():\n",
+    "            collector = CallCollector()\n",
+    "            collector.visit(fn)\n",
+    "            for call in collector.calls:\n",
+    "                if isinstance(call.func, ast.Name) and call.func.id in functions:\n",
+    "                    graph[name].add(call.func.id)\n",
+    "        return graph\n",
+    "\n",
+    "\n",
+    "class RecursiveAnalyzer:\n",
+    "    def strongly_connected_components(self, graph: dict[str, set[str]]) -> list[list[str]]:\n",
+    "        index = 0\n",
+    "        stack: list[str] = []\n",
+    "        on_stack: set[str] = set()\n",
+    "        indices: dict[str, int] = {}\n",
+    "        lowlink: dict[str, int] = {}\n",
+    "        comps: list[list[str]] = []\n",
+    "\n",
+    "        def strongconnect(v: str):\n",
+    "            nonlocal index\n",
+    "            indices[v] = index\n",
+    "            lowlink[v] = index\n",
+    "            index += 1\n",
+    "            stack.append(v)\n",
+    "            on_stack.add(v)\n",
+    "\n",
+    "            for w in graph[v]:\n",
+    "                if w not in indices:\n",
+    "                    strongconnect(w)\n",
+    "                    lowlink[v] = min(lowlink[v], lowlink[w])\n",
+    "                elif w in on_stack:\n",
+    "                    lowlink[v] = min(lowlink[v], indices[w])\n",
+    "\n",
+    "            if lowlink[v] == indices[v]:\n",
+    "                comp = []\n",
+    "                while True:\n",
+    "                    w = stack.pop()\n",
+    "                    on_stack.remove(w)\n",
+    "                    comp.append(w)\n",
+    "                    if w == v:\n",
+    "                        break\n",
+    "                comps.append(comp)\n",
+    "\n",
+    "        for v in graph:\n",
+    "            if v not in indices:\n",
+    "                strongconnect(v)\n",
+    "        return comps\n",
+    "\n",
+    "    def detect(self, graph: dict[str, set[str]]) -> set[str]:\n",
+    "        recursive = set()\n",
+    "        for comp in self.strongly_connected_components(graph):\n",
+    "            if len(comp) > 1:\n",
+    "                recursive.update(comp)\n",
+    "            elif len(comp) == 1 and comp[0] in graph[comp[0]]:\n",
+    "                recursive.add(comp[0])\n",
+    "        return recursive\n",
+    "\n",
+    "\n",
+    "class NameUsageCollector(ast.NodeVisitor):\n",
+    "    def __init__(self):\n",
+    "        self.loads: set[str] = set()\n",
+    "        self.stores: set[str] = set()\n",
+    "\n",
+    "    def visit_Name(self, node: ast.Name):\n",
+    "        if isinstance(node.ctx, ast.Load):\n",
+    "            self.loads.add(node.id)\n",
+    "        elif isinstance(node.ctx, ast.Store):\n",
+    "            self.stores.add(node.id)\n",
+    "\n",
+    "    def visit_FunctionDef(self, node):\n",
+    "        return\n",
+    "\n",
+    "    def visit_ClassDef(self, node):\n",
+    "        return\n",
+    "\n",
+    "\n",
+    "class FamilyInferer:\n",
+    "    def infer(self, fn: ast.FunctionDef) -> str:\n",
+    "        ann_texts = []\n",
+    "        for a in fn.args.args:\n",
+    "            if a.annotation is not None:\n",
+    "                ann_texts.append(ast.unparse(a.annotation))\n",
+    "        if fn.returns is not None:\n",
+    "            ann_texts.append(ast.unparse(fn.returns))\n",
+    "        joined = \" | \".join(ann_texts)\n",
+    "\n",
+    "        if any(tok in joined for tok in [\"TupleLike\", \"Tuple\", \"tuple_\"]):\n",
+    "            return \"tuple\"\n",
+    "        if any(tok in joined for tok in [\"BatchLike\", \"Batch\"]):\n",
+    "            return \"batch\"\n",
+    "        if any(tok in joined for tok in [\"DataFrame\", \"TableLike\", \"Table\", \"pd.DataFrame\"]):\n",
+    "            return \"table\"\n",
+    "\n",
+    "        source = ast.unparse(fn)\n",
+    "        if any(tok in source for tok in [\"pd.\", \".copy(\", \".dropna(\", \".sort_values(\", \".rolling(\"]):\n",
+    "            return \"table\"\n",
+    "        return \"table\"\n",
+    "\n",
+    "\n",
+    "class SinkDetector:\n",
+    "    def detect(self, fn: ast.FunctionDef) -> bool:\n",
+    "        source = ast.unparse(fn)\n",
+    "        if any(tok in source for tok in [\"to_csv(\", \".show(\", \"plt.\", \".save(\", \"print(\"]):\n",
+    "            return True\n",
+    "        if fn.returns is None:\n",
+    "            for stmt in fn.body:\n",
+    "                if isinstance(stmt, ast.Return) and stmt.value is not None:\n",
+    "                    return False\n",
+    "            return True\n",
+    "        return False\n",
+    "\n",
+    "\n",
+    "class LoopCollector(ast.NodeVisitor):\n",
+    "    def __init__(self, function_name: str):\n",
+    "        self.function_name = function_name\n",
+    "        self.has_loop = False\n",
+    "\n",
+    "    def visit_For(self, node: ast.For):\n",
+    "        self.has_loop = True\n",
+    "        self.generic_visit(node)\n",
+    "\n",
+    "    def visit_While(self, node: ast.While):\n",
+    "        self.has_loop = True\n",
+    "        self.generic_visit(node)\n",
+    "\n",
+    "\n",
+    "class FunctionFactsAnalyzer:\n",
+    "    def __init__(self):\n",
+    "        self.family_inferer = FamilyInferer()\n",
+    "        self.sink_detector = SinkDetector()\n",
+    "\n",
+    "    def analyze(\n",
+    "        self,\n",
+    "        tree: ast.Module,\n",
+    "        functions: dict[str, ast.FunctionDef],\n",
+    "        graph: dict[str, set[str]],\n",
+    "        recursive: set[str],\n",
+    "    ) -> dict[str, FunctionFacts]:\n",
+    "        import_names = ImportCollector().collect_names(tree)\n",
+    "        facts: dict[str, FunctionFacts] = {}\n",
+    "        for name, fn in functions.items():\n",
+    "            usage = NameUsageCollector()\n",
+    "            usage.visit(fn)\n",
+    "            loops = LoopCollector(name)\n",
+    "            loops.visit(fn)\n",
+    "            facts[name] = FunctionFacts(\n",
+    "                name=name,\n",
+    "                params=[a.arg for a in fn.args.args],\n",
+    "                returns_annotation=ast.unparse(fn.returns) if fn.returns is not None else None,\n",
+    "                called_functions=set(graph[name]),\n",
+    "                has_loop=loops.has_loop,\n",
+    "                recursive=name in recursive,\n",
+    "                family=self.family_inferer.infer(fn),\n",
+    "                is_sink=self.sink_detector.detect(fn),\n",
+    "                globals_used=(usage.loads - usage.stores - {a.arg for a in fn.args.args}) & import_names,\n",
+    "            )\n",
+    "        return facts\n",
+    "\n",
+    "\n",
+    "class SourceSchemaInferer:\n",
+    "    def __init__(self):\n",
+    "        try:\n",
+    "            import pandas as _pd\n",
+    "        except Exception:\n",
+    "            _pd = None\n",
+    "        self.pd = _pd\n",
+    "\n",
+    "    def _literal_value(self, node: ast.AST | None):\n",
+    "        if isinstance(node, ast.Constant):\n",
+    "            return node.value\n",
+    "        return None\n",
+    "\n",
+    "    def _resolve_expr(self, expr: ast.AST | None, ui_defaults: dict[str, Any]):\n",
+    "        if expr is None:\n",
+    "            return None\n",
+    "        if isinstance(expr, ast.Constant):\n",
+    "            return expr.value\n",
+    "        if isinstance(expr, ast.Name):\n",
+    "            return ui_defaults.get(expr.id)\n",
+    "        return None\n",
+    "\n",
+    "    def _map_dtype(self, dtype: Any) -> str:\n",
+    "        kind = getattr(dtype, \"kind\", None)\n",
+    "        if kind == \"b\":\n",
+    "            return \"boolean\"\n",
+    "        if kind == \"i\":\n",
+    "            try:\n",
+    "                itemsize = int(getattr(dtype, \"itemsize\", 8) or 8)\n",
+    "            except Exception:\n",
+    "                itemsize = 8\n",
+    "            return \"integer\" if itemsize <= 4 else \"long\"\n",
+    "        if kind == \"u\":\n",
+    "            try:\n",
+    "                itemsize = int(getattr(dtype, \"itemsize\", 8) or 8)\n",
+    "            except Exception:\n",
+    "                itemsize = 8\n",
+    "            return \"integer\" if itemsize <= 4 else \"long\"\n",
+    "        if kind == \"f\":\n",
+    "            return \"double\"\n",
+    "        if kind == \"M\":\n",
+    "            return \"timestamp\"\n",
+    "        if kind == \"S\":\n",
+    "            return \"binary\"\n",
+    "        return \"string\"\n",
+    "\n",
+    "    def infer(self, fn: ast.FunctionDef, stage: StageInvocation) -> list[dict[str, str]]:\n",
+    "        if not stage.is_source or stage.family != \"table\" or self.pd is None:\n",
+    "            return []\n",
+    "\n",
+    "        ui_defaults = {spec.name: self._literal_value(spec.default_ast) for spec in stage.ui_params}\n",
+    "\n",
+    "        for node in ast.walk(fn):\n",
+    "            if not isinstance(node, ast.Call):\n",
+    "                continue\n",
+    "            if not isinstance(node.func, ast.Attribute):\n",
+    "                continue\n",
+    "            if not (isinstance(node.func.value, ast.Name) and node.func.value.id == \"pd\" and node.func.attr == \"read_csv\"):\n",
+    "                continue\n",
+    "\n",
+    "            source_expr = None\n",
+    "            if node.args:\n",
+    "                source_expr = node.args[0]\n",
+    "            else:\n",
+    "                for kw in node.keywords:\n",
+    "                    if kw.arg in (\"filepath_or_buffer\", \"path\", \"url\"):\n",
+    "                        source_expr = kw.value\n",
+    "                        break\n",
+    "\n",
+    "            source_value = self._resolve_expr(source_expr, ui_defaults)\n",
+    "            if not isinstance(source_value, str) or not source_value:\n",
+    "                continue\n",
+    "\n",
+    "            try:\n",
+    "                df = self.pd.read_csv(source_value, nrows=50)\n",
+    "            except Exception:\n",
+    "                continue\n",
+    "\n",
+    "            cols: list[dict[str, str]] = []\n",
+    "            for col in df.columns:\n",
+    "                try:\n",
+    "                    dtype = df[col].dtype\n",
+    "                except Exception:\n",
+    "                    dtype = None\n",
+    "                cols.append({\n",
+    "                    \"attributeName\": str(col),\n",
+    "                    \"attributeType\": self._map_dtype(dtype),\n",
+    "                })\n",
+    "            if cols:\n",
+    "                return cols\n",
+    "\n",
+    "        return []\n",
+    "\n",
+    "\n",
+    "class TableOutputSchemaInferer:\n",
+    "    def _schema_dict(self, cols: list[dict[str, str]]) -> dict[str, str]:\n",
+    "        return {c[\"attributeName\"]: c[\"attributeType\"] for c in cols if c.get(\"attributeName\")}\n",
+    "\n",
+    "    def _schema_list(self, d: dict[str, str]) -> list[dict[str, str]]:\n",
+    "        return [{\"attributeName\": k, \"attributeType\": v} for k, v in d.items()]\n",
+    "\n",
+    "    def _list_of_constant_strings(self, node: ast.AST | None) -> list[str] | None:\n",
+    "        if isinstance(node, (ast.List, ast.Tuple)):\n",
+    "            vals = []\n",
+    "            for elt in node.elts:\n",
+    "                if isinstance(elt, ast.Constant) and isinstance(elt.value, str):\n",
+    "                    vals.append(elt.value)\n",
+    "                else:\n",
+    "                    return None\n",
+    "            return vals\n",
+    "        return None\n",
+    "\n",
+    "    def _infer_scalar_type(self, expr: ast.AST, env: dict[str, dict[str, str]]) -> str:\n",
+    "        if isinstance(expr, ast.Constant):\n",
+    "            v = expr.value\n",
+    "            if isinstance(v, bool):\n",
+    "                return \"boolean\"\n",
+    "            if isinstance(v, int):\n",
+    "                return \"long\"\n",
+    "            if isinstance(v, float):\n",
+    "                return \"double\"\n",
+    "            return \"string\"\n",
+    "        if isinstance(expr, ast.Subscript) and isinstance(expr.value, ast.Name):\n",
+    "            if isinstance(expr.slice, ast.Constant) and isinstance(expr.slice.value, str):\n",
+    "                return env.get(expr.value.id, {}).get(expr.slice.value, \"string\")\n",
+    "        if isinstance(expr, ast.BinOp):\n",
+    "            return \"double\"\n",
+    "        if isinstance(expr, ast.Call) and isinstance(expr.func, ast.Attribute):\n",
+    "            attr = expr.func.attr\n",
+    "            if isinstance(expr.func.value, ast.Name) and expr.func.value.id == \"pd\" and attr == \"to_datetime\":\n",
+    "                return \"timestamp\"\n",
+    "            if attr in {\"pct_change\", \"mean\", \"sum\", \"std\", \"median\", \"max\", \"min\", \"rolling\"}:\n",
+    "                return \"double\"\n",
+    "        return \"string\"\n",
+    "\n",
+    "    def _resolve_table_schema(self, expr: ast.AST, env: dict[str, dict[str, str]]) -> dict[str, str] | None:\n",
+    "        if isinstance(expr, ast.Name):\n",
+    "            schema = env.get(expr.id)\n",
+    "            return dict(schema) if schema is not None else None\n",
+    "        if isinstance(expr, ast.Subscript):\n",
+    "            base = self._resolve_table_schema(expr.value, env)\n",
+    "            cols = self._list_of_constant_strings(expr.slice)\n",
+    "            if base is not None and cols is not None:\n",
+    "                return {c: base[c] for c in cols if c in base}\n",
+    "            return None\n",
+    "        if isinstance(expr, ast.Call) and isinstance(expr.func, ast.Attribute):\n",
+    "            base = self._resolve_table_schema(expr.func.value, env)\n",
+    "            attr = expr.func.attr\n",
+    "            if base is None:\n",
+    "                return None\n",
+    "            if attr in {\"copy\", \"dropna\", \"sort_values\", \"reset_index\", \"rename_axis\", \"fillna\", \"head\", \"tail\"}:\n",
+    "                return dict(base)\n",
+    "            if attr == \"assign\":\n",
+    "                out = dict(base)\n",
+    "                for kw in expr.keywords:\n",
+    "                    if kw.arg:\n",
+    "                        out[kw.arg] = self._infer_scalar_type(kw.value, env)\n",
+    "                return out\n",
+    "            return dict(base)\n",
+    "        return None\n",
+    "\n",
+    "    def infer(self, fn: ast.FunctionDef, stage: StageInvocation, input_columns: list[dict[str, str]]) -> tuple[list[dict[str, str]], bool]:\n",
+    "        if stage.family != \"table\":\n",
+    "            return [], True\n",
+    "        env: dict[str, dict[str, str]] = {}\n",
+    "        if stage.data_inputs:\n",
+    "            if len(stage.data_inputs) == 1:\n",
+    "                env[stage.data_inputs[0].param_name] = self._schema_dict(input_columns)\n",
+    "            else:\n",
+    "                merged: dict[str, str] = {}\n",
+    "                for spec in stage.data_inputs:\n",
+    "                    for c in input_columns:\n",
+    "                        if c.get(\"attributeName\"):\n",
+    "                            merged[c[\"attributeName\"]] = c[\"attributeType\"]\n",
+    "                    env[spec.param_name] = dict(merged)\n",
+    "        elif stage.is_source and stage.source_columns:\n",
+    "            # Seed read_csv-assigned variables lazily below.\n",
+    "            pass\n",
+    "\n",
+    "        return_schema: dict[str, str] | None = None\n",
+    "        for stmt in fn.body:\n",
+    "            if isinstance(stmt, ast.Assign):\n",
+    "                value_schema = self._resolve_table_schema(stmt.value, env)\n",
+    "                if value_schema is not None:\n",
+    "                    for t in stmt.targets:\n",
+    "                        if isinstance(t, ast.Name):\n",
+    "                            env[t.id] = dict(value_schema)\n",
+    "                for t in stmt.targets:\n",
+    "                    if isinstance(t, ast.Subscript) and isinstance(t.value, ast.Name):\n",
+    "                        root = t.value.id\n",
+    "                        if root not in env:\n",
+    "                            continue\n",
+    "                        if isinstance(t.slice, ast.Constant) and isinstance(t.slice.value, str):\n",
+    "                            env[root][t.slice.value] = self._infer_scalar_type(stmt.value, env)\n",
+    "            elif isinstance(stmt, ast.Return) and stmt.value is not None:\n",
+    "                rs = self._resolve_table_schema(stmt.value, env)\n",
+    "                if rs is not None:\n",
+    "                    return_schema = rs\n",
+    "\n",
+    "        if return_schema is None:\n",
+    "            if stage.is_source:\n",
+    "                return_schema = self._schema_dict(stage.source_columns)\n",
+    "            elif len(stage.data_inputs) == 1:\n",
+    "                return_schema = self._schema_dict(input_columns)\n",
+    "            else:\n",
+    "                return_schema = {}\n",
+    "                for c in input_columns:\n",
+    "                    if c.get(\"attributeName\"):\n",
+    "                        return_schema[c[\"attributeName\"]] = c[\"attributeType\"]\n",
+    "\n",
+    "        input_schema_dict = self._schema_dict(input_columns)\n",
+    "        retain = return_schema == input_schema_dict\n",
+    "        # Emit explicit output columns for all table stages to keep runtime schema aligned.\n",
+    "        return self._schema_list(return_schema), False if return_schema else retain\n",
+    "\n",
+    "\n",
+    "class WorkflowIdFactory:\n",
+    "    def operator_id(self, operator_type: str = \"PythonUDFV2\") -> str:\n",
+    "        return f\"{operator_type}-operator-{uuid.uuid4()}\"\n",
+    "\n",
+    "    def link_id(self) -> str:\n",
+    "        return f\"link-{uuid.uuid4()}\"\n",
+    "\n",
+    "    def stage_id(self, base: str, idx: int) -> str:\n",
+    "        return f\"{base}__{idx}\"\n",
+    "\n",
+    "\n",
+    "class ConstantEnvBuilder:\n",
+    "    def build(self, entry_fn: ast.FunctionDef) -> dict[str, ast.AST]:\n",
+    "        env: dict[str, ast.AST] = {}\n",
+    "        for stmt in entry_fn.body:\n",
+    "            if isinstance(stmt, ast.Assign) and isinstance(stmt.value, ast.Constant):\n",
+    "                for t in stmt.targets:\n",
+    "                    if isinstance(t, ast.Name):\n",
+    "                        env[t.id] = copy.deepcopy(stmt.value)\n",
+    "        return env\n",
+    "\n",
+    "\n",
+    "class AttributeTypeInferer:\n",
+    "    def from_annotation(self, annotation: ast.AST | None) -> str:\n",
+    "        if annotation is None:\n",
+    "            return \"AttributeType.STRING\"\n",
+    "        text = ast.unparse(annotation)\n",
+    "        if text in (\"str\", \"Optional[str]\"):\n",
+    "            return \"AttributeType.STRING\"\n",
+    "        if text in (\"int\", \"Optional[int]\"):\n",
+    "            return \"AttributeType.INTEGER\"\n",
+    "        if text in (\"float\", \"Optional[float]\"):\n",
+    "            return \"AttributeType.DOUBLE\"\n",
+    "        if text in (\"bool\", \"Optional[bool]\"):\n",
+    "            return \"AttributeType.BOOLEAN\"\n",
+    "        return \"AttributeType.STRING\"\n",
+    "\n",
+    "    def from_value(self, node: ast.AST | None) -> str:\n",
+    "        if isinstance(node, ast.Constant):\n",
+    "            py_name = type(node.value).__name__\n",
+    "            return ATTRIBUTE_TYPE_FOR_PY.get(py_name, \"AttributeType.STRING\")\n",
+    "        return \"AttributeType.STRING\"\n",
+    "\n",
+    "\n",
+    "class EntryDagExtractor:\n",
+    "    def __init__(self):\n",
+    "        self.type_inferer = AttributeTypeInferer()\n",
+    "        self.ids = WorkflowIdFactory()\n",
+    "\n",
+    "    def _assigned_names(self, stmt: ast.stmt) -> list[str]:\n",
+    "        names: list[str] = []\n",
+    "        if isinstance(stmt, ast.Assign):\n",
+    "            for t in stmt.targets:\n",
+    "                if isinstance(t, ast.Name):\n",
+    "                    names.append(t.id)\n",
+    "                elif isinstance(t, (ast.Tuple, ast.List)):\n",
+    "                    for elt in t.elts:\n",
+    "                        if isinstance(elt, ast.Name):\n",
+    "                            names.append(elt.id)\n",
+    "        return names\n",
+    "\n",
+    "    def extract(\n",
+    "        self,\n",
+    "        entry_fn: ast.FunctionDef,\n",
+    "        functions: dict[str, ast.FunctionDef],\n",
+    "        facts: dict[str, FunctionFacts],\n",
+    "        constant_env: dict[str, ast.AST],\n",
+    "    ) -> list[StageInvocation]:\n",
+    "        stages: list[StageInvocation] = []\n",
+    "        producer_for_var: dict[str, str] = {}\n",
+    "        stage_index = 0\n",
+    "\n",
+    "        for stmt in entry_fn.body:\n",
+    "            call = None\n",
+    "            if isinstance(stmt, ast.Assign) and isinstance(stmt.value, ast.Call):\n",
+    "                call = stmt.value\n",
+    "            elif isinstance(stmt, ast.Expr) and isinstance(stmt.value, ast.Call):\n",
+    "                call = stmt.value\n",
+    "            if not call or not isinstance(call.func, ast.Name) or call.func.id not in functions or call.func.id == entry_fn.name:\n",
+    "                continue\n",
+    "\n",
+    "            fn_name = call.func.id\n",
+    "            fn = functions[fn_name]\n",
+    "            fact = facts[fn_name]\n",
+    "            assigned_to = self._assigned_names(stmt)\n",
+    "            data_inputs: list[DataInputSpec] = []\n",
+    "            ui_params: list[UiParamSpec] = []\n",
+    "\n",
+    "            for i, param in enumerate(fn.args.args):\n",
+    "                arg_node = None\n",
+    "                if i < len(call.args):\n",
+    "                    arg_node = call.args[i]\n",
+    "                else:\n",
+    "                    # support exact-name keyword arguments\n",
+    "                    for kw in call.keywords:\n",
+    "                        if kw.arg == param.arg:\n",
+    "                            arg_node = kw.value\n",
+    "                            break\n",
+    "\n",
+    "                if arg_node is None:\n",
+    "                    continue\n",
+    "\n",
+    "                if isinstance(arg_node, ast.Name) and arg_node.id in producer_for_var:\n",
+    "                    data_inputs.append(DataInputSpec(\n",
+    "                        param_name=param.arg,\n",
+    "                        upstream_stage_id=producer_for_var[arg_node.id],\n",
+    "                        upstream_var=arg_node.id,\n",
+    "                        input_port=len(data_inputs),\n",
+    "                    ))\n",
+    "                else:\n",
+    "                    default_ast = copy.deepcopy(constant_env[arg_node.id]) if isinstance(arg_node, ast.Name) and arg_node.id in constant_env else copy.deepcopy(arg_node)\n",
+    "                    attr_type = self.type_inferer.from_value(default_ast)\n",
+    "                    if attr_type == \"AttributeType.STRING\":\n",
+    "                        attr_type = self.type_inferer.from_annotation(param.annotation) or attr_type\n",
+    "                    ui_params.append(UiParamSpec(name=param.arg, attr_type=attr_type, default_ast=default_ast))\n",
+    "\n",
+    "            is_source = len(data_inputs) == 0\n",
+    "            stage = StageInvocation(\n",
+    "                stage_id=self.ids.stage_id(fn_name, stage_index),\n",
+    "                function_name=fn_name,\n",
+    "                family=fact.family,\n",
+    "                is_source=is_source,\n",
+    "                is_sink=fact.is_sink,\n",
+    "                recursive=fact.recursive,\n",
+    "                has_loop=fact.has_loop,\n",
+    "                assigned_to=assigned_to,\n",
+    "                data_inputs=data_inputs,\n",
+    "                ui_params=ui_params,\n",
+    "            )\n",
+    "            stages.append(stage)\n",
+    "            for name in assigned_to:\n",
+    "                producer_for_var[name] = stage.stage_id\n",
+    "            stage_index += 1\n",
+    "\n",
+    "        return stages\n",
+    "\n",
+    "\n",
+    "class ParamRewriter(ast.NodeTransformer):\n",
+    "    def __init__(self, name_mapping: dict[str, ast.AST]):\n",
+    "        self.name_mapping = name_mapping\n",
+    "\n",
+    "    def visit_Name(self, node: ast.Name):\n",
+    "        if isinstance(node.ctx, ast.Load) and node.id in self.name_mapping:\n",
+    "            return ast.copy_location(copy.deepcopy(self.name_mapping[node.id]), node)\n",
+    "        return node\n",
+    "\n",
+    "\n",
+    "class YieldTransformer:\n",
+    "    def __init__(self, family: str, is_source: bool, is_sink: bool, passthrough_name: str):\n",
+    "        self.family = family\n",
+    "        self.is_source = is_source\n",
+    "        self.is_sink = is_sink\n",
+    "        self.passthrough_name = passthrough_name\n",
+    "\n",
+    "    def _yield_stmt(self, value: ast.AST | None) -> ast.stmt:\n",
+    "        return ast.Expr(value=ast.Yield(value=value))\n",
+    "\n",
+    "    def transform_block(self, body: list[ast.stmt], top_level: bool = False) -> list[ast.stmt]:\n",
+    "        new_body: list[ast.stmt] = []\n",
+    "        for stmt in body:\n",
+    "            if isinstance(stmt, ast.Return):\n",
+    "                if stmt.value is not None:\n",
+    "                    new_body.append(self._yield_stmt(stmt.value))\n",
+    "                elif top_level and (not self.is_source) and self.is_sink:\n",
+    "                    new_body.append(self._yield_stmt(ast.Name(id=self.passthrough_name, ctx=ast.Load())))\n",
+    "                new_body.append(ast.Return(value=None))\n",
+    "            elif isinstance(stmt, ast.If):\n",
+    "                stmt.body = self.transform_block(stmt.body, top_level=False)\n",
+    "                stmt.orelse = self.transform_block(stmt.orelse, top_level=False)\n",
+    "                new_body.append(stmt)\n",
+    "            elif isinstance(stmt, ast.For):\n",
+    "                stmt.body = self.transform_block(stmt.body, top_level=False)\n",
+    "                stmt.orelse = self.transform_block(stmt.orelse, top_level=False)\n",
+    "                new_body.append(stmt)\n",
+    "            elif isinstance(stmt, ast.While):\n",
+    "                stmt.body = self.transform_block(stmt.body, top_level=False)\n",
+    "                stmt.orelse = self.transform_block(stmt.orelse, top_level=False)\n",
+    "                new_body.append(stmt)\n",
+    "            else:\n",
+    "                new_body.append(stmt)\n",
+    "\n",
+    "        has_yield = any(isinstance(n, ast.Yield) for n in ast.walk(ast.Module(body=new_body, type_ignores=[])))\n",
+    "        if top_level and not has_yield and not self.is_source:\n",
+    "            new_body.append(self._yield_stmt(ast.Name(id=self.passthrough_name, ctx=ast.Load())))\n",
+    "        return new_body\n",
+    "\n",
+    "\n",
+    "class UdfBaseSelector:\n",
+    "    def select(self, family: str, is_source: bool) -> tuple[str, str, str]:\n",
+    "        if is_source:\n",
+    "            return \"ProcessSourceOperator\", \"UDFSourceOperator\", \"produce\"\n",
+    "        if family == \"tuple\":\n",
+    "            return \"ProcessTupleOperator\", \"UDFOperatorV2\", \"process_tuple\"\n",
+    "        if family == \"batch\":\n",
+    "            return \"ProcessBatchOperator\", \"UDFBatchOperator\", \"process_batch\"\n",
+    "        return \"ProcessTableOperator\", \"UDFTableOperator\", \"process_table\"\n",
+    "\n",
+    "    def workflow_operator_type(self, family: str, is_source: bool, input_count: int) -> str:\n",
+    "        \"\"\"\n",
+    "        Workflow JSON only uses the Python operator types that are known to exist\n",
+    "        in the target Texera deployment:\n",
+    "          - PythonUDFSourceV2 for 0-input source operators\n",
+    "          - PythonUDFV2 for regular 1-input Python UDFs\n",
+    "          - DualInputPortsPythonUDFV2 for 2-input Python UDFs\n",
+    "\n",
+    "        The implementation class inside `code` may still be UDFOperatorV2,\n",
+    "        UDFBatchOperator, UDFTableOperator, or UDFSourceOperator.\n",
+    "        \"\"\"\n",
+    "        if is_source:\n",
+    "            return \"PythonUDFSourceV2\"\n",
+    "        if input_count == 2:\n",
+    "            return \"DualInputPortsPythonUDFV2\"\n",
+    "        return \"PythonUDFV2\"\n",
+    "\n",
+    "    def data_variable(self, family: str) -> str:\n",
+    "        return {\"tuple\": \"tuple_\", \"batch\": \"batch\"}.get(family, \"table\")\n",
+    "\n",
+    "    def process_signature(self, family: str, is_source: bool) -> tuple[list[ast.arg], Optional[ast.AST]]:\n",
+    "        if is_source:\n",
+    "            return (\n",
+    "                [ast.arg(arg=\"self\")],\n",
+    "                ast.Subscript(\n",
+    "                    value=ast.Name(id=\"Iterator\", ctx=ast.Load()),\n",
+    "                    slice=ast.Subscript(\n",
+    "                        value=ast.Name(id=\"Optional\", ctx=ast.Load()),\n",
+    "                        slice=ast.Subscript(\n",
+    "                            value=ast.Name(id=\"Union\", ctx=ast.Load()),\n",
+    "                            slice=ast.Tuple(elts=[ast.Name(id=\"TupleLike\", ctx=ast.Load()), ast.Name(id=\"TableLike\", ctx=ast.Load())], ctx=ast.Load()),\n",
+    "                            ctx=ast.Load(),\n",
+    "                        ),\n",
+    "                        ctx=ast.Load(),\n",
+    "                    ),\n",
+    "                    ctx=ast.Load(),\n",
+    "                ),\n",
+    "            )\n",
+    "        if family == \"tuple\":\n",
+    "            return (\n",
+    "                [ast.arg(arg=\"self\"), ast.arg(arg=\"tuple_\", annotation=ast.Name(id=\"Tuple\", ctx=ast.Load())), ast.arg(arg=\"port\", annotation=ast.Name(id=\"int\", ctx=ast.Load()))],\n",
+    "                ast.Subscript(value=ast.Name(id=\"Iterator\", ctx=ast.Load()), slice=ast.Subscript(value=ast.Name(id=\"Optional\", ctx=ast.Load()), slice=ast.Name(id=\"TupleLike\", ctx=ast.Load()), ctx=ast.Load()), ctx=ast.Load()),\n",
+    "            )\n",
+    "        if family == \"batch\":\n",
+    "            return (\n",
+    "                [ast.arg(arg=\"self\"), ast.arg(arg=\"batch\", annotation=ast.Name(id=\"Batch\", ctx=ast.Load())), ast.arg(arg=\"port\", annotation=ast.Name(id=\"int\", ctx=ast.Load()))],\n",
+    "                ast.Subscript(value=ast.Name(id=\"Iterator\", ctx=ast.Load()), slice=ast.Subscript(value=ast.Name(id=\"Optional\", ctx=ast.Load()), slice=ast.Name(id=\"BatchLike\", ctx=ast.Load()), ctx=ast.Load()), ctx=ast.Load()),\n",
+    "            )\n",
+    "        return (\n",
+    "            [ast.arg(arg=\"self\"), ast.arg(arg=\"table\", annotation=ast.Name(id=\"Table\", ctx=ast.Load())), ast.arg(arg=\"port\", annotation=ast.Name(id=\"int\", ctx=ast.Load()))],\n",
+    "            ast.Subscript(value=ast.Name(id=\"Iterator\", ctx=ast.Load()), slice=ast.Subscript(value=ast.Name(id=\"Optional\", ctx=ast.Load()), slice=ast.Name(id=\"TableLike\", ctx=ast.Load()), ctx=ast.Load()), ctx=ast.Load()),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "class UiParameterEmitter:\n",
+    "    def statements(self, spec: UiParamSpec) -> list[ast.stmt]:\n",
+    "        stmts: list[ast.stmt] = []\n",
+    "        if spec.default_ast is not None:\n",
+    "            stmts.append(\n",
+    "                ast.Assign(\n",
+    "                    targets=[ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=spec.name, ctx=ast.Store())],\n",
+    "                    value=copy.deepcopy(spec.default_ast),\n",
+    "                )\n",
+    "            )\n",
+    "        else:\n",
+    "            stmts.append(\n",
+    "                ast.Assign(\n",
+    "                    targets=[ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=spec.name, ctx=ast.Store())],\n",
+    "                    value=ast.Constant(value=None),\n",
+    "                )\n",
+    "            )\n",
+    "        return stmts\n",
+    "\n",
+    "    def commented_line(self, spec: UiParamSpec) -> str:\n",
+    "        attr_suffix = spec.attr_type.split(\".\")[-1]\n",
+    "        return f\"# self.UiParameter({spec.name!r}, AttributeType.{attr_suffix})  # UiParameter disabled for now\"\n",
+    "\n",
+    "    def hardcoded_comment_line(self, spec: UiParamSpec) -> str:\n",
+    "        return f\"# Temporary hardcoded default for UI parameter '{spec.name}' from the source script.\"\n",
+    "\n",
+    "\n",
+    "class CodeCommentInjector:\n",
+    "    def inject_ui_parameter_comments(self, code: str, stage: StageInvocation, ui_emitter: UiParameterEmitter) -> str:\n",
+    "        if not stage.ui_params:\n",
+    "            return code\n",
+    "        lines = code.splitlines()\n",
+    "        out: list[str] = []\n",
+    "        specs_by_line = {\n",
+    "            f\"self.{spec.name} = {ast.unparse(spec.default_ast) if spec.default_ast is not None else 'None'}\": spec\n",
+    "            for spec in stage.ui_params\n",
+    "        }\n",
+    "        for line in lines:\n",
+    "            out.append(line)\n",
+    "            stripped = line.strip()\n",
+    "            spec = specs_by_line.get(stripped)\n",
+    "            if spec is not None:\n",
+    "                indent = line[: len(line) - len(line.lstrip())]\n",
+    "                out.append(indent + ui_emitter.hardcoded_comment_line(spec))\n",
+    "                out.append(indent + ui_emitter.commented_line(spec))\n",
+    "        return \"\\n\".join(out) + \"\\n\"\n",
+    "\n",
+    "\n",
+    "class FunctionBodyCompiler:\n",
+    "    def __init__(self):\n",
+    "        self.base_selector = UdfBaseSelector()\n",
+    "        self.ui_emitter = UiParameterEmitter()\n",
+    "        self.comment_injector = CodeCommentInjector()\n",
+    "\n",
+    "    def _param_mapping(self, stage: StageInvocation) -> dict[str, ast.AST]:\n",
+    "        mapping: dict[str, ast.AST] = {}\n",
+    "        for spec in stage.ui_params:\n",
+    "            mapping[spec.name] = ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=spec.name, ctx=ast.Load())\n",
+    "        if stage.is_source:\n",
+    "            return mapping\n",
+    "        if len(stage.data_inputs) == 1:\n",
+    "            only = stage.data_inputs[0]\n",
+    "            mapping[only.param_name] = ast.Name(id=self.base_selector.data_variable(stage.family), ctx=ast.Load())\n",
+    "        elif len(stage.data_inputs) > 1:\n",
+    "            for spec in stage.data_inputs:\n",
+    "                mapping[spec.param_name] = ast.Name(id=f\"input_{spec.input_port}\", ctx=ast.Load())\n",
+    "        return mapping\n",
+    "\n",
+    "    def _prep_statements(self, stage: StageInvocation) -> list[ast.stmt]:\n",
+    "        stmts: list[ast.stmt] = []\n",
+    "        if len(stage.data_inputs) > 1:\n",
+    "            data_var = self.base_selector.data_variable(stage.family)\n",
+    "            stmts.append(ast.Assign(\n",
+    "                targets=[ast.Subscript(value=ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=\"_texera_inputs\", ctx=ast.Load()), slice=ast.Name(id=\"port\", ctx=ast.Load()), ctx=ast.Store())],\n",
+    "                value=ast.Name(id=data_var, ctx=ast.Load()),\n",
+    "            ))\n",
+    "            cond = ast.Compare(\n",
+    "                left=ast.Call(func=ast.Name(id=\"len\", ctx=ast.Load()), args=[ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=\"_texera_inputs\", ctx=ast.Load())], keywords=[]),\n",
+    "                ops=[ast.Lt()],\n",
+    "                comparators=[ast.Constant(value=len(stage.data_inputs))],\n",
+    "            )\n",
+    "            stmts.append(ast.If(test=cond, body=[ast.Return(value=None)], orelse=[]))\n",
+    "            for spec in stage.data_inputs:\n",
+    "                stmts.append(ast.Assign(\n",
+    "                    targets=[ast.Name(id=f\"input_{spec.input_port}\", ctx=ast.Store())],\n",
+    "                    value=ast.Subscript(value=ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=\"_texera_inputs\", ctx=ast.Load()), slice=ast.Constant(value=spec.input_port), ctx=ast.Load()),\n",
+    "                ))\n",
+    "        return stmts\n",
+    "\n",
+    "    def compile_body(self, fn: ast.FunctionDef, stage: StageInvocation) -> list[ast.stmt]:\n",
+    "        fn_copy = copy.deepcopy(fn)\n",
+    "        mapping = self._param_mapping(stage)\n",
+    "        fn_copy = ParamRewriter(mapping).visit(fn_copy)\n",
+    "        ast.fix_missing_locations(fn_copy)\n",
+    "\n",
+    "        body: list[ast.stmt] = self._prep_statements(stage)\n",
+    "        for stmt in fn_copy.body:\n",
+    "            if isinstance(stmt, ast.Expr) and isinstance(stmt.value, ast.Constant) and isinstance(stmt.value.value, str):\n",
+    "                continue\n",
+    "            body.append(stmt)\n",
+    "        body = YieldTransformer(\n",
+    "            family=stage.family,\n",
+    "            is_source=stage.is_source,\n",
+    "            is_sink=stage.is_sink,\n",
+    "            passthrough_name=self.base_selector.data_variable(stage.family),\n",
+    "        ).transform_block(body, top_level=True)\n",
+    "        if not body:\n",
+    "            body = [ast.Pass()]\n",
+    "        return body\n",
+    "\n",
+    "    def emit_class(self, import_nodes: list[ast.stmt], fn: ast.FunctionDef, stage: StageInvocation) -> str:\n",
+    "        class_name, base_name, method_name = self.base_selector.select(stage.family, stage.is_source)\n",
+    "        args_list, returns_ann = self.base_selector.process_signature(stage.family, stage.is_source)\n",
+    "        body = self.compile_body(fn, stage)\n",
+    "\n",
+    "        open_body: list[ast.stmt] = []\n",
+    "        for spec in stage.ui_params:\n",
+    "            open_body.extend(self.ui_emitter.statements(spec))\n",
+    "        if len(stage.data_inputs) > 1:\n",
+    "            open_body.append(ast.Assign(\n",
+    "                targets=[ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=\"_texera_inputs\", ctx=ast.Store())],\n",
+    "                value=ast.Dict(keys=[], values=[]),\n",
+    "            ))\n",
+    "        if not open_body:\n",
+    "            open_body = [ast.Pass()]\n",
+    "\n",
+    "        class_body: list[ast.stmt] = []\n",
+    "        if stage.recursive:\n",
+    "            class_body.append(ast.Expr(value=ast.Constant(value=f\"TRANSLATOR NOTE: {stage.function_name} is recursive.\")))\n",
+    "        if stage.has_loop:\n",
+    "            class_body.append(ast.Expr(value=ast.Constant(value=f\"TRANSLATOR NOTE: {stage.function_name} contains loops.\")))\n",
+    "\n",
+    "        class_body.append(ast.FunctionDef(\n",
+    "            name=\"open\",\n",
+    "            args=ast.arguments(posonlyargs=[], args=[ast.arg(arg=\"self\")], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]),\n",
+    "            body=open_body,\n",
+    "            decorator_list=[],\n",
+    "            returns=ast.Name(id=\"None\", ctx=ast.Load()),\n",
+    "            type_comment=None,\n",
+    "        ))\n",
+    "        class_body.append(ast.FunctionDef(\n",
+    "            name=method_name,\n",
+    "            args=ast.arguments(posonlyargs=[], args=args_list, vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]),\n",
+    "            body=body,\n",
+    "            decorator_list=[ast.Name(id=\"overrides\", ctx=ast.Load())],\n",
+    "            returns=returns_ann,\n",
+    "            type_comment=None,\n",
+    "        ))\n",
+    "        class_body.append(ast.FunctionDef(\n",
+    "            name=\"close\",\n",
+    "            args=ast.arguments(posonlyargs=[], args=[ast.arg(arg=\"self\")], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]),\n",
+    "            body=[ast.Pass()],\n",
+    "            decorator_list=[],\n",
+    "            returns=ast.Name(id=\"None\", ctx=ast.Load()),\n",
+    "            type_comment=None,\n",
+    "        ))\n",
+    "        cls = ast.ClassDef(name=class_name, bases=[ast.Name(id=base_name, ctx=ast.Load())], keywords=[], body=class_body, decorator_list=[])\n",
+    "        ast.fix_missing_locations(cls)\n",
+    "\n",
+    "        prelude = [\n",
+    "            \"from typing import Iterator, Optional, Union\",\n",
+    "            *[ast.unparse(n) for n in import_nodes],\n",
+    "            \"from pytexera import *\",\n",
+    "            \"from core.models.schema.attribute_type import AttributeType\",\n",
+    "            \"\",\n",
+    "        ]\n",
+    "        module = ast.Module(body=[cls], type_ignores=[])\n",
+    "        ast.fix_missing_locations(module)\n",
+    "        rendered = \"\\n\".join(prelude) + ast.unparse(module) + \"\\n\"\n",
+    "        return self.comment_injector.inject_ui_parameter_comments(rendered, stage, self.ui_emitter)\n",
+    "\n",
+    "\n",
+    "class WorkflowOperatorBuilder:\n",
+    "    def _operator_properties(self, stage: StageInvocation) -> dict[str, Any]:\n",
+    "        props: dict[str, Any] = {\n",
+    "            \"code\": stage.code,\n",
+    "            \"workers\": 1,\n",
+    "        }\n",
+    "        if stage.operator_type == \"PythonUDFSourceV2\":\n",
+    "            if stage.source_columns:\n",
+    "                props[\"columns\"] = stage.source_columns\n",
+    "        else:\n",
+    "            props[\"retainInputColumns\"] = stage.retain_input_columns\n",
+    "            if stage.output_columns:\n",
+    "                props[\"outputColumns\"] = stage.output_columns\n",
+    "        return props\n",
+    "\n",
+    "    def _input_ports(self, stage: StageInvocation) -> list[dict[str, Any]]:\n",
+    "        if stage.is_source:\n",
+    "            return []\n",
+    "        count = max(1, len(stage.data_inputs))\n",
+    "        ports = []\n",
+    "        for i in range(count):\n",
+    "            display_name = \"\"\n",
+    "            if count > 1 and i < len(stage.data_inputs):\n",
+    "                display_name = stage.data_inputs[i].param_name\n",
+    "            dependencies: list[dict[str, Any]] = []\n",
+    "            if count > 1 and i > 0:\n",
+    "                dependencies = [{\"id\": i - 1, \"internal\": False}]\n",
+    "            ports.append({\n",
+    "                \"portID\": f\"input-{i}\",\n",
+    "                \"displayName\": display_name,\n",
+    "                \"allowMultiInputs\": True,\n",
+    "                \"isDynamicPort\": False,\n",
+    "                \"dependencies\": dependencies,\n",
+    "            })\n",
+    "        return ports\n",
+    "\n",
+    "    def _dynamic_ports_flags(self, stage: StageInvocation) -> tuple[bool, bool]:\n",
+    "        if stage.is_source:\n",
+    "            return False, False\n",
+    "        if stage.operator_type == \"DualInputPortsPythonUDFV2\":\n",
+    "            return False, False\n",
+    "        return True, True\n",
+    "\n",
+    "    def build(self, stage: StageInvocation, view_result: bool = False) -> dict[str, Any]:\n",
+    "        dynamic_input_ports, dynamic_output_ports = self._dynamic_ports_flags(stage)\n",
+    "        operator = {\n",
+    "            \"operatorID\": stage.operator_id,\n",
+    "            \"operatorType\": stage.operator_type,\n",
+    "            \"operatorVersion\": \"N/A\",\n",
+    "            \"operatorProperties\": self._operator_properties(stage),\n",
+    "            \"inputPorts\": self._input_ports(stage),\n",
+    "            \"outputPorts\": [\n",
+    "                {\n",
+    "                    \"portID\": \"output-0\",\n",
+    "                    \"displayName\": \"\",\n",
+    "                    \"allowMultiInputs\": False,\n",
+    "                    \"isDynamicPort\": False,\n",
+    "                }\n",
+    "            ],\n",
+    "            \"showAdvanced\": False,\n",
+    "            \"isDisabled\": False,\n",
+    "            \"customDisplayName\": stage.display_name,\n",
+    "            \"dynamicInputPorts\": dynamic_input_ports,\n",
+    "            \"dynamicOutputPorts\": dynamic_output_ports,\n",
+    "        }\n",
+    "        if view_result:\n",
+    "            operator[\"viewResult\"] = True\n",
+    "        return operator\n",
+    "\n",
+    "\n",
+    "class WorkflowLinkBuilder:\n",
+    "    def __init__(self):\n",
+    "        self.ids = WorkflowIdFactory()\n",
+    "\n",
+    "    def build_links(self, stages: list[StageInvocation]) -> list[dict[str, Any]]:\n",
+    "        stage_by_id = {s.stage_id: s for s in stages}\n",
+    "        links: list[dict[str, Any]] = []\n",
+    "        for stage in stages:\n",
+    "            for inp in stage.data_inputs:\n",
+    "                upstream = stage_by_id[inp.upstream_stage_id]\n",
+    "                links.append({\n",
+    "                    \"linkID\": self.ids.link_id(),\n",
+    "                    \"source\": {\"operatorID\": upstream.operator_id, \"portID\": \"output-0\"},\n",
+    "                    \"target\": {\"operatorID\": stage.operator_id, \"portID\": f\"input-{inp.input_port}\"},\n",
+    "                })\n",
+    "        return links\n",
+    "\n",
+    "\n",
+    "class WorkflowLayoutBuilder:\n",
+    "    def build_positions(self, stages: list[StageInvocation], start_x: int = 240, start_y: int = 180, x_gap: int = 300, y_gap: int = 180) -> dict[str, dict[str, int]]:\n",
+    "        preds: dict[str, list[str]] = {s.stage_id: [inp.upstream_stage_id for inp in s.data_inputs] for s in stages}\n",
+    "        layers: dict[str, int] = {}\n",
+    "        remaining = {s.stage_id for s in stages}\n",
+    "        while remaining:\n",
+    "            progressed = False\n",
+    "            for sid in list(remaining):\n",
+    "                if all(p in layers for p in preds[sid]):\n",
+    "                    layers[sid] = 0 if not preds[sid] else max(layers[p] for p in preds[sid]) + 1\n",
+    "                    remaining.remove(sid)\n",
+    "                    progressed = True\n",
+    "            if not progressed:\n",
+    "                for sid in list(remaining):\n",
+    "                    layers[sid] = 0\n",
+    "                    remaining.remove(sid)\n",
+    "        by_layer: dict[int, list[StageInvocation]] = {}\n",
+    "        for s in stages:\n",
+    "            by_layer.setdefault(layers[s.stage_id], []).append(s)\n",
+    "        positions: dict[str, dict[str, int]] = {}\n",
+    "        for layer, items in sorted(by_layer.items()):\n",
+    "            for idx, stage in enumerate(items):\n",
+    "                positions[stage.operator_id] = {\"x\": start_x + layer * x_gap, \"y\": start_y + idx * y_gap}\n",
+    "        return positions\n",
+    "\n",
+    "\n",
+    "class WorkflowJsonAssembler:\n",
+    "    def __init__(self):\n",
+    "        self.operator_builder = WorkflowOperatorBuilder()\n",
+    "        self.link_builder = WorkflowLinkBuilder()\n",
+    "        self.layout_builder = WorkflowLayoutBuilder()\n",
+    "\n",
+    "    def assemble(self, stages: list[StageInvocation]) -> dict[str, Any]:\n",
+    "        operators = [\n",
+    "            self.operator_builder.build(stage, view_result=(i == len(stages) - 1))\n",
+    "            for i, stage in enumerate(stages)\n",
+    "        ]\n",
+    "        return {\n",
+    "            \"operators\": operators,\n",
+    "            \"operatorPositions\": self.layout_builder.build_positions(stages),\n",
+    "            \"links\": self.link_builder.build_links(stages),\n",
+    "            \"commentBoxes\": [],\n",
+    "            \"settings\": {\"dataTransferBatchSize\": 400},\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "class WorkflowTranslator:\n",
+    "    def __init__(self):\n",
+    "        self.parser = SourceModuleParser()\n",
+    "        self.imports = ImportCollector()\n",
+    "        self.functions = FunctionIndex()\n",
+    "        self.call_graph = CallGraphBuilder()\n",
+    "        self.recursion = RecursiveAnalyzer()\n",
+    "        self.facts = FunctionFactsAnalyzer()\n",
+    "        self.constant_env = ConstantEnvBuilder()\n",
+    "        self.dag_extractor = EntryDagExtractor()\n",
+    "        self.ids = WorkflowIdFactory()\n",
+    "        self.code_emitter = FunctionBodyCompiler()\n",
+    "        self.workflow = WorkflowJsonAssembler()\n",
+    "        self.schema_inferer = SourceSchemaInferer()\n",
+    "        self.table_schema_inferer = TableOutputSchemaInferer()\n",
+    "\n",
+    "    def translate(self, source: str, entry: str = \"main\") -> dict[str, Any]:\n",
+    "        tree = self.parser.parse(source)\n",
+    "        import_nodes = self.imports.collect_nodes(tree)\n",
+    "        functions = self.functions.collect(tree)\n",
+    "        if entry not in functions:\n",
+    "            raise ValueError(f\"Entry function {entry!r} not found. Available: {sorted(functions)}\")\n",
+    "\n",
+    "        graph = self.call_graph.build(functions)\n",
+    "        recursive = self.recursion.detect(graph)\n",
+    "        facts = self.facts.analyze(tree, functions, graph, recursive)\n",
+    "        const_env = self.constant_env.build(functions[entry])\n",
+    "        stages = self.dag_extractor.extract(functions[entry], functions, facts, const_env)\n",
+    "        if not stages:\n",
+    "            raise ValueError(f\"Entry function {entry!r} does not contain a detectable pipeline of helper calls.\")\n",
+    "\n",
+    "        stage_by_id: dict[str, StageInvocation] = {s.stage_id: s for s in stages}\n",
+    "        for stage in stages:\n",
+    "            fn = functions[stage.function_name]\n",
+    "            if len(stage.data_inputs) > 2:\n",
+    "                raise ValueError(f\"Current workflow JSON emitter supports at most 2 data inputs per stage, got {len(stage.data_inputs)} for {stage.function_name!r}.\")\n",
+    "            stage.operator_type = UdfBaseSelector().workflow_operator_type(stage.family, stage.is_source, len(stage.data_inputs))\n",
+    "            stage.operator_id = self.ids.operator_id(stage.operator_type)\n",
+    "            stage.display_name = \" \".join(part.capitalize() for part in stage.function_name.split(\"_\"))\n",
+    "            stage.source_columns = self.schema_inferer.infer(fn, stage)\n",
+    "            if stage.operator_type == \"PythonUDFSourceV2\" and stage.family == \"table\" and not stage.source_columns:\n",
+    "                raise ValueError(\n",
+    "                    f\"Could not infer output columns for source stage {stage.function_name!r}. \"\n",
+    "                    \"Make the source schema explicit or use a source that can be sampled at translation time.\"\n",
+    "                )\n",
+    "            if stage.family == \"table\":\n",
+    "                input_columns: list[dict[str, str]] = []\n",
+    "                for di in stage.data_inputs:\n",
+    "                    upstream = stage_by_id[di.upstream_stage_id]\n",
+    "                    input_columns.extend(upstream.output_columns or upstream.source_columns)\n",
+    "                if stage.is_source:\n",
+    "                    stage.output_columns = list(stage.source_columns)\n",
+    "                    stage.retain_input_columns = False\n",
+    "                else:\n",
+    "                    stage.output_columns, stage.retain_input_columns = self.table_schema_inferer.infer(fn, stage, input_columns)\n",
+    "            stage.code = self.code_emitter.emit_class(import_nodes, fn, stage)\n",
+    "\n",
+    "        workflow_json = self.workflow.assemble(stages)\n",
+    "        metadata = {\n",
+    "            \"entry\": entry,\n",
+    "            \"stages\": [\n",
+    "                {\n",
+    "                    \"stage_id\": s.stage_id,\n",
+    "                    \"function\": s.function_name,\n",
+    "                    \"operatorID\": s.operator_id,\n",
+    "                    \"operator_type\": s.operator_type,\n",
+    "                    \"family\": s.family,\n",
+    "                    \"base_class\": UdfBaseSelector().select(s.family, s.is_source)[1],\n",
+    "                    \"method\": UdfBaseSelector().select(s.family, s.is_source)[2],\n",
+    "                    \"is_source\": s.is_source,\n",
+    "                    \"is_sink\": s.is_sink,\n",
+    "                    \"recursive\": s.recursive,\n",
+    "                    \"has_loop\": s.has_loop,\n",
+    "                    \"assigned_to\": s.assigned_to,\n",
+    "                    \"data_inputs\": [\n",
+    "                        {\n",
+    "                            \"param_name\": di.param_name,\n",
+    "                            \"upstream_stage_id\": di.upstream_stage_id,\n",
+    "                            \"upstream_var\": di.upstream_var,\n",
+    "                            \"input_port\": di.input_port,\n",
+    "                        }\n",
+    "                        for di in s.data_inputs\n",
+    "                    ],\n",
+    "                    \"ui_parameters\": [\n",
+    "                        {\n",
+    "                            \"name\": p.name,\n",
+    "                            \"attr_type\": p.attr_type,\n",
+    "                            \"default\": ast.unparse(p.default_ast) if p.default_ast is not None else None,\n",
+    "                        }\n",
+    "                        for p in s.ui_params\n",
+    "                    ],\n",
+    "                    \"source_columns\": s.source_columns,\n",
+    "                    \"output_columns\": s.output_columns,\n",
+    "                    \"retain_input_columns\": s.retain_input_columns,\n",
+    "                }\n",
+    "                for s in stages\n",
+    "            ],\n",
+    "        }\n",
+    "        return {\"workflow_json\": workflow_json, \"metadata\": metadata}\n",
+    "\n",
+    "\n",
+    "def translate_to_texera_workflow_json(source: str, entry: str = \"main\") -> dict[str, Any]:\n",
+    "    return WorkflowTranslator().translate(source, entry=entry)\n",
+    "\n",
+    "\n",
+    "def write_translation(result: dict[str, Any], out_prefix: str | Path) -> dict[str, str]:\n",
+    "    out_prefix = Path(out_prefix)\n",
+    "    base = out_prefix.with_suffix(\"\")\n",
+    "    workflow_path = base.parent / f\"{base.name}.workflow.json\"\n",
+    "    meta_path = base.parent / f\"{base.name}.workflow.meta.json\"\n",
+    "    workflow_path.write_text(json.dumps(result[\"workflow_json\"], indent=2) + \"\\n\", encoding=\"utf-8\")\n",
+    "    meta_path.write_text(json.dumps(result[\"metadata\"], indent=2) + \"\\n\", encoding=\"utf-8\")\n",
+    "    return {\"workflow_json\": str(workflow_path), \"workflow_metadata\": str(meta_path)}\n",
+    "\n",
+    "# =============================\n",
+    "# Native-operator knowledge base\n",
+    "# =============================\n",
+    "\n",
+    "@dataclass\n",
+    "class NativeOperatorMatch:\n",
+    "    operator_type: str\n",
+    "    properties: dict[str, Any]\n",
+    "    display_name: str | None = None\n",
+    "    confidence: float = 1.0\n",
+    "    output_columns: list[dict[str, str]] | None = None\n",
+    "    retain_input_columns: bool | None = None\n",
+    "    notes: list[str] = field(default_factory=list)\n",
+    "\n",
+    "\n",
+    "class NativeOperatorCatalog:\n",
+    "    def __init__(self, catalog_json_path: str | Path | None = None):\n",
+    "        self.catalog_json_path = Path(catalog_json_path) if catalog_json_path else None\n",
+    "        self.templates: dict[str, dict[str, Any]] = {}\n",
+    "        self._load_default_if_available()\n",
+    "\n",
+    "    def _load_default_if_available(self):\n",
+    "        if self.catalog_json_path is None:\n",
+    "            default = Path('/mnt/data/Untitled workflow (11).json')\n",
+    "            if default.exists():\n",
+    "                self.catalog_json_path = default\n",
+    "        if self.catalog_json_path and self.catalog_json_path.exists():\n",
+    "            self.load(self.catalog_json_path)\n",
+    "\n",
+    "    def load(self, path: str | Path):\n",
+    "        path = Path(path)\n",
+    "        obj = json.loads(path.read_text(encoding='utf-8'))\n",
+    "        templates: dict[str, dict[str, Any]] = {}\n",
+    "        for op in obj.get('operators', []):\n",
+    "            op_type = op.get('operatorType')\n",
+    "            if op_type and op_type not in templates:\n",
+    "                templates[op_type] = copy.deepcopy(op)\n",
+    "        self.templates = templates\n",
+    "\n",
+    "    def has(self, operator_type: str) -> bool:\n",
+    "        return operator_type in self.templates\n",
+    "\n",
+    "    def template(self, operator_type: str) -> dict[str, Any]:\n",
+    "        if operator_type not in self.templates:\n",
+    "            raise KeyError(f'Operator type {operator_type!r} not found in native operator catalog')\n",
+    "        return copy.deepcopy(self.templates[operator_type])\n",
+    "\n",
+    "\n",
+    "class ImportAliasIndex:\n",
+    "    def build(self, tree: ast.Module) -> dict[str, str]:\n",
+    "        aliases: dict[str, str] = {}\n",
+    "        for node in tree.body:\n",
+    "            if isinstance(node, ast.Import):\n",
+    "                for alias in node.names:\n",
+    "                    aliases[alias.asname or alias.name.split('.')[0]] = alias.name\n",
+    "            elif isinstance(node, ast.ImportFrom):\n",
+    "                mod = node.module or ''\n",
+    "                for alias in node.names:\n",
+    "                    aliases[alias.asname or alias.name] = f\"{mod}.{alias.name}\" if mod else alias.name\n",
+    "        return aliases\n",
+    "\n",
+    "\n",
+    "class QualifiedNameResolver:\n",
+    "    def __init__(self, aliases: dict[str, str]):\n",
+    "        self.aliases = aliases\n",
+    "\n",
+    "    def resolve(self, node: ast.AST | None) -> str | None:\n",
+    "        if node is None:\n",
+    "            return None\n",
+    "        if isinstance(node, ast.Name):\n",
+    "            return self.aliases.get(node.id, node.id)\n",
+    "        if isinstance(node, ast.Attribute):\n",
+    "            base = self.resolve(node.value)\n",
+    "            if base is None:\n",
+    "                return None\n",
+    "            return f\"{base}.{node.attr}\"\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "@dataclass\n",
+    "class CallSignature:\n",
+    "    qualified_names: tuple[str, ...]\n",
+    "    positional_params: tuple[str, ...] = ()\n",
+    "    keyword_aliases: dict[str, str] = field(default_factory=dict)\n",
+    "\n",
+    "\n",
+    "class CallArgumentBinder:\n",
+    "    def bind(self, call: ast.Call, sig: CallSignature) -> dict[str, ast.AST]:\n",
+    "        bound: dict[str, ast.AST] = {}\n",
+    "        for idx, arg in enumerate(call.args):\n",
+    "            if idx < len(sig.positional_params):\n",
+    "                bound[sig.positional_params[idx]] = arg\n",
+    "        for kw in call.keywords:\n",
+    "            if kw.arg is None:\n",
+    "                continue\n",
+    "            canonical = sig.keyword_aliases.get(kw.arg, kw.arg)\n",
+    "            bound[canonical] = kw.value\n",
+    "        return bound\n",
+    "\n",
+    "\n",
+    "class StageSemanticFacts:\n",
+    "    def __init__(self, fn: ast.FunctionDef, resolver: QualifiedNameResolver):\n",
+    "        self.fn = fn\n",
+    "        self.resolver = resolver\n",
+    "        self.calls: list[ast.Call] = []\n",
+    "        self.assignments: list[ast.Assign] = []\n",
+    "        self.returns: list[ast.Return] = []\n",
+    "        self.name_to_value: dict[str, ast.AST] = {}\n",
+    "        self._scan()\n",
+    "\n",
+    "    def _scan(self):\n",
+    "        for stmt in self.fn.body:\n",
+    "            if isinstance(stmt, ast.Assign):\n",
+    "                self.assignments.append(stmt)\n",
+    "                if len(stmt.targets) == 1 and isinstance(stmt.targets[0], ast.Name):\n",
+    "                    self.name_to_value[stmt.targets[0].id] = stmt.value\n",
+    "            elif isinstance(stmt, ast.Return):\n",
+    "                self.returns.append(stmt)\n",
+    "            for node in ast.walk(stmt):\n",
+    "                if isinstance(node, ast.Call):\n",
+    "                    self.calls.append(node)\n",
+    "\n",
+    "    def call_qname(self, call: ast.Call) -> str | None:\n",
+    "        return self.resolver.resolve(call.func)\n",
+    "\n",
+    "    def all_call_names(self) -> list[str]:\n",
+    "        out = []\n",
+    "        for call in self.calls:\n",
+    "            name = self.call_qname(call)\n",
+    "            if name:\n",
+    "                out.append(name)\n",
+    "        return out\n",
+    "\n",
+    "    def final_return_value(self) -> ast.AST | None:\n",
+    "        for stmt in reversed(self.fn.body):\n",
+    "            if isinstance(stmt, ast.Return):\n",
+    "                return stmt.value\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class NativeMatchContext:\n",
+    "    def __init__(self, stage: StageInvocation, fn: ast.FunctionDef, facts: StageSemanticFacts,\n",
+    "                 input_columns: list[dict[str, str]], const_env: dict[str, ast.AST]):\n",
+    "        self.stage = stage\n",
+    "        self.fn = fn\n",
+    "        self.facts = facts\n",
+    "        self.input_columns = input_columns\n",
+    "        self.const_env = const_env\n",
+    "\n",
+    "    @property\n",
+    "    def input_column_names(self) -> list[str]:\n",
+    "        return [c.get('attributeName', '') for c in self.input_columns if c.get('attributeName')]\n",
+    "\n",
+    "    def resolve_const(self, node: ast.AST | None) -> ast.AST | None:\n",
+    "        if isinstance(node, ast.Name) and node.id in self.const_env:\n",
+    "            return self.const_env[node.id]\n",
+    "        return node\n",
+    "\n",
+    "    def const_value(self, node: ast.AST | None) -> Any:\n",
+    "        node = self.resolve_const(node)\n",
+    "        if isinstance(node, ast.Constant):\n",
+    "            return node.value\n",
+    "        return None\n",
+    "\n",
+    "    def subscript_column(self, node: ast.AST | None) -> str | None:\n",
+    "        if isinstance(node, ast.Subscript):\n",
+    "            sl = node.slice\n",
+    "            if isinstance(sl, ast.Constant) and isinstance(sl.value, str):\n",
+    "                return sl.value\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class NativeMatcher:\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        raise NotImplementedError\n",
+    "\n",
+    "\n",
+    "class PandasReadCsvMatcher(NativeMatcher):\n",
+    "    SIG = CallSignature(\n",
+    "        qualified_names=('pandas.read_csv', 'pd.read_csv'),\n",
+    "        positional_params=('filepath_or_buffer',),\n",
+    "        keyword_aliases={'filepath_or_buffer': 'filepath_or_buffer', 'sep': 'sep', 'delimiter': 'sep', 'header': 'header'},\n",
+    "    )\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        self.binder = CallArgumentBinder()\n",
+    "\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        if not ctx.stage.is_source:\n",
+    "            return None\n",
+    "        for call in ctx.facts.calls:\n",
+    "            qn = ctx.facts.call_qname(call)\n",
+    "            if qn not in self.SIG.qualified_names:\n",
+    "                continue\n",
+    "            args = self.binder.bind(call, self.SIG)\n",
+    "            file_node = ctx.resolve_const(args.get('filepath_or_buffer'))\n",
+    "            file_value = ctx.const_value(file_node)\n",
+    "            if isinstance(file_value, str) and file_value:\n",
+    "                file_name = file_value\n",
+    "            elif file_node is not None:\n",
+    "                file_name = ast.unparse(file_node)\n",
+    "            else:\n",
+    "                file_name = ''\n",
+    "            sep = ctx.const_value(args.get('sep')) or ','\n",
+    "            header = args.get('header')\n",
+    "            has_header = ctx.const_value(header)\n",
+    "            if has_header is None:\n",
+    "                has_header = True\n",
+    "            props = {\n",
+    "                'fileEncoding': 'UTF_8',\n",
+    "                'customDelimiter': sep,\n",
+    "                'hasHeader': bool(has_header),\n",
+    "                'fileName': file_name,\n",
+    "            }\n",
+    "            return NativeOperatorMatch(\n",
+    "                operator_type='CSVFileScan',\n",
+    "                properties=props,\n",
+    "                display_name='CSV File Scan',\n",
+    "                confidence=0.99,\n",
+    "                output_columns=ctx.stage.source_columns or None,\n",
+    "                retain_input_columns=False,\n",
+    "                notes=['Matched pandas.read_csv to CSVFileScan'],\n",
+    "            )\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class HistPlotMatcher(NativeMatcher):\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        for call in ctx.facts.calls:\n",
+    "            qn = ctx.facts.call_qname(call)\n",
+    "            col = None\n",
+    "            if qn in ('matplotlib.pyplot.hist', 'plt.hist') and call.args:\n",
+    "                col = ctx.subscript_column(call.args[0])\n",
+    "            elif qn in ('seaborn.histplot', 'sns.histplot'):\n",
+    "                for kw in call.keywords:\n",
+    "                    if kw.arg == 'x':\n",
+    "                        if isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str):\n",
+    "                            col = kw.value.value\n",
+    "                        else:\n",
+    "                            col = ctx.subscript_column(kw.value)\n",
+    "            elif qn in ('plotly.express.histogram', 'px.histogram'):\n",
+    "                for kw in call.keywords:\n",
+    "                    if kw.arg in ('x', 'y') and isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str):\n",
+    "                        col = kw.value.value\n",
+    "                        break\n",
+    "            elif qn is None and isinstance(call.func, ast.Attribute) and call.func.attr == 'plot':\n",
+    "                base = call.func.value\n",
+    "                if isinstance(base, ast.Call) and isinstance(base.func, ast.Attribute) and base.func.attr == 'dropna':\n",
+    "                    if call.keywords:\n",
+    "                        for kw in call.keywords:\n",
+    "                            if kw.arg == 'kind' and isinstance(kw.value, ast.Constant) and kw.value.value == 'hist':\n",
+    "                                col = ctx.subscript_column(base.func.value)\n",
+    "                                break\n",
+    "            if col:\n",
+    "                return NativeOperatorMatch(\n",
+    "                    operator_type='Histogram',\n",
+    "                    properties={'value': col},\n",
+    "                    display_name='Histogram',\n",
+    "                    confidence=0.95,\n",
+    "                    output_columns=ctx.input_columns,\n",
+    "                    retain_input_columns=True,\n",
+    "                    notes=[f'Matched histogram on column {col!r}'],\n",
+    "                )\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class ScatterPlotMatcher(NativeMatcher):\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        for call in ctx.facts.calls:\n",
+    "            qn = ctx.facts.call_qname(call)\n",
+    "            x = y = None\n",
+    "            if qn in ('matplotlib.pyplot.scatter', 'plt.scatter') and len(call.args) >= 2:\n",
+    "                x = ctx.subscript_column(call.args[0])\n",
+    "                y = ctx.subscript_column(call.args[1])\n",
+    "            elif qn in ('seaborn.scatterplot', 'sns.scatterplot'):\n",
+    "                for kw in call.keywords:\n",
+    "                    if kw.arg == 'x':\n",
+    "                        x = kw.value.value if isinstance(kw.value, ast.Constant) else ctx.subscript_column(kw.value)\n",
+    "                    elif kw.arg == 'y':\n",
+    "                        y = kw.value.value if isinstance(kw.value, ast.Constant) else ctx.subscript_column(kw.value)\n",
+    "            elif qn in ('plotly.express.scatter', 'px.scatter'):\n",
+    "                for kw in call.keywords:\n",
+    "                    if kw.arg == 'x' and isinstance(kw.value, ast.Constant):\n",
+    "                        x = kw.value.value\n",
+    "                    elif kw.arg == 'y' and isinstance(kw.value, ast.Constant):\n",
+    "                        y = kw.value.value\n",
+    "            if x and y:\n",
+    "                return NativeOperatorMatch(\n",
+    "                    operator_type='Scatterplot',\n",
+    "                    properties={'xColumn': x, 'yColumn': y, 'alpha': 1.0, 'xLogScale': False, 'yLogScale': False},\n",
+    "                    display_name='Scatter Plot',\n",
+    "                    confidence=0.95,\n",
+    "                    output_columns=ctx.input_columns,\n",
+    "                    retain_input_columns=True,\n",
+    "                    notes=[f'Matched scatter plot on {x!r}, {y!r}'],\n",
+    "                )\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class PieChartMatcher(NativeMatcher):\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        for call in ctx.facts.calls:\n",
+    "            qn = ctx.facts.call_qname(call)\n",
+    "            value = name = None\n",
+    "            if qn in ('matplotlib.pyplot.pie', 'plt.pie') and call.args:\n",
+    "                value = ctx.subscript_column(call.args[0])\n",
+    "                for kw in call.keywords:\n",
+    "                    if kw.arg == 'labels':\n",
+    "                        name = ctx.subscript_column(kw.value)\n",
+    "            elif qn in ('plotly.express.pie', 'px.pie'):\n",
+    "                for kw in call.keywords:\n",
+    "                    if kw.arg == 'values' and isinstance(kw.value, ast.Constant):\n",
+    "                        value = kw.value.value\n",
+    "                    elif kw.arg == 'names' and isinstance(kw.value, ast.Constant):\n",
+    "                        name = kw.value.value\n",
+    "            if value and name:\n",
+    "                return NativeOperatorMatch(\n",
+    "                    operator_type='PieChart',\n",
+    "                    properties={'value': value, 'name': name},\n",
+    "                    display_name='Pie Chart',\n",
+    "                    confidence=0.93,\n",
+    "                    output_columns=ctx.input_columns,\n",
+    "                    retain_input_columns=True,\n",
+    "                )\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class WordCloudMatcher(NativeMatcher):\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        # Look for WordCloud().generate(_from_text)?(table['col'])\n",
+    "        for call in ctx.facts.calls:\n",
+    "            qn = ctx.facts.call_qname(call)\n",
+    "            if qn not in ('wordcloud.WordCloud', 'WordCloud'):\n",
+    "                continue\n",
+    "            generated_col = None\n",
+    "            top_n = 200\n",
+    "            for kw in call.keywords:\n",
+    "                if kw.arg in ('max_words', 'top_n'):\n",
+    "                    v = ctx.const_value(kw.value)\n",
+    "                    if isinstance(v, int):\n",
+    "                        top_n = v\n",
+    "            # search users of constructor result\n",
+    "        for node in ast.walk(ctx.fn):\n",
+    "            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) and node.func.attr in ('generate', 'generate_from_text') and node.args:\n",
+    "                generated_col = ctx.subscript_column(node.args[0])\n",
+    "                if generated_col:\n",
+    "                    return NativeOperatorMatch(\n",
+    "                        operator_type='WordCloud',\n",
+    "                        properties={'topN': top_n, 'textColumn': generated_col},\n",
+    "                        display_name='Word Cloud',\n",
+    "                        confidence=0.92,\n",
+    "                        output_columns=ctx.input_columns,\n",
+    "                        retain_input_columns=True,\n",
+    "                    )\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class NativeOperatorKnowledgeBase:\n",
+    "    def __init__(self):\n",
+    "        self.matchers: list[NativeMatcher] = [\n",
+    "            PandasReadCsvMatcher(),\n",
+    "            HistPlotMatcher(),\n",
+    "            ScatterPlotMatcher(),\n",
+    "            PieChartMatcher(),\n",
+    "            WordCloudMatcher(),\n",
+    "        ]\n",
+    "\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        best: NativeOperatorMatch | None = None\n",
+    "        for matcher in self.matchers:\n",
+    "            match = matcher.match(ctx)\n",
+    "            if match is not None and (best is None or match.confidence > best.confidence):\n",
+    "                best = match\n",
+    "        return best\n",
+    "\n",
+    "\n",
+    "class NativeStagePlanner:\n",
+    "    def __init__(self, catalog: NativeOperatorCatalog | None = None):\n",
+    "        self.catalog = catalog or NativeOperatorCatalog()\n",
+    "        self.aliases = ImportAliasIndex()\n",
+    "        self.knowledge = NativeOperatorKnowledgeBase()\n",
+    "\n",
+    "    def plan(self, tree: ast.Module, fn: ast.FunctionDef, stage: StageInvocation,\n",
+    "             input_columns: list[dict[str, str]], const_env: dict[str, ast.AST]) -> NativeOperatorMatch | None:\n",
+    "        alias_map = self.aliases.build(tree)\n",
+    "        resolver = QualifiedNameResolver(alias_map)\n",
+    "        facts = StageSemanticFacts(fn, resolver)\n",
+    "        ctx = NativeMatchContext(stage, fn, facts, input_columns, const_env)\n",
+    "        match = self.knowledge.match(ctx)\n",
+    "        if match is None:\n",
+    "            return None\n",
+    "        if not self.catalog.has(match.operator_type):\n",
+    "            return None\n",
+    "        return match\n",
+    "\n",
+    "\n",
+    "# =============================\n",
+    "# Override workflow builders with native support\n",
+    "# =============================\n",
+    "\n",
+    "class WorkflowOperatorBuilder:\n",
+    "    def __init__(self, native_catalog: NativeOperatorCatalog | None = None):\n",
+    "        self.native_catalog = native_catalog or NativeOperatorCatalog()\n",
+    "\n",
+    "    def _operator_properties(self, stage: StageInvocation) -> dict[str, Any]:\n",
+    "        if getattr(stage, 'native_operator_type', None):\n",
+    "            props = copy.deepcopy(getattr(stage, 'native_operator_properties', {}) or {})\n",
+    "            return props\n",
+    "        props: dict[str, Any] = {\n",
+    "            'code': stage.code,\n",
+    "            'workers': 1,\n",
+    "        }\n",
+    "        if stage.operator_type == 'PythonUDFSourceV2':\n",
+    "            if stage.source_columns:\n",
+    "                props['columns'] = stage.source_columns\n",
+    "        else:\n",
+    "            props['retainInputColumns'] = stage.retain_input_columns\n",
+    "            if stage.output_columns:\n",
+    "                props['outputColumns'] = stage.output_columns\n",
+    "        return props\n",
+    "\n",
+    "    def _input_ports(self, stage: StageInvocation) -> list[dict[str, Any]]:\n",
+    "        if getattr(stage, 'native_operator_type', None):\n",
+    "            template = self.native_catalog.template(stage.native_operator_type)\n",
+    "            ports = copy.deepcopy(template.get('inputPorts', []))\n",
+    "            count = len(stage.data_inputs)\n",
+    "            if count and len(ports) >= count:\n",
+    "                for i in range(count):\n",
+    "                    if i < len(ports):\n",
+    "                        ports[i]['portID'] = f'input-{i}'\n",
+    "                        if count > 1 and i < len(stage.data_inputs):\n",
+    "                            ports[i]['displayName'] = stage.data_inputs[i].param_name\n",
+    "                            ports[i]['dependencies'] = [] if i == 0 else [{'id': i - 1, 'internal': False}]\n",
+    "                return ports[:count]\n",
+    "            return ports\n",
+    "        if stage.is_source:\n",
+    "            return []\n",
+    "        count = max(1, len(stage.data_inputs))\n",
+    "        ports = []\n",
+    "        for i in range(count):\n",
+    "            display_name = ''\n",
+    "            if count > 1 and i < len(stage.data_inputs):\n",
+    "                display_name = stage.data_inputs[i].param_name\n",
+    "            dependencies: list[dict[str, Any]] = []\n",
+    "            if count > 1 and i > 0:\n",
+    "                dependencies = [{'id': i - 1, 'internal': False}]\n",
+    "            ports.append({\n",
+    "                'portID': f'input-{i}',\n",
+    "                'displayName': display_name,\n",
+    "                'allowMultiInputs': True,\n",
+    "                'isDynamicPort': False,\n",
+    "                'dependencies': dependencies,\n",
+    "            })\n",
+    "        return ports\n",
+    "\n",
+    "    def _dynamic_ports_flags(self, stage: StageInvocation) -> tuple[bool, bool]:\n",
+    "        if getattr(stage, 'native_operator_type', None):\n",
+    "            template = self.native_catalog.template(stage.native_operator_type)\n",
+    "            return bool(template.get('dynamicInputPorts', False)), bool(template.get('dynamicOutputPorts', False))\n",
+    "        if stage.is_source:\n",
+    "            return False, False\n",
+    "        if stage.operator_type == 'DualInputPortsPythonUDFV2':\n",
+    "            return False, False\n",
+    "        return True, True\n",
+    "\n",
+    "    def build(self, stage: StageInvocation, view_result: bool = False) -> dict[str, Any]:\n",
+    "        if getattr(stage, 'native_operator_type', None):\n",
+    "            template = self.native_catalog.template(stage.native_operator_type)\n",
+    "            template['operatorID'] = stage.operator_id\n",
+    "            template['operatorType'] = stage.native_operator_type\n",
+    "            template['operatorVersion'] = 'N/A'\n",
+    "            template['operatorProperties'] = self._operator_properties(stage)\n",
+    "            template['inputPorts'] = self._input_ports(stage)\n",
+    "            template['outputPorts'] = copy.deepcopy(template.get('outputPorts', [\n",
+    "                {'portID': 'output-0', 'displayName': '', 'allowMultiInputs': False, 'isDynamicPort': False}\n",
+    "            ]))\n",
+    "            template['customDisplayName'] = stage.display_name\n",
+    "            if view_result:\n",
+    "                template['viewResult'] = True\n",
+    "            return template\n",
+    "\n",
+    "        dynamic_input_ports, dynamic_output_ports = self._dynamic_ports_flags(stage)\n",
+    "        operator = {\n",
+    "            'operatorID': stage.operator_id,\n",
+    "            'operatorType': stage.operator_type,\n",
+    "            'operatorVersion': 'N/A',\n",
+    "            'operatorProperties': self._operator_properties(stage),\n",
+    "            'inputPorts': self._input_ports(stage),\n",
+    "            'outputPorts': [\n",
+    "                {'portID': 'output-0', 'displayName': '', 'allowMultiInputs': False, 'isDynamicPort': False}\n",
+    "            ],\n",
+    "            'showAdvanced': False,\n",
+    "            'isDisabled': False,\n",
+    "            'customDisplayName': stage.display_name,\n",
+    "            'dynamicInputPorts': dynamic_input_ports,\n",
+    "            'dynamicOutputPorts': dynamic_output_ports,\n",
+    "        }\n",
+    "        if view_result:\n",
+    "            operator['viewResult'] = True\n",
+    "        return operator\n",
+    "\n",
+    "\n",
+    "class WorkflowJsonAssembler:\n",
+    "    def __init__(self, native_catalog: NativeOperatorCatalog | None = None):\n",
+    "        self.operator_builder = WorkflowOperatorBuilder(native_catalog=native_catalog)\n",
+    "        self.link_builder = WorkflowLinkBuilder()\n",
+    "        self.layout_builder = WorkflowLayoutBuilder()\n",
+    "\n",
+    "    def assemble(self, stages: list[StageInvocation]) -> dict[str, Any]:\n",
+    "        operators = [\n",
+    "            self.operator_builder.build(stage, view_result=(i == len(stages) - 1))\n",
+    "            for i, stage in enumerate(stages)\n",
+    "        ]\n",
+    "        return {\n",
+    "            'operators': operators,\n",
+    "            'operatorPositions': self.layout_builder.build_positions(stages),\n",
+    "            'links': self.link_builder.build_links(stages),\n",
+    "            'commentBoxes': [],\n",
+    "            'settings': {'dataTransferBatchSize': 400},\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "class WorkflowTranslator:\n",
+    "    def __init__(self, catalog_json_path: str | Path | None = None):\n",
+    "        self.parser = SourceModuleParser()\n",
+    "        self.imports = ImportCollector()\n",
+    "        self.functions = FunctionIndex()\n",
+    "        self.call_graph = CallGraphBuilder()\n",
+    "        self.recursion = RecursiveAnalyzer()\n",
+    "        self.facts = FunctionFactsAnalyzer()\n",
+    "        self.constant_env = ConstantEnvBuilder()\n",
+    "        self.dag_extractor = EntryDagExtractor()\n",
+    "        self.ids = WorkflowIdFactory()\n",
+    "        self.code_emitter = FunctionBodyCompiler()\n",
+    "        self.catalog = NativeOperatorCatalog(catalog_json_path)\n",
+    "        self.native_planner = NativeStagePlanner(self.catalog)\n",
+    "        self.workflow = WorkflowJsonAssembler(native_catalog=self.catalog)\n",
+    "        self.schema_inferer = SourceSchemaInferer()\n",
+    "        self.table_schema_inferer = TableOutputSchemaInferer()\n",
+    "\n",
+    "    def translate(self, source: str, entry: str = 'main') -> dict[str, Any]:\n",
+    "        tree = self.parser.parse(source)\n",
+    "        import_nodes = self.imports.collect_nodes(tree)\n",
+    "        functions = self.functions.collect(tree)\n",
+    "        if entry not in functions:\n",
+    "            raise ValueError(f\"Entry function {entry!r} not found. Available: {sorted(functions)}\")\n",
+    "\n",
+    "        graph = self.call_graph.build(functions)\n",
+    "        recursive = self.recursion.detect(graph)\n",
+    "        facts = self.facts.analyze(tree, functions, graph, recursive)\n",
+    "        const_env = self.constant_env.build(functions[entry])\n",
+    "        stages = self.dag_extractor.extract(functions[entry], functions, facts, const_env)\n",
+    "        if not stages:\n",
+    "            raise ValueError(f\"Entry function {entry!r} does not contain a detectable pipeline of helper calls.\")\n",
+    "\n",
+    "        stage_by_id: dict[str, StageInvocation] = {s.stage_id: s for s in stages}\n",
+    "        selector = UdfBaseSelector()\n",
+    "        for stage in stages:\n",
+    "            fn = functions[stage.function_name]\n",
+    "            if len(stage.data_inputs) > 2:\n",
+    "                raise ValueError(f\"Current workflow JSON emitter supports at most 2 data inputs per stage, got {len(stage.data_inputs)} for {stage.function_name!r}.\")\n",
+    "            stage.display_name = ' '.join(part.capitalize() for part in stage.function_name.split('_'))\n",
+    "            # default UDF path\n",
+    "            stage.operator_type = selector.workflow_operator_type(stage.family, stage.is_source, len(stage.data_inputs))\n",
+    "            stage.operator_id = self.ids.operator_id(stage.operator_type)\n",
+    "            stage.source_columns = self.schema_inferer.infer(fn, stage)\n",
+    "            if stage.family == 'table':\n",
+    "                input_columns: list[dict[str, str]] = []\n",
+    "                for di in stage.data_inputs:\n",
+    "                    upstream = stage_by_id[di.upstream_stage_id]\n",
+    "                    input_columns.extend(getattr(upstream, 'output_columns', []) or getattr(upstream, 'source_columns', []))\n",
+    "                if stage.is_source:\n",
+    "                    stage.output_columns = list(stage.source_columns)\n",
+    "                    stage.retain_input_columns = False\n",
+    "                else:\n",
+    "                    stage.output_columns, stage.retain_input_columns = self.table_schema_inferer.infer(fn, stage, input_columns)\n",
+    "            else:\n",
+    "                input_columns = []\n",
+    "\n",
+    "            native_match = self.native_planner.plan(tree, fn, stage, input_columns, const_env)\n",
+    "            if native_match is not None:\n",
+    "                stage.native_operator_type = native_match.operator_type\n",
+    "                stage.native_operator_properties = native_match.properties\n",
+    "                stage.native_notes = native_match.notes\n",
+    "                stage.operator_type = native_match.operator_type\n",
+    "                stage.operator_id = self.ids.operator_id(native_match.operator_type)\n",
+    "                if native_match.display_name:\n",
+    "                    stage.display_name = native_match.display_name if stage.is_source else stage.display_name\n",
+    "                if native_match.output_columns is not None:\n",
+    "                    stage.output_columns = native_match.output_columns\n",
+    "                    if stage.is_source:\n",
+    "                        stage.source_columns = native_match.output_columns\n",
+    "                if native_match.retain_input_columns is not None:\n",
+    "                    stage.retain_input_columns = native_match.retain_input_columns\n",
+    "                stage.code = ''\n",
+    "            else:\n",
+    "                if stage.operator_type == 'PythonUDFSourceV2' and stage.family == 'table' and not stage.source_columns:\n",
+    "                    raise ValueError(\n",
+    "                        f\"Could not infer output columns for source stage {stage.function_name!r}. \"\n",
+    "                        'Make the source schema explicit or use a source that can be sampled at translation time.'\n",
+    "                    )\n",
+    "                stage.code = self.code_emitter.emit_class(import_nodes, fn, stage)\n",
+    "\n",
+    "        workflow_json = self.workflow.assemble(stages)\n",
+    "        metadata = {\n",
+    "            'entry': entry,\n",
+    "            'catalog_path': str(self.catalog.catalog_json_path) if self.catalog.catalog_json_path else None,\n",
+    "            'stages': [\n",
+    "                {\n",
+    "                    'stage_id': s.stage_id,\n",
+    "                    'function': s.function_name,\n",
+    "                    'operatorID': s.operator_id,\n",
+    "                    'operator_type': s.operator_type,\n",
+    "                    'native_operator_type': getattr(s, 'native_operator_type', None),\n",
+    "                    'family': s.family,\n",
+    "                    'base_class': None if getattr(s, 'native_operator_type', None) else selector.select(s.family, s.is_source)[1],\n",
+    "                    'method': None if getattr(s, 'native_operator_type', None) else selector.select(s.family, s.is_source)[2],\n",
+    "                    'is_source': s.is_source,\n",
+    "                    'is_sink': s.is_sink,\n",
+    "                    'recursive': s.recursive,\n",
+    "                    'has_loop': s.has_loop,\n",
+    "                    'assigned_to': s.assigned_to,\n",
+    "                    'data_inputs': [\n",
+    "                        {\n",
+    "                            'param_name': di.param_name,\n",
+    "                            'upstream_stage_id': di.upstream_stage_id,\n",
+    "                            'upstream_var': di.upstream_var,\n",
+    "                            'input_port': di.input_port,\n",
+    "                        }\n",
+    "                        for di in s.data_inputs\n",
+    "                    ],\n",
+    "                    'ui_parameters': [\n",
+    "                        {\n",
+    "                            'name': p.name,\n",
+    "                            'attr_type': p.attr_type,\n",
+    "                            'default': ast.unparse(p.default_ast) if p.default_ast is not None else None,\n",
+    "                        }\n",
+    "                        for p in s.ui_params\n",
+    "                    ],\n",
+    "                    'source_columns': s.source_columns,\n",
+    "                    'output_columns': s.output_columns,\n",
+    "                    'retain_input_columns': s.retain_input_columns,\n",
+    "                    'native_notes': getattr(s, 'native_notes', []),\n",
+    "                }\n",
+    "                for s in stages\n",
+    "            ],\n",
+    "        }\n",
+    "        return {'workflow_json': workflow_json, 'metadata': metadata}\n",
+    "\n",
+    "\n",
+    "def translate_to_texera_workflow_json(source: str, entry: str = 'main', catalog_json_path: str | Path | None = None) -> dict[str, Any]:\n",
+    "    return WorkflowTranslator(catalog_json_path=catalog_json_path).translate(source, entry=entry)\n",
+    "\n",
+    "# =============================\n",
+    "# Native matcher extensions added in v15\n",
+    "# =============================\n",
+    "\n",
+    "class TimeSeriesPlotMatcher(NativeMatcher):\n",
+    "    \"\"\"Match simple matplotlib/seaborn/plotly line plots over a column pair.\n",
+    "\n",
+    "    Conservative mapping:\n",
+    "      plt.plot(table['date'], table['close']) -> TimeSeriesPlot\n",
+    "    We avoid LineChart here because the catalog template exposes a less explicit\n",
+    "    nested `lines` shape, while TimeSeriesPlot has a stable flat property schema.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        for call in ctx.facts.calls:\n",
+    "            qn = ctx.facts.call_qname(call)\n",
+    "            x = y = None\n",
+    "\n",
+    "            if qn in ('matplotlib.pyplot.plot', 'plt.plot') and len(call.args) >= 2:\n",
+    "                x = ctx.subscript_column(call.args[0])\n",
+    "                y = ctx.subscript_column(call.args[1])\n",
+    "            elif qn in ('seaborn.lineplot', 'sns.lineplot'):\n",
+    "                for kw in call.keywords:\n",
+    "                    if kw.arg == 'x':\n",
+    "                        x = kw.value.value if isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str) else ctx.subscript_column(kw.value)\n",
+    "                    elif kw.arg == 'y':\n",
+    "                        y = kw.value.value if isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str) else ctx.subscript_column(kw.value)\n",
+    "            elif qn in ('plotly.express.line', 'px.line'):\n",
+    "                for kw in call.keywords:\n",
+    "                    if kw.arg == 'x' and isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str):\n",
+    "                        x = kw.value.value\n",
+    "                    elif kw.arg == 'y' and isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str):\n",
+    "                        y = kw.value.value\n",
+    "\n",
+    "            if x and y:\n",
+    "                return NativeOperatorMatch(\n",
+    "                    operator_type='TimeSeriesPlot',\n",
+    "                    properties={\n",
+    "                        'timeColumn': x,\n",
+    "                        'valueColumn': y,\n",
+    "                        'line': y,\n",
+    "                        'facetColumn': 'No Selection',\n",
+    "                        'categoryColumn': 'No Selection',\n",
+    "                        'slider': False,\n",
+    "                    },\n",
+    "                    display_name='Time Series Plot',\n",
+    "                    confidence=0.96,\n",
+    "                    output_columns=ctx.input_columns,\n",
+    "                    retain_input_columns=True,\n",
+    "                    notes=[f'Matched line/time-series plot on x={x!r}, y={y!r}'],\n",
+    "                )\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class NativeOperatorKnowledgeBase:\n",
+    "    def __init__(self):\n",
+    "        self.matchers: list[NativeMatcher] = [\n",
+    "            PandasReadCsvMatcher(),\n",
+    "            TimeSeriesPlotMatcher(),\n",
+    "            HistPlotMatcher(),\n",
+    "            ScatterPlotMatcher(),\n",
+    "            PieChartMatcher(),\n",
+    "            WordCloudMatcher(),\n",
+    "        ]\n",
+    "\n",
+    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
+    "        best: NativeOperatorMatch | None = None\n",
+    "        for matcher in self.matchers:\n",
+    "            match = matcher.match(ctx)\n",
+    "            if match is not None and (best is None or match.confidence > best.confidence):\n",
+    "                best = match\n",
+    "        return best\n",
+    "if __name__ == \"__main__\":\n",
+    "    result = translate_to_texera_workflow_json(sample, entry=\"main\", catalog_json_path=\"catalog.json\")\n",
+    "    paths = write_translation(result, \"demo_texera_workflow_vx2\")\n",
+    "    print(json.dumps(paths, indent=2))"
+   ],
+   "id": "419833dbad71acdf",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"workflow_json\": \"demo_texera_workflow_vx2.workflow.json\",\n",
+      "  \"workflow_metadata\": \"demo_texera_workflow_vx2.workflow.meta.json\"\n",
+      "}\n"
+     ]
+    }
+   ],
+   "execution_count": 52
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T07:44:16.497856Z",
+     "start_time": "2026-03-04T07:44:15.182307Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "a8549cb2873a7b65",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"workflow_json\": \"demo_texera_workflow_vx2.workflow.json\",\n",
+      "  \"workflow_metadata\": \"demo_texera_workflow_vx2.workflow.meta.json\"\n",
+      "}\n"
+     ]
+    }
+   ],
+   "execution_count": 40
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T05:37:27.140055Z",
+     "start_time": "2026-03-04T05:37:27.135017Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "sample = '''\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def extract_data(url: str) -> pd.DataFrame:\n",
+    "    df = pd.read_csv(url)\n",
+    "    print(\"Step 1: Extract\")\n",
+    "    print(df.head())\n",
+    "    return df\n",
+    "\n",
+    "def select_columns(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    selected = df[[\"date\", \"open\", \"high\", \"low\", \"close\", \"volume\"]].copy()\n",
+    "    print(\"\\\\nStep 2: Select columns\")\n",
+    "    print(selected.head())\n",
+    "    return selected\n",
+    "\n",
+    "def clean_data(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    cleaned = df.dropna().copy()\n",
+    "    print(\"\\\\nStep 3: Clean data\")\n",
+    "    print(cleaned.head())\n",
+    "    return cleaned\n",
+    "\n",
+    "def transform_data(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    transformed = df.copy()\n",
+    "    transformed[\"date\"] = pd.to_datetime(transformed[\"date\"], errors=\"coerce\")\n",
+    "    transformed = transformed.dropna(subset=[\"date\"]).copy()\n",
+    "    transformed = transformed.sort_values(\"date\")\n",
+    "    transformed[\"daily_return\"] = transformed[\"close\"].pct_change() * 100\n",
+    "    transformed[\"ma_30\"] = transformed[\"close\"].rolling(window=30).mean()\n",
+    "    print(\"\\\\nStep 4: Transform data\")\n",
+    "    print(transformed.head())\n",
+    "    return transformed\n",
+    "\n",
+    "def load_data(df: pd.DataFrame, output_file: str) -> None:\n",
+    "    df.to_csv(output_file, index=False)\n",
+    "    print(f\"\\\\nStep 5: Load data\")\n",
+    "    print(f\"Saved cleaned data to: {output_file}\")\n",
+    "\n",
+    "def plot_data(df: pd.DataFrame) -> None:\n",
+    "    plt.figure(figsize=(10, 5))\n",
+    "    plt.plot(df[\"date\"], df[\"close\"])\n",
+    "    plt.show()\n",
+    "\n",
+    "def main():\n",
+    "    url = \"https://raw.githubusercontent.com/vega/vega-datasets/main/data/sp500-2000.csv\"\n",
+    "    output_file = \"clean_sp500_2000.csv\"\n",
+    "\n",
+    "    raw_df = extract_data(url)\n",
+    "    selected_df = select_columns(raw_df)\n",
+    "    cleaned_df = clean_data(selected_df)\n",
+    "    transformed_df = transform_data(cleaned_df)\n",
+    "    load_data(transformed_df, output_file)\n",
+    "    plot_data(transformed_df)'''"
+   ],
+   "id": "85c4fdcfef2fa543",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "ab7e07559b2247ee"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 4a5686154c01b7811c276b2ae2f89d92cf0385ec Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Sat, 7 Mar 2026 00:17:37 -0600
Subject: [PATCH 05/12] v2.1

---
 .../operator-property-edit-frame.component.ts | 41 ++++---------------
 1 file changed, 8 insertions(+), 33 deletions(-)

diff --git a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
index 300c5bfcbab..e98193c9924 100644
--- a/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
+++ b/frontend/src/app/workspace/component/property-editor/operator-property-edit-frame/operator-property-edit-frame.component.ts
@@ -200,41 +200,16 @@ export class OperatorPropertyEditFrameComponent implements OnInit, OnChanges, On
       .subscribe(({ operatorId, parameters }) => {
         if (operatorId !== this.currentOperatorId) return;
 
-        const readCurrentUiParams = () =>
-          this.workflowActionService.getTexeraGraph().getOperator(operatorId).operatorProperties?.uiParameters ?? [];
+        const currentOperator = this.workflowActionService.getTexeraGraph().getOperator(operatorId);
 
-        // initial read
-        let currentUiParams = readCurrentUiParams();
-
-        // max attempts = abs(prevLen - newLen), with sane bounds
-        const diffLen = Math.abs((currentUiParams?.length ?? 0) - (parameters?.length ?? 0));
-        const MAX_ATTEMPTS = Math.min(Math.max(diffLen, 1), 20);
-
-        let attempts = 0;
-
-        while (!isEqual(currentUiParams, parameters) && attempts < MAX_ATTEMPTS) {
-          const currentOperator = this.workflowActionService.getTexeraGraph().getOperator(operatorId);
-
-          const newModel = {
-            ...cloneDeep(currentOperator.operatorProperties),
-            uiParameters: cloneDeep(parameters),
-          };
-
-          this.listeningToChange = false;
-          this.workflowActionService.setOperatorProperty(operatorId, newModel);
-          this.listeningToChange = true;
-
-          // re-read after mutation
-          currentUiParams = readCurrentUiParams();
-          attempts++;
-        }
+        const newModel = {
+          ...cloneDeep(currentOperator.operatorProperties),
+          uiParameters: cloneDeep(parameters),
+        };
 
-        if (!isEqual(currentUiParams, parameters)) {
-          console.warn(`uiParameters did not converge after ${attempts}/${MAX_ATTEMPTS} attempts`, {
-            currentLen: currentUiParams.length,
-            targetLen: parameters.length,
-          });
-        }
+        this.listeningToChange = false;
+        this.workflowActionService.setOperatorProperty(operatorId, newModel);
+        this.listeningToChange = true;
       });
   }
 

From 5aa3f4e26e135b776e4425be4825b95353840741 Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Sat, 7 Mar 2026 01:54:49 -0600
Subject: [PATCH 06/12] vnt

---
 .../amber/pybuilder/EncodableInspector.scala   | 16 +++-------------
 .../pybuilder/EncodableStringAnnotation.java   |  3 +--
 .../python/PythonUdfUiParameterInjector.scala  | 18 ++++++++++++++++++
 .../PythonUdfUiParameterInjectorSpec.scala     | 18 ++++++++++++++++++
 .../ui-udf-parameters.component.html           | 18 ++++++++++++++++++
 .../ui-udf-parameters.component.ts             | 18 ++++++++++++++++++
 .../ui-udf-parameters-sync.service.ts          | 18 ++++++++++++++++++
 7 files changed, 94 insertions(+), 15 deletions(-)

diff --git a/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableInspector.scala b/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableInspector.scala
index 9362016c72f..58bdcb649ec 100644
--- a/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableInspector.scala
+++ b/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableInspector.scala
@@ -46,7 +46,7 @@ final class EncodableInspector[C <: blackbox.Context](val c: C) {
 
   // Keep this as a string so it also works if the annotation is referenced indirectly.
   private val encodableStringAnnotationFqn =
-    "org.apache.texera.amber.pybuilder.EncodableStringAnnotation"
+    "org.apache.texera.amber.EncodableStringAnn"
 
   /**
    * If we are pointing at a getter/accessor, hop to its accessed field symbol when possible.
@@ -110,20 +110,10 @@ final class EncodableInspector[C <: blackbox.Context](val c: C) {
     val symHasAnn =
       rawSym != null && rawSym != NoSymbol && {
         val accessed = safeAccessed(rawSym)
-        accessed != null && accessed != NoSymbol &&
-          accessed.annotations.exists(annIsEncodableString)
+        accessed != null && accessed != NoSymbol && accessed.annotations.exists(annIsEncodableString)
       }
 
-    val methodReturnHasAnn =
-      rawSym != null && rawSym != NoSymbol && (rawSym match {
-        case m: MethodSymbol =>
-          typeHasEncodableString(m.typeSignature.finalResultType)
-        case _ =>
-          false
-      })
-
-    symHasAnn || methodReturnHasAnn ||
-      (tree.tpe != null && typeHasEncodableString(tree.tpe))
+    symHasAnn || (tree.tpe != null && typeHasEncodableString(tree.tpe))
   }
 
   def isPythonTemplateBuilderArg(argExpr: c.Expr[Any]): Boolean = {
diff --git a/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableStringAnnotation.java b/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableStringAnnotation.java
index f94c01f680a..ea17e6d0130 100644
--- a/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableStringAnnotation.java
+++ b/common/pybuilder/src/main/scala/org/apache/texera/amber/pybuilder/EncodableStringAnnotation.java
@@ -29,7 +29,6 @@
     ElementType.FIELD,
     ElementType.PARAMETER,
     ElementType.TYPE_USE,
-    ElementType.LOCAL_VARIABLE,
-    ElementType.METHOD
+    ElementType.LOCAL_VARIABLE
 })
 public @interface EncodableStringAnnotation {}
\ No newline at end of file
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
index 76bf11048e4..189882b7dc6 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
@@ -1,3 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
 package org.apache.texera.amber.operator.udf.python
 
 import org.apache.texera.amber.pybuilder.PythonTemplateBuilder
diff --git a/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
index e4946c1b0f6..9d7e367255b 100644
--- a/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
+++ b/common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjectorSpec.scala
@@ -1,3 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
 package org.apache.texera.amber.operator.udf.python
 
 import org.apache.texera.amber.core.tuple.{Attribute, AttributeType}
diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
index 8b9ffecdb2f..0ecfb16a780 100644
--- a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.html
@@ -1,3 +1,21 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements.  See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership.  The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License.  You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied.  See the License for the
+ specific language governing permissions and limitations
+ under the License.
+-->
 <div
   class="ui-udf-param-list"
   *ngIf="(model?.length ?? 0) > 0">
diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
index 85972be9e76..10e98d7afa7 100644
--- a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.ts
@@ -1,3 +1,21 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
 import { Component } from "@angular/core";
 import { FieldArrayType, FormlyFieldConfig } from "@ngx-formly/core";
 
diff --git a/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts
index c28742977ff..edd9cc428a2 100644
--- a/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts
+++ b/frontend/src/app/workspace/service/code-editor/ui-udf-parameters-sync.service.ts
@@ -1,3 +1,21 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
 import { Injectable } from "@angular/core";
 import { isEqual } from "lodash-es";
 import { ReplaySubject } from "rxjs";

From 1d82c9af5f2216c7c77a247d5f87416ce0b8970f Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Sat, 7 Mar 2026 01:55:53 -0600
Subject: [PATCH 07/12] v2

---
 .../src/main/scala/py2udf/catalog.json        | 2889 ----------
 .../main/scala/py2udf/clean_sp500_2000.csv    | 5106 -----------------
 .../demo_texera_workflow_vx2.workflow.json    |  400 --
 ...emo_texera_workflow_vx2.workflow.meta.json |  398 --
 .../src/main/scala/py2udf/py2udf.ipynb        | 2028 -------
 5 files changed, 10821 deletions(-)
 delete mode 100644 common/workflow-operator/src/main/scala/py2udf/catalog.json
 delete mode 100644 common/workflow-operator/src/main/scala/py2udf/clean_sp500_2000.csv
 delete mode 100644 common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.json
 delete mode 100644 common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.meta.json
 delete mode 100644 common/workflow-operator/src/main/scala/py2udf/py2udf.ipynb

diff --git a/common/workflow-operator/src/main/scala/py2udf/catalog.json b/common/workflow-operator/src/main/scala/py2udf/catalog.json
deleted file mode 100644
index a65f77859a7..00000000000
--- a/common/workflow-operator/src/main/scala/py2udf/catalog.json
+++ /dev/null
@@ -1,2889 +0,0 @@
-{
-  "operators": [
-    {
-      "operatorID": "ArrowSource-operator-2953ddfa-3b7c-4b40-be61-e86af2d5e4fa",
-      "operatorType": "ArrowSource",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "fileName": ""
-      },
-      "inputPorts": [],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Arrow File Scan",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "CSVFileScan-operator-7a2892e1-8812-41d9-80aa-273aa68cb8cc",
-      "operatorType": "CSVFileScan",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "fileEncoding": "UTF_8",
-        "customDelimiter": ",",
-        "hasHeader": true,
-        "fileName": ""
-      },
-      "inputPorts": [],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "CSV File Scan",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "FileScan-operator-a71dc68f-95db-433a-b927-4aa1748d6ee6",
-      "operatorType": "FileScan",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "encoding": "UTF_8",
-        "extract": false,
-        "outputFileName": false,
-        "attributeType": "string",
-        "attributeName": "line",
-        "fileName": ""
-      },
-      "inputPorts": [],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": " File Scan",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "JSONLFileScan-operator-fa519958-7a70-4761-a579-aa2192e026d9",
-      "operatorType": "JSONLFileScan",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "fileEncoding": "UTF_8",
-        "fileName": ""
-      },
-      "inputPorts": [],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "JSONL File Scan",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "TextInput-operator-c922ef7e-d321-495c-9517-25ebe6669d86",
-      "operatorType": "TextInput",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "attributeType": "string",
-        "attributeName": "line",
-        "textInput": ""
-      },
-      "inputPorts": [],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Text Input",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "DictionaryMatcher-operator-433f9c67-db8c-4682-8181-d7ee21dc446f",
-      "operatorType": "DictionaryMatcher",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "result attribute": "matched",
-        "Dictionary": "",
-        "Attribute": "",
-        "Matching type": ""
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Dictionary matcher",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "KeywordSearch-operator-f4d943e2-f145-483c-9b73-301201fbebc4",
-      "operatorType": "KeywordSearch",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "attribute": "",
-        "keyword": ""
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Keyword Search",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "Regex-operator-a822d4bb-174d-4cda-a282-5915d5bc5f22",
-      "operatorType": "Regex",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "caseInsensitive": false,
-        "attribute": "",
-        "regex": ""
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Regular Expression",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "SubstringSearch-operator-22333ad4-cf2c-4bdf-a204-a6ab2ab8e826",
-      "operatorType": "SubstringSearch",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "isCaseSensitive": false,
-        "attribute": "",
-        "substring": ""
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Substring Search",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "CartesianProduct-operator-98d5fc05-8542-43fc-97f3-4ffeee552a8b",
-      "operatorType": "CartesianProduct",
-      "operatorVersion": "N/A",
-      "operatorProperties": {},
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "left",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        },
-        {
-          "portID": "input-1",
-          "displayName": "right",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": [
-            {
-              "id": 0,
-              "internal": false
-            }
-          ]
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Cartesian Product",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "HashJoin-operator-6be1c0e0-4ffc-4b3a-9921-652089668587",
-      "operatorType": "HashJoin",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "joinType": "inner",
-        "buildAttributeName": "",
-        "probeAttributeName": ""
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "left",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        },
-        {
-          "portID": "input-1",
-          "displayName": "right",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": [
-            {
-              "id": 0,
-              "internal": false
-            }
-          ]
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Hash Join",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "IntervalJoin-operator-94ce5ee3-888d-4194-8750-6af5eafb3bcd",
-      "operatorType": "IntervalJoin",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "constant": 10,
-        "includeLeftBound": true,
-        "includeRightBound": true,
-        "timeIntervalType": "day",
-        "leftAttributeName": "",
-        "rightAttributeName": ""
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "left table",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        },
-        {
-          "portID": "input-1",
-          "displayName": "right table",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": [
-            {
-              "id": 0,
-              "internal": false
-            }
-          ]
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Interval Join",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "Aggregate-operator-fbcc0b1a-d5bc-42c7-89d9-5f99a98d0569",
-      "operatorType": "Aggregate",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "aggregations": [
-          {
-            "aggFunction": "",
-            "attribute": "",
-            "result attribute": ""
-          }
-        ]
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Aggregate",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "Distinct-operator-e55deaf5-0898-4af9-8195-965899c8ea9b",
-      "operatorType": "Distinct",
-      "operatorVersion": "N/A",
-      "operatorProperties": {},
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Distinct",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "Sort-operator-0ed0e580-1853-46a1-aca8-95c338099e8b",
-      "operatorType": "Sort",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "attributes": []
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Sort",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "Sort-operator-6ed8c337-70be-4e3a-b931-73aa0ecf0d42",
-      "operatorType": "Sort",
-      "operatorVersion": "N/A",
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\ No newline at end of file
diff --git a/common/workflow-operator/src/main/scala/py2udf/clean_sp500_2000.csv b/common/workflow-operator/src/main/scala/py2udf/clean_sp500_2000.csv
deleted file mode 100644
index b1e782376af..00000000000
--- a/common/workflow-operator/src/main/scala/py2udf/clean_sp500_2000.csv
+++ /dev/null
@@ -1,5106 +0,0 @@
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-2020-03-16,2508.590088,2562.97998,2380.939941,2386.129883,7781540000,-11.984055248695647,3114.5653157333336
-2020-03-17,2425.659912,2553.929932,2367.040039,2529.189941,8358500000,5.995484949047936,3090.574316366667
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-2020-03-20,2431.939941,2453.01001,2295.560059,2304.919922,9044690000,-4.335951242408564,2995.0526448666665
-2020-03-23,2290.709961,2300.72998,2191.860107,2237.399902,7402180000,-2.9293868023585024,2958.7089762333335
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-2020-03-25,2457.77002,2571.419922,2407.530029,2475.560059,8285670000,1.1535011665884554,2899.143977866667
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-2020-03-30,2558.97998,2631.800049,2545.280029,2626.649902,5746220000,3.351600922771647,2821.2986491
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-2020-04-02,2458.540039,2533.219971,2455.790039,2526.899902,6454990000,2.28293470957297,2736.3759847333336
-2020-04-03,2514.919922,2538.179932,2459.959961,2488.649902,6087190000,-1.513712512700871,2708.072648133333
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-2020-04-07,2738.649902,2756.889893,2657.669922,2659.409912,7040720000,-0.1603052960193252,2673.7056478
-2020-04-08,2685.0,2760.75,2663.300049,2749.97998,5856370000,3.4056452745897747,2661.491984033333
-2020-04-09,2776.98999,2818.570068,2762.360107,2789.820068,7880140000,1.4487410195618944,2655.193985966667
-2020-04-13,2782.459961,2782.459961,2721.169922,2761.629883,5274310000,-1.0104660627883844,2648.774316366667
-2020-04-14,2805.100098,2851.850098,2805.100098,2846.060059,5567400000,3.057258922339079,2640.635319
-2020-04-15,2795.639893,2801.879883,2761.540039,2783.360107,5203390000,-2.2030438817243536,2633.301652
-2020-04-16,2799.340088,2806.51001,2764.320068,2799.550049,5179990000,0.5816689676367393,2622.282649733333
-2020-04-17,2842.429932,2879.219971,2830.879883,2874.560059,5792140000,2.6793594930297315,2617.303320333333
diff --git a/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.json b/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.json
deleted file mode 100644
index e17e1172872..00000000000
--- a/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.json
+++ /dev/null
@@ -1,400 +0,0 @@
-{
-  "operators": [
-    {
-      "operatorID": "CSVFileScan-operator-52a5c4c1-6d4c-4ce6-8d58-c69e991b2b15",
-      "operatorType": "CSVFileScan",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "fileEncoding": "UTF_8",
-        "customDelimiter": ",",
-        "hasHeader": true,
-        "fileName": "https://raw.githubusercontent.com/vega/vega-datasets/main/data/sp500-2000.csv"
-      },
-      "inputPorts": [],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "CSV File Scan",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false
-    },
-    {
-      "operatorID": "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34",
-      "operatorType": "PythonUDFV2",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "code": "from typing import Iterator, Optional, Union\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pytexera import *\nfrom core.models.schema.attribute_type import AttributeType\nclass ProcessTableOperator(UDFTableOperator):\n\n    def open(self) -> None:\n        pass\n\n    @overrides\n    def process_table(self, table: Table, port: int) -> Iterator[Optional[TableLike]]:\n        selected = table[['date', 'open', 'high', 'low', 'close', 'volume']].copy()\n        print('\\nStep 2: Select columns')\n        print(selected.head())\n        yield selected\n        return\n\n    def close(self) -> None:\n        pass\n",
-        "workers": 1,
-        "retainInputColumns": false,
-        "outputColumns": [
-          {
-            "attributeName": "date",
-            "attributeType": "string"
-          },
-          {
-            "attributeName": "open",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "high",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "low",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "close",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "volume",
-            "attributeType": "long"
-          }
-        ]
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": true,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Select Columns",
-      "dynamicInputPorts": true,
-      "dynamicOutputPorts": true
-    },
-    {
-      "operatorID": "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf",
-      "operatorType": "PythonUDFV2",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "code": "from typing import Iterator, Optional, Union\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pytexera import *\nfrom core.models.schema.attribute_type import AttributeType\nclass ProcessTableOperator(UDFTableOperator):\n\n    def open(self) -> None:\n        pass\n\n    @overrides\n    def process_table(self, table: Table, port: int) -> Iterator[Optional[TableLike]]:\n        cleaned = table.dropna().copy()\n        print('\\nStep 3: Clean data')\n        print(cleaned.head())\n        yield cleaned\n        return\n\n    def close(self) -> None:\n        pass\n",
-        "workers": 1,
-        "retainInputColumns": false,
-        "outputColumns": [
-          {
-            "attributeName": "date",
-            "attributeType": "string"
-          },
-          {
-            "attributeName": "open",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "high",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "low",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "close",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "volume",
-            "attributeType": "long"
-          }
-        ]
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": true,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Clean Data",
-      "dynamicInputPorts": true,
-      "dynamicOutputPorts": true
-    },
-    {
-      "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
-      "operatorType": "PythonUDFV2",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "code": "from typing import Iterator, Optional, Union\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pytexera import *\nfrom core.models.schema.attribute_type import AttributeType\nclass ProcessTableOperator(UDFTableOperator):\n\n    def open(self) -> None:\n        pass\n\n    @overrides\n    def process_table(self, table: Table, port: int) -> Iterator[Optional[TableLike]]:\n        transformed = table.copy()\n        transformed['date'] = pd.to_datetime(transformed['date'], errors='coerce')\n        transformed = transformed.dropna(subset=['date']).copy()\n        transformed = transformed.sort_values('date')\n        transformed['daily_return'] = transformed['close'].pct_change() * 100\n        transformed['ma_30'] = transformed['close'].rolling(window=30).mean()\n        print('\\nStep 4: Transform data')\n        print(transformed.head())\n        yield transformed\n        return\n\n    def close(self) -> None:\n        pass\n",
-        "workers": 1,
-        "retainInputColumns": false,
-        "outputColumns": [
-          {
-            "attributeName": "date",
-            "attributeType": "timestamp"
-          },
-          {
-            "attributeName": "open",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "high",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "low",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "close",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "volume",
-            "attributeType": "long"
-          },
-          {
-            "attributeName": "daily_return",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "ma_30",
-            "attributeType": "double"
-          }
-        ]
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": true,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Transform Data",
-      "dynamicInputPorts": true,
-      "dynamicOutputPorts": true
-    },
-    {
-      "operatorID": "PythonUDFV2-operator-f45cfd8f-0f39-42c6-a97a-35a0094359da",
-      "operatorType": "PythonUDFV2",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "code": "from typing import Iterator, Optional, Union\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pytexera import *\nfrom core.models.schema.attribute_type import AttributeType\nclass ProcessTableOperator(UDFTableOperator):\n\n    def open(self) -> None:\n        self.output_file = 'clean_sp500_2000.csv'\n        # Temporary hardcoded default for UI parameter 'output_file' from the source script.\n        # self.UiParameter('output_file', AttributeType.STRING)  # UiParameter disabled for now\n\n    @overrides\n    def process_table(self, table: Table, port: int) -> Iterator[Optional[TableLike]]:\n        table.to_csv(self.output_file, index=False)\n        print(f'\\nStep 5: Load data')\n        print(f'Saved cleaned data to: {self.output_file}')\n        yield table\n\n    def close(self) -> None:\n        pass\n",
-        "workers": 1,
-        "retainInputColumns": false,
-        "outputColumns": [
-          {
-            "attributeName": "date",
-            "attributeType": "timestamp"
-          },
-          {
-            "attributeName": "open",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "high",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "low",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "close",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "volume",
-            "attributeType": "long"
-          },
-          {
-            "attributeName": "daily_return",
-            "attributeType": "double"
-          },
-          {
-            "attributeName": "ma_30",
-            "attributeType": "double"
-          }
-        ]
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": true,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Load Data",
-      "dynamicInputPorts": true,
-      "dynamicOutputPorts": true
-    },
-    {
-      "operatorID": "TimeSeriesPlot-operator-4c61e814-c060-4a03-8170-2bd0c441ac8e",
-      "operatorType": "TimeSeriesPlot",
-      "operatorVersion": "N/A",
-      "operatorProperties": {
-        "timeColumn": "date",
-        "valueColumn": "close",
-        "line": "close",
-        "facetColumn": "No Selection",
-        "categoryColumn": "No Selection",
-        "slider": false
-      },
-      "inputPorts": [
-        {
-          "portID": "input-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false,
-          "dependencies": []
-        }
-      ],
-      "outputPorts": [
-        {
-          "portID": "output-0",
-          "displayName": "",
-          "allowMultiInputs": false,
-          "isDynamicPort": false
-        }
-      ],
-      "showAdvanced": false,
-      "isDisabled": false,
-      "customDisplayName": "Plot Data",
-      "dynamicInputPorts": false,
-      "dynamicOutputPorts": false,
-      "viewResult": true
-    }
-  ],
-  "operatorPositions": {
-    "CSVFileScan-operator-52a5c4c1-6d4c-4ce6-8d58-c69e991b2b15": {
-      "x": 240,
-      "y": 180
-    },
-    "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34": {
-      "x": 540,
-      "y": 180
-    },
-    "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf": {
-      "x": 840,
-      "y": 180
-    },
-    "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798": {
-      "x": 1140,
-      "y": 180
-    },
-    "PythonUDFV2-operator-f45cfd8f-0f39-42c6-a97a-35a0094359da": {
-      "x": 1440,
-      "y": 180
-    },
-    "TimeSeriesPlot-operator-4c61e814-c060-4a03-8170-2bd0c441ac8e": {
-      "x": 1440,
-      "y": 360
-    }
-  },
-  "links": [
-    {
-      "linkID": "link-84bd3626-9908-4fb1-b746-ad9da043aa92",
-      "source": {
-        "operatorID": "CSVFileScan-operator-52a5c4c1-6d4c-4ce6-8d58-c69e991b2b15",
-        "portID": "output-0"
-      },
-      "target": {
-        "operatorID": "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34",
-        "portID": "input-0"
-      }
-    },
-    {
-      "linkID": "link-49eb65dd-4c68-4df6-a245-aedb508b7857",
-      "source": {
-        "operatorID": "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34",
-        "portID": "output-0"
-      },
-      "target": {
-        "operatorID": "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf",
-        "portID": "input-0"
-      }
-    },
-    {
-      "linkID": "link-bf345bd5-6196-482e-bda1-bba512abf7da",
-      "source": {
-        "operatorID": "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf",
-        "portID": "output-0"
-      },
-      "target": {
-        "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
-        "portID": "input-0"
-      }
-    },
-    {
-      "linkID": "link-6031e10a-0ac6-42af-9321-2782a2be6d8e",
-      "source": {
-        "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
-        "portID": "output-0"
-      },
-      "target": {
-        "operatorID": "PythonUDFV2-operator-f45cfd8f-0f39-42c6-a97a-35a0094359da",
-        "portID": "input-0"
-      }
-    },
-    {
-      "linkID": "link-9b1eacbb-1a02-47b7-b14e-6c1d41964c8e",
-      "source": {
-        "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
-        "portID": "output-0"
-      },
-      "target": {
-        "operatorID": "TimeSeriesPlot-operator-4c61e814-c060-4a03-8170-2bd0c441ac8e",
-        "portID": "input-0"
-      }
-    }
-  ],
-  "commentBoxes": [],
-  "settings": {
-    "dataTransferBatchSize": 400
-  }
-}
diff --git a/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.meta.json b/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.meta.json
deleted file mode 100644
index 8136a9df4bc..00000000000
--- a/common/workflow-operator/src/main/scala/py2udf/demo_texera_workflow_vx2.workflow.meta.json
+++ /dev/null
@@ -1,398 +0,0 @@
-{
-  "entry": "main",
-  "catalog_path": "catalog.json",
-  "stages": [
-    {
-      "stage_id": "extract_data__0",
-      "function": "extract_data",
-      "operatorID": "CSVFileScan-operator-52a5c4c1-6d4c-4ce6-8d58-c69e991b2b15",
-      "operator_type": "CSVFileScan",
-      "native_operator_type": "CSVFileScan",
-      "family": "table",
-      "base_class": null,
-      "method": null,
-      "is_source": true,
-      "is_sink": true,
-      "recursive": false,
-      "has_loop": false,
-      "assigned_to": [
-        "raw_df"
-      ],
-      "data_inputs": [],
-      "ui_parameters": [
-        {
-          "name": "url",
-          "attr_type": "AttributeType.STRING",
-          "default": "'https://raw.githubusercontent.com/vega/vega-datasets/main/data/sp500-2000.csv'"
-        }
-      ],
-      "source_columns": [
-        {
-          "attributeName": "date",
-          "attributeType": "string"
-        },
-        {
-          "attributeName": "open",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "high",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "low",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "close",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "adjclose",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "volume",
-          "attributeType": "long"
-        }
-      ],
-      "output_columns": [
-        {
-          "attributeName": "date",
-          "attributeType": "string"
-        },
-        {
-          "attributeName": "open",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "high",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "low",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "close",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "adjclose",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "volume",
-          "attributeType": "long"
-        }
-      ],
-      "retain_input_columns": false,
-      "native_notes": [
-        "Matched pandas.read_csv to CSVFileScan"
-      ]
-    },
-    {
-      "stage_id": "select_columns__1",
-      "function": "select_columns",
-      "operatorID": "PythonUDFV2-operator-da9d7c0b-301c-4074-9fe4-262c29fd2b34",
-      "operator_type": "PythonUDFV2",
-      "native_operator_type": null,
-      "family": "table",
-      "base_class": "UDFTableOperator",
-      "method": "process_table",
-      "is_source": false,
-      "is_sink": true,
-      "recursive": false,
-      "has_loop": false,
-      "assigned_to": [
-        "selected_df"
-      ],
-      "data_inputs": [
-        {
-          "param_name": "df",
-          "upstream_stage_id": "extract_data__0",
-          "upstream_var": "raw_df",
-          "input_port": 0
-        }
-      ],
-      "ui_parameters": [],
-      "source_columns": [],
-      "output_columns": [
-        {
-          "attributeName": "date",
-          "attributeType": "string"
-        },
-        {
-          "attributeName": "open",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "high",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "low",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "close",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "volume",
-          "attributeType": "long"
-        }
-      ],
-      "retain_input_columns": false,
-      "native_notes": []
-    },
-    {
-      "stage_id": "clean_data__2",
-      "function": "clean_data",
-      "operatorID": "PythonUDFV2-operator-9763cabd-b5c1-46ca-97de-7de4796f11bf",
-      "operator_type": "PythonUDFV2",
-      "native_operator_type": null,
-      "family": "table",
-      "base_class": "UDFTableOperator",
-      "method": "process_table",
-      "is_source": false,
-      "is_sink": true,
-      "recursive": false,
-      "has_loop": false,
-      "assigned_to": [
-        "cleaned_df"
-      ],
-      "data_inputs": [
-        {
-          "param_name": "df",
-          "upstream_stage_id": "select_columns__1",
-          "upstream_var": "selected_df",
-          "input_port": 0
-        }
-      ],
-      "ui_parameters": [],
-      "source_columns": [],
-      "output_columns": [
-        {
-          "attributeName": "date",
-          "attributeType": "string"
-        },
-        {
-          "attributeName": "open",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "high",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "low",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "close",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "volume",
-          "attributeType": "long"
-        }
-      ],
-      "retain_input_columns": false,
-      "native_notes": []
-    },
-    {
-      "stage_id": "transform_data__3",
-      "function": "transform_data",
-      "operatorID": "PythonUDFV2-operator-cdae744a-1e5e-4e8f-bd77-10434620f798",
-      "operator_type": "PythonUDFV2",
-      "native_operator_type": null,
-      "family": "table",
-      "base_class": "UDFTableOperator",
-      "method": "process_table",
-      "is_source": false,
-      "is_sink": true,
-      "recursive": false,
-      "has_loop": false,
-      "assigned_to": [
-        "transformed_df"
-      ],
-      "data_inputs": [
-        {
-          "param_name": "df",
-          "upstream_stage_id": "clean_data__2",
-          "upstream_var": "cleaned_df",
-          "input_port": 0
-        }
-      ],
-      "ui_parameters": [],
-      "source_columns": [],
-      "output_columns": [
-        {
-          "attributeName": "date",
-          "attributeType": "timestamp"
-        },
-        {
-          "attributeName": "open",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "high",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "low",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "close",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "volume",
-          "attributeType": "long"
-        },
-        {
-          "attributeName": "daily_return",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "ma_30",
-          "attributeType": "double"
-        }
-      ],
-      "retain_input_columns": false,
-      "native_notes": []
-    },
-    {
-      "stage_id": "load_data__4",
-      "function": "load_data",
-      "operatorID": "PythonUDFV2-operator-f45cfd8f-0f39-42c6-a97a-35a0094359da",
-      "operator_type": "PythonUDFV2",
-      "native_operator_type": null,
-      "family": "table",
-      "base_class": "UDFTableOperator",
-      "method": "process_table",
-      "is_source": false,
-      "is_sink": true,
-      "recursive": false,
-      "has_loop": false,
-      "assigned_to": [],
-      "data_inputs": [
-        {
-          "param_name": "df",
-          "upstream_stage_id": "transform_data__3",
-          "upstream_var": "transformed_df",
-          "input_port": 0
-        }
-      ],
-      "ui_parameters": [
-        {
-          "name": "output_file",
-          "attr_type": "AttributeType.STRING",
-          "default": "'clean_sp500_2000.csv'"
-        }
-      ],
-      "source_columns": [],
-      "output_columns": [
-        {
-          "attributeName": "date",
-          "attributeType": "timestamp"
-        },
-        {
-          "attributeName": "open",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "high",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "low",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "close",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "volume",
-          "attributeType": "long"
-        },
-        {
-          "attributeName": "daily_return",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "ma_30",
-          "attributeType": "double"
-        }
-      ],
-      "retain_input_columns": false,
-      "native_notes": []
-    },
-    {
-      "stage_id": "plot_data__5",
-      "function": "plot_data",
-      "operatorID": "TimeSeriesPlot-operator-4c61e814-c060-4a03-8170-2bd0c441ac8e",
-      "operator_type": "TimeSeriesPlot",
-      "native_operator_type": "TimeSeriesPlot",
-      "family": "table",
-      "base_class": null,
-      "method": null,
-      "is_source": false,
-      "is_sink": true,
-      "recursive": false,
-      "has_loop": false,
-      "assigned_to": [],
-      "data_inputs": [
-        {
-          "param_name": "df",
-          "upstream_stage_id": "transform_data__3",
-          "upstream_var": "transformed_df",
-          "input_port": 0
-        }
-      ],
-      "ui_parameters": [],
-      "source_columns": [],
-      "output_columns": [
-        {
-          "attributeName": "date",
-          "attributeType": "timestamp"
-        },
-        {
-          "attributeName": "open",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "high",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "low",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "close",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "volume",
-          "attributeType": "long"
-        },
-        {
-          "attributeName": "daily_return",
-          "attributeType": "double"
-        },
-        {
-          "attributeName": "ma_30",
-          "attributeType": "double"
-        }
-      ],
-      "retain_input_columns": true,
-      "native_notes": [
-        "Matched line/time-series plot on x='date', y='close'"
-      ]
-    }
-  ]
-}
diff --git a/common/workflow-operator/src/main/scala/py2udf/py2udf.ipynb b/common/workflow-operator/src/main/scala/py2udf/py2udf.ipynb
deleted file mode 100644
index b5125c6b556..00000000000
--- a/common/workflow-operator/src/main/scala/py2udf/py2udf.ipynb
+++ /dev/null
@@ -1,2028 +0,0 @@
-{
- "cells": [
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T07:52:53.906136Z",
-     "start_time": "2026-03-04T07:52:52.994657Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import ast\n",
-    "import copy\n",
-    "import json\n",
-    "import uuid\n",
-    "from dataclasses import dataclass, field\n",
-    "from pathlib import Path\n",
-    "from typing import Any, Optional\n",
-    "\n",
-    "\n",
-    "ATTRIBUTE_TYPE_FOR_PY = {\n",
-    "    \"str\": \"AttributeType.STRING\",\n",
-    "    \"int\": \"AttributeType.INTEGER\",\n",
-    "    \"float\": \"AttributeType.DOUBLE\",\n",
-    "    \"bool\": \"AttributeType.BOOLEAN\",\n",
-    "}\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class FunctionFacts:\n",
-    "    name: str\n",
-    "    params: list[str]\n",
-    "    returns_annotation: str | None\n",
-    "    called_functions: set[str] = field(default_factory=set)\n",
-    "    has_loop: bool = False\n",
-    "    recursive: bool = False\n",
-    "    family: str = \"table\"  # source | table | batch | tuple\n",
-    "    is_sink: bool = False\n",
-    "    globals_used: set[str] = field(default_factory=set)\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class UiParamSpec:\n",
-    "    name: str\n",
-    "    attr_type: str\n",
-    "    default_ast: ast.AST | None = None\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class DataInputSpec:\n",
-    "    param_name: str\n",
-    "    upstream_stage_id: str\n",
-    "    upstream_var: str\n",
-    "    input_port: int\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class StageInvocation:\n",
-    "    stage_id: str\n",
-    "    function_name: str\n",
-    "    family: str\n",
-    "    is_source: bool\n",
-    "    is_sink: bool\n",
-    "    recursive: bool\n",
-    "    has_loop: bool\n",
-    "    assigned_to: list[str]\n",
-    "    data_inputs: list[DataInputSpec] = field(default_factory=list)\n",
-    "    ui_params: list[UiParamSpec] = field(default_factory=list)\n",
-    "    operator_id: str = \"\"\n",
-    "    display_name: str = \"\"\n",
-    "    code: str = \"\"\n",
-    "    operator_type: str = \"PythonUDFV2\"\n",
-    "    source_columns: list[dict[str, str]] = field(default_factory=list)\n",
-    "    output_columns: list[dict[str, str]] = field(default_factory=list)\n",
-    "    retain_input_columns: bool = True\n",
-    "\n",
-    "\n",
-    "class SourceModuleParser:\n",
-    "    def parse(self, source: str) -> ast.Module:\n",
-    "        return ast.parse(source)\n",
-    "\n",
-    "\n",
-    "class ImportCollector:\n",
-    "    def collect_nodes(self, tree: ast.Module) -> list[ast.stmt]:\n",
-    "        return [n for n in tree.body if isinstance(n, (ast.Import, ast.ImportFrom))]\n",
-    "\n",
-    "    def collect_names(self, tree: ast.Module) -> set[str]:\n",
-    "        names = set()\n",
-    "        for node in self.collect_nodes(tree):\n",
-    "            if isinstance(node, ast.Import):\n",
-    "                for alias in node.names:\n",
-    "                    names.add(alias.asname or alias.name.split(\".\")[0])\n",
-    "            else:\n",
-    "                for alias in node.names:\n",
-    "                    names.add(alias.asname or alias.name)\n",
-    "        return names\n",
-    "\n",
-    "\n",
-    "class FunctionIndex:\n",
-    "    def collect(self, tree: ast.Module) -> dict[str, ast.FunctionDef]:\n",
-    "        return {n.name: n for n in tree.body if isinstance(n, ast.FunctionDef)}\n",
-    "\n",
-    "\n",
-    "class CallCollector(ast.NodeVisitor):\n",
-    "    def __init__(self):\n",
-    "        self.calls: list[ast.Call] = []\n",
-    "\n",
-    "    def visit_Call(self, node: ast.Call):\n",
-    "        self.calls.append(node)\n",
-    "        self.generic_visit(node)\n",
-    "\n",
-    "\n",
-    "class CallGraphBuilder:\n",
-    "    def build(self, functions: dict[str, ast.FunctionDef]) -> dict[str, set[str]]:\n",
-    "        graph = {name: set() for name in functions}\n",
-    "        for name, fn in functions.items():\n",
-    "            collector = CallCollector()\n",
-    "            collector.visit(fn)\n",
-    "            for call in collector.calls:\n",
-    "                if isinstance(call.func, ast.Name) and call.func.id in functions:\n",
-    "                    graph[name].add(call.func.id)\n",
-    "        return graph\n",
-    "\n",
-    "\n",
-    "class RecursiveAnalyzer:\n",
-    "    def strongly_connected_components(self, graph: dict[str, set[str]]) -> list[list[str]]:\n",
-    "        index = 0\n",
-    "        stack: list[str] = []\n",
-    "        on_stack: set[str] = set()\n",
-    "        indices: dict[str, int] = {}\n",
-    "        lowlink: dict[str, int] = {}\n",
-    "        comps: list[list[str]] = []\n",
-    "\n",
-    "        def strongconnect(v: str):\n",
-    "            nonlocal index\n",
-    "            indices[v] = index\n",
-    "            lowlink[v] = index\n",
-    "            index += 1\n",
-    "            stack.append(v)\n",
-    "            on_stack.add(v)\n",
-    "\n",
-    "            for w in graph[v]:\n",
-    "                if w not in indices:\n",
-    "                    strongconnect(w)\n",
-    "                    lowlink[v] = min(lowlink[v], lowlink[w])\n",
-    "                elif w in on_stack:\n",
-    "                    lowlink[v] = min(lowlink[v], indices[w])\n",
-    "\n",
-    "            if lowlink[v] == indices[v]:\n",
-    "                comp = []\n",
-    "                while True:\n",
-    "                    w = stack.pop()\n",
-    "                    on_stack.remove(w)\n",
-    "                    comp.append(w)\n",
-    "                    if w == v:\n",
-    "                        break\n",
-    "                comps.append(comp)\n",
-    "\n",
-    "        for v in graph:\n",
-    "            if v not in indices:\n",
-    "                strongconnect(v)\n",
-    "        return comps\n",
-    "\n",
-    "    def detect(self, graph: dict[str, set[str]]) -> set[str]:\n",
-    "        recursive = set()\n",
-    "        for comp in self.strongly_connected_components(graph):\n",
-    "            if len(comp) > 1:\n",
-    "                recursive.update(comp)\n",
-    "            elif len(comp) == 1 and comp[0] in graph[comp[0]]:\n",
-    "                recursive.add(comp[0])\n",
-    "        return recursive\n",
-    "\n",
-    "\n",
-    "class NameUsageCollector(ast.NodeVisitor):\n",
-    "    def __init__(self):\n",
-    "        self.loads: set[str] = set()\n",
-    "        self.stores: set[str] = set()\n",
-    "\n",
-    "    def visit_Name(self, node: ast.Name):\n",
-    "        if isinstance(node.ctx, ast.Load):\n",
-    "            self.loads.add(node.id)\n",
-    "        elif isinstance(node.ctx, ast.Store):\n",
-    "            self.stores.add(node.id)\n",
-    "\n",
-    "    def visit_FunctionDef(self, node):\n",
-    "        return\n",
-    "\n",
-    "    def visit_ClassDef(self, node):\n",
-    "        return\n",
-    "\n",
-    "\n",
-    "class FamilyInferer:\n",
-    "    def infer(self, fn: ast.FunctionDef) -> str:\n",
-    "        ann_texts = []\n",
-    "        for a in fn.args.args:\n",
-    "            if a.annotation is not None:\n",
-    "                ann_texts.append(ast.unparse(a.annotation))\n",
-    "        if fn.returns is not None:\n",
-    "            ann_texts.append(ast.unparse(fn.returns))\n",
-    "        joined = \" | \".join(ann_texts)\n",
-    "\n",
-    "        if any(tok in joined for tok in [\"TupleLike\", \"Tuple\", \"tuple_\"]):\n",
-    "            return \"tuple\"\n",
-    "        if any(tok in joined for tok in [\"BatchLike\", \"Batch\"]):\n",
-    "            return \"batch\"\n",
-    "        if any(tok in joined for tok in [\"DataFrame\", \"TableLike\", \"Table\", \"pd.DataFrame\"]):\n",
-    "            return \"table\"\n",
-    "\n",
-    "        source = ast.unparse(fn)\n",
-    "        if any(tok in source for tok in [\"pd.\", \".copy(\", \".dropna(\", \".sort_values(\", \".rolling(\"]):\n",
-    "            return \"table\"\n",
-    "        return \"table\"\n",
-    "\n",
-    "\n",
-    "class SinkDetector:\n",
-    "    def detect(self, fn: ast.FunctionDef) -> bool:\n",
-    "        source = ast.unparse(fn)\n",
-    "        if any(tok in source for tok in [\"to_csv(\", \".show(\", \"plt.\", \".save(\", \"print(\"]):\n",
-    "            return True\n",
-    "        if fn.returns is None:\n",
-    "            for stmt in fn.body:\n",
-    "                if isinstance(stmt, ast.Return) and stmt.value is not None:\n",
-    "                    return False\n",
-    "            return True\n",
-    "        return False\n",
-    "\n",
-    "\n",
-    "class LoopCollector(ast.NodeVisitor):\n",
-    "    def __init__(self, function_name: str):\n",
-    "        self.function_name = function_name\n",
-    "        self.has_loop = False\n",
-    "\n",
-    "    def visit_For(self, node: ast.For):\n",
-    "        self.has_loop = True\n",
-    "        self.generic_visit(node)\n",
-    "\n",
-    "    def visit_While(self, node: ast.While):\n",
-    "        self.has_loop = True\n",
-    "        self.generic_visit(node)\n",
-    "\n",
-    "\n",
-    "class FunctionFactsAnalyzer:\n",
-    "    def __init__(self):\n",
-    "        self.family_inferer = FamilyInferer()\n",
-    "        self.sink_detector = SinkDetector()\n",
-    "\n",
-    "    def analyze(\n",
-    "        self,\n",
-    "        tree: ast.Module,\n",
-    "        functions: dict[str, ast.FunctionDef],\n",
-    "        graph: dict[str, set[str]],\n",
-    "        recursive: set[str],\n",
-    "    ) -> dict[str, FunctionFacts]:\n",
-    "        import_names = ImportCollector().collect_names(tree)\n",
-    "        facts: dict[str, FunctionFacts] = {}\n",
-    "        for name, fn in functions.items():\n",
-    "            usage = NameUsageCollector()\n",
-    "            usage.visit(fn)\n",
-    "            loops = LoopCollector(name)\n",
-    "            loops.visit(fn)\n",
-    "            facts[name] = FunctionFacts(\n",
-    "                name=name,\n",
-    "                params=[a.arg for a in fn.args.args],\n",
-    "                returns_annotation=ast.unparse(fn.returns) if fn.returns is not None else None,\n",
-    "                called_functions=set(graph[name]),\n",
-    "                has_loop=loops.has_loop,\n",
-    "                recursive=name in recursive,\n",
-    "                family=self.family_inferer.infer(fn),\n",
-    "                is_sink=self.sink_detector.detect(fn),\n",
-    "                globals_used=(usage.loads - usage.stores - {a.arg for a in fn.args.args}) & import_names,\n",
-    "            )\n",
-    "        return facts\n",
-    "\n",
-    "\n",
-    "class SourceSchemaInferer:\n",
-    "    def __init__(self):\n",
-    "        try:\n",
-    "            import pandas as _pd\n",
-    "        except Exception:\n",
-    "            _pd = None\n",
-    "        self.pd = _pd\n",
-    "\n",
-    "    def _literal_value(self, node: ast.AST | None):\n",
-    "        if isinstance(node, ast.Constant):\n",
-    "            return node.value\n",
-    "        return None\n",
-    "\n",
-    "    def _resolve_expr(self, expr: ast.AST | None, ui_defaults: dict[str, Any]):\n",
-    "        if expr is None:\n",
-    "            return None\n",
-    "        if isinstance(expr, ast.Constant):\n",
-    "            return expr.value\n",
-    "        if isinstance(expr, ast.Name):\n",
-    "            return ui_defaults.get(expr.id)\n",
-    "        return None\n",
-    "\n",
-    "    def _map_dtype(self, dtype: Any) -> str:\n",
-    "        kind = getattr(dtype, \"kind\", None)\n",
-    "        if kind == \"b\":\n",
-    "            return \"boolean\"\n",
-    "        if kind == \"i\":\n",
-    "            try:\n",
-    "                itemsize = int(getattr(dtype, \"itemsize\", 8) or 8)\n",
-    "            except Exception:\n",
-    "                itemsize = 8\n",
-    "            return \"integer\" if itemsize <= 4 else \"long\"\n",
-    "        if kind == \"u\":\n",
-    "            try:\n",
-    "                itemsize = int(getattr(dtype, \"itemsize\", 8) or 8)\n",
-    "            except Exception:\n",
-    "                itemsize = 8\n",
-    "            return \"integer\" if itemsize <= 4 else \"long\"\n",
-    "        if kind == \"f\":\n",
-    "            return \"double\"\n",
-    "        if kind == \"M\":\n",
-    "            return \"timestamp\"\n",
-    "        if kind == \"S\":\n",
-    "            return \"binary\"\n",
-    "        return \"string\"\n",
-    "\n",
-    "    def infer(self, fn: ast.FunctionDef, stage: StageInvocation) -> list[dict[str, str]]:\n",
-    "        if not stage.is_source or stage.family != \"table\" or self.pd is None:\n",
-    "            return []\n",
-    "\n",
-    "        ui_defaults = {spec.name: self._literal_value(spec.default_ast) for spec in stage.ui_params}\n",
-    "\n",
-    "        for node in ast.walk(fn):\n",
-    "            if not isinstance(node, ast.Call):\n",
-    "                continue\n",
-    "            if not isinstance(node.func, ast.Attribute):\n",
-    "                continue\n",
-    "            if not (isinstance(node.func.value, ast.Name) and node.func.value.id == \"pd\" and node.func.attr == \"read_csv\"):\n",
-    "                continue\n",
-    "\n",
-    "            source_expr = None\n",
-    "            if node.args:\n",
-    "                source_expr = node.args[0]\n",
-    "            else:\n",
-    "                for kw in node.keywords:\n",
-    "                    if kw.arg in (\"filepath_or_buffer\", \"path\", \"url\"):\n",
-    "                        source_expr = kw.value\n",
-    "                        break\n",
-    "\n",
-    "            source_value = self._resolve_expr(source_expr, ui_defaults)\n",
-    "            if not isinstance(source_value, str) or not source_value:\n",
-    "                continue\n",
-    "\n",
-    "            try:\n",
-    "                df = self.pd.read_csv(source_value, nrows=50)\n",
-    "            except Exception:\n",
-    "                continue\n",
-    "\n",
-    "            cols: list[dict[str, str]] = []\n",
-    "            for col in df.columns:\n",
-    "                try:\n",
-    "                    dtype = df[col].dtype\n",
-    "                except Exception:\n",
-    "                    dtype = None\n",
-    "                cols.append({\n",
-    "                    \"attributeName\": str(col),\n",
-    "                    \"attributeType\": self._map_dtype(dtype),\n",
-    "                })\n",
-    "            if cols:\n",
-    "                return cols\n",
-    "\n",
-    "        return []\n",
-    "\n",
-    "\n",
-    "class TableOutputSchemaInferer:\n",
-    "    def _schema_dict(self, cols: list[dict[str, str]]) -> dict[str, str]:\n",
-    "        return {c[\"attributeName\"]: c[\"attributeType\"] for c in cols if c.get(\"attributeName\")}\n",
-    "\n",
-    "    def _schema_list(self, d: dict[str, str]) -> list[dict[str, str]]:\n",
-    "        return [{\"attributeName\": k, \"attributeType\": v} for k, v in d.items()]\n",
-    "\n",
-    "    def _list_of_constant_strings(self, node: ast.AST | None) -> list[str] | None:\n",
-    "        if isinstance(node, (ast.List, ast.Tuple)):\n",
-    "            vals = []\n",
-    "            for elt in node.elts:\n",
-    "                if isinstance(elt, ast.Constant) and isinstance(elt.value, str):\n",
-    "                    vals.append(elt.value)\n",
-    "                else:\n",
-    "                    return None\n",
-    "            return vals\n",
-    "        return None\n",
-    "\n",
-    "    def _infer_scalar_type(self, expr: ast.AST, env: dict[str, dict[str, str]]) -> str:\n",
-    "        if isinstance(expr, ast.Constant):\n",
-    "            v = expr.value\n",
-    "            if isinstance(v, bool):\n",
-    "                return \"boolean\"\n",
-    "            if isinstance(v, int):\n",
-    "                return \"long\"\n",
-    "            if isinstance(v, float):\n",
-    "                return \"double\"\n",
-    "            return \"string\"\n",
-    "        if isinstance(expr, ast.Subscript) and isinstance(expr.value, ast.Name):\n",
-    "            if isinstance(expr.slice, ast.Constant) and isinstance(expr.slice.value, str):\n",
-    "                return env.get(expr.value.id, {}).get(expr.slice.value, \"string\")\n",
-    "        if isinstance(expr, ast.BinOp):\n",
-    "            return \"double\"\n",
-    "        if isinstance(expr, ast.Call) and isinstance(expr.func, ast.Attribute):\n",
-    "            attr = expr.func.attr\n",
-    "            if isinstance(expr.func.value, ast.Name) and expr.func.value.id == \"pd\" and attr == \"to_datetime\":\n",
-    "                return \"timestamp\"\n",
-    "            if attr in {\"pct_change\", \"mean\", \"sum\", \"std\", \"median\", \"max\", \"min\", \"rolling\"}:\n",
-    "                return \"double\"\n",
-    "        return \"string\"\n",
-    "\n",
-    "    def _resolve_table_schema(self, expr: ast.AST, env: dict[str, dict[str, str]]) -> dict[str, str] | None:\n",
-    "        if isinstance(expr, ast.Name):\n",
-    "            schema = env.get(expr.id)\n",
-    "            return dict(schema) if schema is not None else None\n",
-    "        if isinstance(expr, ast.Subscript):\n",
-    "            base = self._resolve_table_schema(expr.value, env)\n",
-    "            cols = self._list_of_constant_strings(expr.slice)\n",
-    "            if base is not None and cols is not None:\n",
-    "                return {c: base[c] for c in cols if c in base}\n",
-    "            return None\n",
-    "        if isinstance(expr, ast.Call) and isinstance(expr.func, ast.Attribute):\n",
-    "            base = self._resolve_table_schema(expr.func.value, env)\n",
-    "            attr = expr.func.attr\n",
-    "            if base is None:\n",
-    "                return None\n",
-    "            if attr in {\"copy\", \"dropna\", \"sort_values\", \"reset_index\", \"rename_axis\", \"fillna\", \"head\", \"tail\"}:\n",
-    "                return dict(base)\n",
-    "            if attr == \"assign\":\n",
-    "                out = dict(base)\n",
-    "                for kw in expr.keywords:\n",
-    "                    if kw.arg:\n",
-    "                        out[kw.arg] = self._infer_scalar_type(kw.value, env)\n",
-    "                return out\n",
-    "            return dict(base)\n",
-    "        return None\n",
-    "\n",
-    "    def infer(self, fn: ast.FunctionDef, stage: StageInvocation, input_columns: list[dict[str, str]]) -> tuple[list[dict[str, str]], bool]:\n",
-    "        if stage.family != \"table\":\n",
-    "            return [], True\n",
-    "        env: dict[str, dict[str, str]] = {}\n",
-    "        if stage.data_inputs:\n",
-    "            if len(stage.data_inputs) == 1:\n",
-    "                env[stage.data_inputs[0].param_name] = self._schema_dict(input_columns)\n",
-    "            else:\n",
-    "                merged: dict[str, str] = {}\n",
-    "                for spec in stage.data_inputs:\n",
-    "                    for c in input_columns:\n",
-    "                        if c.get(\"attributeName\"):\n",
-    "                            merged[c[\"attributeName\"]] = c[\"attributeType\"]\n",
-    "                    env[spec.param_name] = dict(merged)\n",
-    "        elif stage.is_source and stage.source_columns:\n",
-    "            # Seed read_csv-assigned variables lazily below.\n",
-    "            pass\n",
-    "\n",
-    "        return_schema: dict[str, str] | None = None\n",
-    "        for stmt in fn.body:\n",
-    "            if isinstance(stmt, ast.Assign):\n",
-    "                value_schema = self._resolve_table_schema(stmt.value, env)\n",
-    "                if value_schema is not None:\n",
-    "                    for t in stmt.targets:\n",
-    "                        if isinstance(t, ast.Name):\n",
-    "                            env[t.id] = dict(value_schema)\n",
-    "                for t in stmt.targets:\n",
-    "                    if isinstance(t, ast.Subscript) and isinstance(t.value, ast.Name):\n",
-    "                        root = t.value.id\n",
-    "                        if root not in env:\n",
-    "                            continue\n",
-    "                        if isinstance(t.slice, ast.Constant) and isinstance(t.slice.value, str):\n",
-    "                            env[root][t.slice.value] = self._infer_scalar_type(stmt.value, env)\n",
-    "            elif isinstance(stmt, ast.Return) and stmt.value is not None:\n",
-    "                rs = self._resolve_table_schema(stmt.value, env)\n",
-    "                if rs is not None:\n",
-    "                    return_schema = rs\n",
-    "\n",
-    "        if return_schema is None:\n",
-    "            if stage.is_source:\n",
-    "                return_schema = self._schema_dict(stage.source_columns)\n",
-    "            elif len(stage.data_inputs) == 1:\n",
-    "                return_schema = self._schema_dict(input_columns)\n",
-    "            else:\n",
-    "                return_schema = {}\n",
-    "                for c in input_columns:\n",
-    "                    if c.get(\"attributeName\"):\n",
-    "                        return_schema[c[\"attributeName\"]] = c[\"attributeType\"]\n",
-    "\n",
-    "        input_schema_dict = self._schema_dict(input_columns)\n",
-    "        retain = return_schema == input_schema_dict\n",
-    "        # Emit explicit output columns for all table stages to keep runtime schema aligned.\n",
-    "        return self._schema_list(return_schema), False if return_schema else retain\n",
-    "\n",
-    "\n",
-    "class WorkflowIdFactory:\n",
-    "    def operator_id(self, operator_type: str = \"PythonUDFV2\") -> str:\n",
-    "        return f\"{operator_type}-operator-{uuid.uuid4()}\"\n",
-    "\n",
-    "    def link_id(self) -> str:\n",
-    "        return f\"link-{uuid.uuid4()}\"\n",
-    "\n",
-    "    def stage_id(self, base: str, idx: int) -> str:\n",
-    "        return f\"{base}__{idx}\"\n",
-    "\n",
-    "\n",
-    "class ConstantEnvBuilder:\n",
-    "    def build(self, entry_fn: ast.FunctionDef) -> dict[str, ast.AST]:\n",
-    "        env: dict[str, ast.AST] = {}\n",
-    "        for stmt in entry_fn.body:\n",
-    "            if isinstance(stmt, ast.Assign) and isinstance(stmt.value, ast.Constant):\n",
-    "                for t in stmt.targets:\n",
-    "                    if isinstance(t, ast.Name):\n",
-    "                        env[t.id] = copy.deepcopy(stmt.value)\n",
-    "        return env\n",
-    "\n",
-    "\n",
-    "class AttributeTypeInferer:\n",
-    "    def from_annotation(self, annotation: ast.AST | None) -> str:\n",
-    "        if annotation is None:\n",
-    "            return \"AttributeType.STRING\"\n",
-    "        text = ast.unparse(annotation)\n",
-    "        if text in (\"str\", \"Optional[str]\"):\n",
-    "            return \"AttributeType.STRING\"\n",
-    "        if text in (\"int\", \"Optional[int]\"):\n",
-    "            return \"AttributeType.INTEGER\"\n",
-    "        if text in (\"float\", \"Optional[float]\"):\n",
-    "            return \"AttributeType.DOUBLE\"\n",
-    "        if text in (\"bool\", \"Optional[bool]\"):\n",
-    "            return \"AttributeType.BOOLEAN\"\n",
-    "        return \"AttributeType.STRING\"\n",
-    "\n",
-    "    def from_value(self, node: ast.AST | None) -> str:\n",
-    "        if isinstance(node, ast.Constant):\n",
-    "            py_name = type(node.value).__name__\n",
-    "            return ATTRIBUTE_TYPE_FOR_PY.get(py_name, \"AttributeType.STRING\")\n",
-    "        return \"AttributeType.STRING\"\n",
-    "\n",
-    "\n",
-    "class EntryDagExtractor:\n",
-    "    def __init__(self):\n",
-    "        self.type_inferer = AttributeTypeInferer()\n",
-    "        self.ids = WorkflowIdFactory()\n",
-    "\n",
-    "    def _assigned_names(self, stmt: ast.stmt) -> list[str]:\n",
-    "        names: list[str] = []\n",
-    "        if isinstance(stmt, ast.Assign):\n",
-    "            for t in stmt.targets:\n",
-    "                if isinstance(t, ast.Name):\n",
-    "                    names.append(t.id)\n",
-    "                elif isinstance(t, (ast.Tuple, ast.List)):\n",
-    "                    for elt in t.elts:\n",
-    "                        if isinstance(elt, ast.Name):\n",
-    "                            names.append(elt.id)\n",
-    "        return names\n",
-    "\n",
-    "    def extract(\n",
-    "        self,\n",
-    "        entry_fn: ast.FunctionDef,\n",
-    "        functions: dict[str, ast.FunctionDef],\n",
-    "        facts: dict[str, FunctionFacts],\n",
-    "        constant_env: dict[str, ast.AST],\n",
-    "    ) -> list[StageInvocation]:\n",
-    "        stages: list[StageInvocation] = []\n",
-    "        producer_for_var: dict[str, str] = {}\n",
-    "        stage_index = 0\n",
-    "\n",
-    "        for stmt in entry_fn.body:\n",
-    "            call = None\n",
-    "            if isinstance(stmt, ast.Assign) and isinstance(stmt.value, ast.Call):\n",
-    "                call = stmt.value\n",
-    "            elif isinstance(stmt, ast.Expr) and isinstance(stmt.value, ast.Call):\n",
-    "                call = stmt.value\n",
-    "            if not call or not isinstance(call.func, ast.Name) or call.func.id not in functions or call.func.id == entry_fn.name:\n",
-    "                continue\n",
-    "\n",
-    "            fn_name = call.func.id\n",
-    "            fn = functions[fn_name]\n",
-    "            fact = facts[fn_name]\n",
-    "            assigned_to = self._assigned_names(stmt)\n",
-    "            data_inputs: list[DataInputSpec] = []\n",
-    "            ui_params: list[UiParamSpec] = []\n",
-    "\n",
-    "            for i, param in enumerate(fn.args.args):\n",
-    "                arg_node = None\n",
-    "                if i < len(call.args):\n",
-    "                    arg_node = call.args[i]\n",
-    "                else:\n",
-    "                    # support exact-name keyword arguments\n",
-    "                    for kw in call.keywords:\n",
-    "                        if kw.arg == param.arg:\n",
-    "                            arg_node = kw.value\n",
-    "                            break\n",
-    "\n",
-    "                if arg_node is None:\n",
-    "                    continue\n",
-    "\n",
-    "                if isinstance(arg_node, ast.Name) and arg_node.id in producer_for_var:\n",
-    "                    data_inputs.append(DataInputSpec(\n",
-    "                        param_name=param.arg,\n",
-    "                        upstream_stage_id=producer_for_var[arg_node.id],\n",
-    "                        upstream_var=arg_node.id,\n",
-    "                        input_port=len(data_inputs),\n",
-    "                    ))\n",
-    "                else:\n",
-    "                    default_ast = copy.deepcopy(constant_env[arg_node.id]) if isinstance(arg_node, ast.Name) and arg_node.id in constant_env else copy.deepcopy(arg_node)\n",
-    "                    attr_type = self.type_inferer.from_value(default_ast)\n",
-    "                    if attr_type == \"AttributeType.STRING\":\n",
-    "                        attr_type = self.type_inferer.from_annotation(param.annotation) or attr_type\n",
-    "                    ui_params.append(UiParamSpec(name=param.arg, attr_type=attr_type, default_ast=default_ast))\n",
-    "\n",
-    "            is_source = len(data_inputs) == 0\n",
-    "            stage = StageInvocation(\n",
-    "                stage_id=self.ids.stage_id(fn_name, stage_index),\n",
-    "                function_name=fn_name,\n",
-    "                family=fact.family,\n",
-    "                is_source=is_source,\n",
-    "                is_sink=fact.is_sink,\n",
-    "                recursive=fact.recursive,\n",
-    "                has_loop=fact.has_loop,\n",
-    "                assigned_to=assigned_to,\n",
-    "                data_inputs=data_inputs,\n",
-    "                ui_params=ui_params,\n",
-    "            )\n",
-    "            stages.append(stage)\n",
-    "            for name in assigned_to:\n",
-    "                producer_for_var[name] = stage.stage_id\n",
-    "            stage_index += 1\n",
-    "\n",
-    "        return stages\n",
-    "\n",
-    "\n",
-    "class ParamRewriter(ast.NodeTransformer):\n",
-    "    def __init__(self, name_mapping: dict[str, ast.AST]):\n",
-    "        self.name_mapping = name_mapping\n",
-    "\n",
-    "    def visit_Name(self, node: ast.Name):\n",
-    "        if isinstance(node.ctx, ast.Load) and node.id in self.name_mapping:\n",
-    "            return ast.copy_location(copy.deepcopy(self.name_mapping[node.id]), node)\n",
-    "        return node\n",
-    "\n",
-    "\n",
-    "class YieldTransformer:\n",
-    "    def __init__(self, family: str, is_source: bool, is_sink: bool, passthrough_name: str):\n",
-    "        self.family = family\n",
-    "        self.is_source = is_source\n",
-    "        self.is_sink = is_sink\n",
-    "        self.passthrough_name = passthrough_name\n",
-    "\n",
-    "    def _yield_stmt(self, value: ast.AST | None) -> ast.stmt:\n",
-    "        return ast.Expr(value=ast.Yield(value=value))\n",
-    "\n",
-    "    def transform_block(self, body: list[ast.stmt], top_level: bool = False) -> list[ast.stmt]:\n",
-    "        new_body: list[ast.stmt] = []\n",
-    "        for stmt in body:\n",
-    "            if isinstance(stmt, ast.Return):\n",
-    "                if stmt.value is not None:\n",
-    "                    new_body.append(self._yield_stmt(stmt.value))\n",
-    "                elif top_level and (not self.is_source) and self.is_sink:\n",
-    "                    new_body.append(self._yield_stmt(ast.Name(id=self.passthrough_name, ctx=ast.Load())))\n",
-    "                new_body.append(ast.Return(value=None))\n",
-    "            elif isinstance(stmt, ast.If):\n",
-    "                stmt.body = self.transform_block(stmt.body, top_level=False)\n",
-    "                stmt.orelse = self.transform_block(stmt.orelse, top_level=False)\n",
-    "                new_body.append(stmt)\n",
-    "            elif isinstance(stmt, ast.For):\n",
-    "                stmt.body = self.transform_block(stmt.body, top_level=False)\n",
-    "                stmt.orelse = self.transform_block(stmt.orelse, top_level=False)\n",
-    "                new_body.append(stmt)\n",
-    "            elif isinstance(stmt, ast.While):\n",
-    "                stmt.body = self.transform_block(stmt.body, top_level=False)\n",
-    "                stmt.orelse = self.transform_block(stmt.orelse, top_level=False)\n",
-    "                new_body.append(stmt)\n",
-    "            else:\n",
-    "                new_body.append(stmt)\n",
-    "\n",
-    "        has_yield = any(isinstance(n, ast.Yield) for n in ast.walk(ast.Module(body=new_body, type_ignores=[])))\n",
-    "        if top_level and not has_yield and not self.is_source:\n",
-    "            new_body.append(self._yield_stmt(ast.Name(id=self.passthrough_name, ctx=ast.Load())))\n",
-    "        return new_body\n",
-    "\n",
-    "\n",
-    "class UdfBaseSelector:\n",
-    "    def select(self, family: str, is_source: bool) -> tuple[str, str, str]:\n",
-    "        if is_source:\n",
-    "            return \"ProcessSourceOperator\", \"UDFSourceOperator\", \"produce\"\n",
-    "        if family == \"tuple\":\n",
-    "            return \"ProcessTupleOperator\", \"UDFOperatorV2\", \"process_tuple\"\n",
-    "        if family == \"batch\":\n",
-    "            return \"ProcessBatchOperator\", \"UDFBatchOperator\", \"process_batch\"\n",
-    "        return \"ProcessTableOperator\", \"UDFTableOperator\", \"process_table\"\n",
-    "\n",
-    "    def workflow_operator_type(self, family: str, is_source: bool, input_count: int) -> str:\n",
-    "        \"\"\"\n",
-    "        Workflow JSON only uses the Python operator types that are known to exist\n",
-    "        in the target Texera deployment:\n",
-    "          - PythonUDFSourceV2 for 0-input source operators\n",
-    "          - PythonUDFV2 for regular 1-input Python UDFs\n",
-    "          - DualInputPortsPythonUDFV2 for 2-input Python UDFs\n",
-    "\n",
-    "        The implementation class inside `code` may still be UDFOperatorV2,\n",
-    "        UDFBatchOperator, UDFTableOperator, or UDFSourceOperator.\n",
-    "        \"\"\"\n",
-    "        if is_source:\n",
-    "            return \"PythonUDFSourceV2\"\n",
-    "        if input_count == 2:\n",
-    "            return \"DualInputPortsPythonUDFV2\"\n",
-    "        return \"PythonUDFV2\"\n",
-    "\n",
-    "    def data_variable(self, family: str) -> str:\n",
-    "        return {\"tuple\": \"tuple_\", \"batch\": \"batch\"}.get(family, \"table\")\n",
-    "\n",
-    "    def process_signature(self, family: str, is_source: bool) -> tuple[list[ast.arg], Optional[ast.AST]]:\n",
-    "        if is_source:\n",
-    "            return (\n",
-    "                [ast.arg(arg=\"self\")],\n",
-    "                ast.Subscript(\n",
-    "                    value=ast.Name(id=\"Iterator\", ctx=ast.Load()),\n",
-    "                    slice=ast.Subscript(\n",
-    "                        value=ast.Name(id=\"Optional\", ctx=ast.Load()),\n",
-    "                        slice=ast.Subscript(\n",
-    "                            value=ast.Name(id=\"Union\", ctx=ast.Load()),\n",
-    "                            slice=ast.Tuple(elts=[ast.Name(id=\"TupleLike\", ctx=ast.Load()), ast.Name(id=\"TableLike\", ctx=ast.Load())], ctx=ast.Load()),\n",
-    "                            ctx=ast.Load(),\n",
-    "                        ),\n",
-    "                        ctx=ast.Load(),\n",
-    "                    ),\n",
-    "                    ctx=ast.Load(),\n",
-    "                ),\n",
-    "            )\n",
-    "        if family == \"tuple\":\n",
-    "            return (\n",
-    "                [ast.arg(arg=\"self\"), ast.arg(arg=\"tuple_\", annotation=ast.Name(id=\"Tuple\", ctx=ast.Load())), ast.arg(arg=\"port\", annotation=ast.Name(id=\"int\", ctx=ast.Load()))],\n",
-    "                ast.Subscript(value=ast.Name(id=\"Iterator\", ctx=ast.Load()), slice=ast.Subscript(value=ast.Name(id=\"Optional\", ctx=ast.Load()), slice=ast.Name(id=\"TupleLike\", ctx=ast.Load()), ctx=ast.Load()), ctx=ast.Load()),\n",
-    "            )\n",
-    "        if family == \"batch\":\n",
-    "            return (\n",
-    "                [ast.arg(arg=\"self\"), ast.arg(arg=\"batch\", annotation=ast.Name(id=\"Batch\", ctx=ast.Load())), ast.arg(arg=\"port\", annotation=ast.Name(id=\"int\", ctx=ast.Load()))],\n",
-    "                ast.Subscript(value=ast.Name(id=\"Iterator\", ctx=ast.Load()), slice=ast.Subscript(value=ast.Name(id=\"Optional\", ctx=ast.Load()), slice=ast.Name(id=\"BatchLike\", ctx=ast.Load()), ctx=ast.Load()), ctx=ast.Load()),\n",
-    "            )\n",
-    "        return (\n",
-    "            [ast.arg(arg=\"self\"), ast.arg(arg=\"table\", annotation=ast.Name(id=\"Table\", ctx=ast.Load())), ast.arg(arg=\"port\", annotation=ast.Name(id=\"int\", ctx=ast.Load()))],\n",
-    "            ast.Subscript(value=ast.Name(id=\"Iterator\", ctx=ast.Load()), slice=ast.Subscript(value=ast.Name(id=\"Optional\", ctx=ast.Load()), slice=ast.Name(id=\"TableLike\", ctx=ast.Load()), ctx=ast.Load()), ctx=ast.Load()),\n",
-    "        )\n",
-    "\n",
-    "\n",
-    "class UiParameterEmitter:\n",
-    "    def statements(self, spec: UiParamSpec) -> list[ast.stmt]:\n",
-    "        stmts: list[ast.stmt] = []\n",
-    "        if spec.default_ast is not None:\n",
-    "            stmts.append(\n",
-    "                ast.Assign(\n",
-    "                    targets=[ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=spec.name, ctx=ast.Store())],\n",
-    "                    value=copy.deepcopy(spec.default_ast),\n",
-    "                )\n",
-    "            )\n",
-    "        else:\n",
-    "            stmts.append(\n",
-    "                ast.Assign(\n",
-    "                    targets=[ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=spec.name, ctx=ast.Store())],\n",
-    "                    value=ast.Constant(value=None),\n",
-    "                )\n",
-    "            )\n",
-    "        return stmts\n",
-    "\n",
-    "    def commented_line(self, spec: UiParamSpec) -> str:\n",
-    "        attr_suffix = spec.attr_type.split(\".\")[-1]\n",
-    "        return f\"# self.UiParameter({spec.name!r}, AttributeType.{attr_suffix})  # UiParameter disabled for now\"\n",
-    "\n",
-    "    def hardcoded_comment_line(self, spec: UiParamSpec) -> str:\n",
-    "        return f\"# Temporary hardcoded default for UI parameter '{spec.name}' from the source script.\"\n",
-    "\n",
-    "\n",
-    "class CodeCommentInjector:\n",
-    "    def inject_ui_parameter_comments(self, code: str, stage: StageInvocation, ui_emitter: UiParameterEmitter) -> str:\n",
-    "        if not stage.ui_params:\n",
-    "            return code\n",
-    "        lines = code.splitlines()\n",
-    "        out: list[str] = []\n",
-    "        specs_by_line = {\n",
-    "            f\"self.{spec.name} = {ast.unparse(spec.default_ast) if spec.default_ast is not None else 'None'}\": spec\n",
-    "            for spec in stage.ui_params\n",
-    "        }\n",
-    "        for line in lines:\n",
-    "            out.append(line)\n",
-    "            stripped = line.strip()\n",
-    "            spec = specs_by_line.get(stripped)\n",
-    "            if spec is not None:\n",
-    "                indent = line[: len(line) - len(line.lstrip())]\n",
-    "                out.append(indent + ui_emitter.hardcoded_comment_line(spec))\n",
-    "                out.append(indent + ui_emitter.commented_line(spec))\n",
-    "        return \"\\n\".join(out) + \"\\n\"\n",
-    "\n",
-    "\n",
-    "class FunctionBodyCompiler:\n",
-    "    def __init__(self):\n",
-    "        self.base_selector = UdfBaseSelector()\n",
-    "        self.ui_emitter = UiParameterEmitter()\n",
-    "        self.comment_injector = CodeCommentInjector()\n",
-    "\n",
-    "    def _param_mapping(self, stage: StageInvocation) -> dict[str, ast.AST]:\n",
-    "        mapping: dict[str, ast.AST] = {}\n",
-    "        for spec in stage.ui_params:\n",
-    "            mapping[spec.name] = ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=spec.name, ctx=ast.Load())\n",
-    "        if stage.is_source:\n",
-    "            return mapping\n",
-    "        if len(stage.data_inputs) == 1:\n",
-    "            only = stage.data_inputs[0]\n",
-    "            mapping[only.param_name] = ast.Name(id=self.base_selector.data_variable(stage.family), ctx=ast.Load())\n",
-    "        elif len(stage.data_inputs) > 1:\n",
-    "            for spec in stage.data_inputs:\n",
-    "                mapping[spec.param_name] = ast.Name(id=f\"input_{spec.input_port}\", ctx=ast.Load())\n",
-    "        return mapping\n",
-    "\n",
-    "    def _prep_statements(self, stage: StageInvocation) -> list[ast.stmt]:\n",
-    "        stmts: list[ast.stmt] = []\n",
-    "        if len(stage.data_inputs) > 1:\n",
-    "            data_var = self.base_selector.data_variable(stage.family)\n",
-    "            stmts.append(ast.Assign(\n",
-    "                targets=[ast.Subscript(value=ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=\"_texera_inputs\", ctx=ast.Load()), slice=ast.Name(id=\"port\", ctx=ast.Load()), ctx=ast.Store())],\n",
-    "                value=ast.Name(id=data_var, ctx=ast.Load()),\n",
-    "            ))\n",
-    "            cond = ast.Compare(\n",
-    "                left=ast.Call(func=ast.Name(id=\"len\", ctx=ast.Load()), args=[ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=\"_texera_inputs\", ctx=ast.Load())], keywords=[]),\n",
-    "                ops=[ast.Lt()],\n",
-    "                comparators=[ast.Constant(value=len(stage.data_inputs))],\n",
-    "            )\n",
-    "            stmts.append(ast.If(test=cond, body=[ast.Return(value=None)], orelse=[]))\n",
-    "            for spec in stage.data_inputs:\n",
-    "                stmts.append(ast.Assign(\n",
-    "                    targets=[ast.Name(id=f\"input_{spec.input_port}\", ctx=ast.Store())],\n",
-    "                    value=ast.Subscript(value=ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=\"_texera_inputs\", ctx=ast.Load()), slice=ast.Constant(value=spec.input_port), ctx=ast.Load()),\n",
-    "                ))\n",
-    "        return stmts\n",
-    "\n",
-    "    def compile_body(self, fn: ast.FunctionDef, stage: StageInvocation) -> list[ast.stmt]:\n",
-    "        fn_copy = copy.deepcopy(fn)\n",
-    "        mapping = self._param_mapping(stage)\n",
-    "        fn_copy = ParamRewriter(mapping).visit(fn_copy)\n",
-    "        ast.fix_missing_locations(fn_copy)\n",
-    "\n",
-    "        body: list[ast.stmt] = self._prep_statements(stage)\n",
-    "        for stmt in fn_copy.body:\n",
-    "            if isinstance(stmt, ast.Expr) and isinstance(stmt.value, ast.Constant) and isinstance(stmt.value.value, str):\n",
-    "                continue\n",
-    "            body.append(stmt)\n",
-    "        body = YieldTransformer(\n",
-    "            family=stage.family,\n",
-    "            is_source=stage.is_source,\n",
-    "            is_sink=stage.is_sink,\n",
-    "            passthrough_name=self.base_selector.data_variable(stage.family),\n",
-    "        ).transform_block(body, top_level=True)\n",
-    "        if not body:\n",
-    "            body = [ast.Pass()]\n",
-    "        return body\n",
-    "\n",
-    "    def emit_class(self, import_nodes: list[ast.stmt], fn: ast.FunctionDef, stage: StageInvocation) -> str:\n",
-    "        class_name, base_name, method_name = self.base_selector.select(stage.family, stage.is_source)\n",
-    "        args_list, returns_ann = self.base_selector.process_signature(stage.family, stage.is_source)\n",
-    "        body = self.compile_body(fn, stage)\n",
-    "\n",
-    "        open_body: list[ast.stmt] = []\n",
-    "        for spec in stage.ui_params:\n",
-    "            open_body.extend(self.ui_emitter.statements(spec))\n",
-    "        if len(stage.data_inputs) > 1:\n",
-    "            open_body.append(ast.Assign(\n",
-    "                targets=[ast.Attribute(value=ast.Name(id=\"self\", ctx=ast.Load()), attr=\"_texera_inputs\", ctx=ast.Store())],\n",
-    "                value=ast.Dict(keys=[], values=[]),\n",
-    "            ))\n",
-    "        if not open_body:\n",
-    "            open_body = [ast.Pass()]\n",
-    "\n",
-    "        class_body: list[ast.stmt] = []\n",
-    "        if stage.recursive:\n",
-    "            class_body.append(ast.Expr(value=ast.Constant(value=f\"TRANSLATOR NOTE: {stage.function_name} is recursive.\")))\n",
-    "        if stage.has_loop:\n",
-    "            class_body.append(ast.Expr(value=ast.Constant(value=f\"TRANSLATOR NOTE: {stage.function_name} contains loops.\")))\n",
-    "\n",
-    "        class_body.append(ast.FunctionDef(\n",
-    "            name=\"open\",\n",
-    "            args=ast.arguments(posonlyargs=[], args=[ast.arg(arg=\"self\")], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]),\n",
-    "            body=open_body,\n",
-    "            decorator_list=[],\n",
-    "            returns=ast.Name(id=\"None\", ctx=ast.Load()),\n",
-    "            type_comment=None,\n",
-    "        ))\n",
-    "        class_body.append(ast.FunctionDef(\n",
-    "            name=method_name,\n",
-    "            args=ast.arguments(posonlyargs=[], args=args_list, vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]),\n",
-    "            body=body,\n",
-    "            decorator_list=[ast.Name(id=\"overrides\", ctx=ast.Load())],\n",
-    "            returns=returns_ann,\n",
-    "            type_comment=None,\n",
-    "        ))\n",
-    "        class_body.append(ast.FunctionDef(\n",
-    "            name=\"close\",\n",
-    "            args=ast.arguments(posonlyargs=[], args=[ast.arg(arg=\"self\")], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]),\n",
-    "            body=[ast.Pass()],\n",
-    "            decorator_list=[],\n",
-    "            returns=ast.Name(id=\"None\", ctx=ast.Load()),\n",
-    "            type_comment=None,\n",
-    "        ))\n",
-    "        cls = ast.ClassDef(name=class_name, bases=[ast.Name(id=base_name, ctx=ast.Load())], keywords=[], body=class_body, decorator_list=[])\n",
-    "        ast.fix_missing_locations(cls)\n",
-    "\n",
-    "        prelude = [\n",
-    "            \"from typing import Iterator, Optional, Union\",\n",
-    "            *[ast.unparse(n) for n in import_nodes],\n",
-    "            \"from pytexera import *\",\n",
-    "            \"from core.models.schema.attribute_type import AttributeType\",\n",
-    "            \"\",\n",
-    "        ]\n",
-    "        module = ast.Module(body=[cls], type_ignores=[])\n",
-    "        ast.fix_missing_locations(module)\n",
-    "        rendered = \"\\n\".join(prelude) + ast.unparse(module) + \"\\n\"\n",
-    "        return self.comment_injector.inject_ui_parameter_comments(rendered, stage, self.ui_emitter)\n",
-    "\n",
-    "\n",
-    "class WorkflowOperatorBuilder:\n",
-    "    def _operator_properties(self, stage: StageInvocation) -> dict[str, Any]:\n",
-    "        props: dict[str, Any] = {\n",
-    "            \"code\": stage.code,\n",
-    "            \"workers\": 1,\n",
-    "        }\n",
-    "        if stage.operator_type == \"PythonUDFSourceV2\":\n",
-    "            if stage.source_columns:\n",
-    "                props[\"columns\"] = stage.source_columns\n",
-    "        else:\n",
-    "            props[\"retainInputColumns\"] = stage.retain_input_columns\n",
-    "            if stage.output_columns:\n",
-    "                props[\"outputColumns\"] = stage.output_columns\n",
-    "        return props\n",
-    "\n",
-    "    def _input_ports(self, stage: StageInvocation) -> list[dict[str, Any]]:\n",
-    "        if stage.is_source:\n",
-    "            return []\n",
-    "        count = max(1, len(stage.data_inputs))\n",
-    "        ports = []\n",
-    "        for i in range(count):\n",
-    "            display_name = \"\"\n",
-    "            if count > 1 and i < len(stage.data_inputs):\n",
-    "                display_name = stage.data_inputs[i].param_name\n",
-    "            dependencies: list[dict[str, Any]] = []\n",
-    "            if count > 1 and i > 0:\n",
-    "                dependencies = [{\"id\": i - 1, \"internal\": False}]\n",
-    "            ports.append({\n",
-    "                \"portID\": f\"input-{i}\",\n",
-    "                \"displayName\": display_name,\n",
-    "                \"allowMultiInputs\": True,\n",
-    "                \"isDynamicPort\": False,\n",
-    "                \"dependencies\": dependencies,\n",
-    "            })\n",
-    "        return ports\n",
-    "\n",
-    "    def _dynamic_ports_flags(self, stage: StageInvocation) -> tuple[bool, bool]:\n",
-    "        if stage.is_source:\n",
-    "            return False, False\n",
-    "        if stage.operator_type == \"DualInputPortsPythonUDFV2\":\n",
-    "            return False, False\n",
-    "        return True, True\n",
-    "\n",
-    "    def build(self, stage: StageInvocation, view_result: bool = False) -> dict[str, Any]:\n",
-    "        dynamic_input_ports, dynamic_output_ports = self._dynamic_ports_flags(stage)\n",
-    "        operator = {\n",
-    "            \"operatorID\": stage.operator_id,\n",
-    "            \"operatorType\": stage.operator_type,\n",
-    "            \"operatorVersion\": \"N/A\",\n",
-    "            \"operatorProperties\": self._operator_properties(stage),\n",
-    "            \"inputPorts\": self._input_ports(stage),\n",
-    "            \"outputPorts\": [\n",
-    "                {\n",
-    "                    \"portID\": \"output-0\",\n",
-    "                    \"displayName\": \"\",\n",
-    "                    \"allowMultiInputs\": False,\n",
-    "                    \"isDynamicPort\": False,\n",
-    "                }\n",
-    "            ],\n",
-    "            \"showAdvanced\": False,\n",
-    "            \"isDisabled\": False,\n",
-    "            \"customDisplayName\": stage.display_name,\n",
-    "            \"dynamicInputPorts\": dynamic_input_ports,\n",
-    "            \"dynamicOutputPorts\": dynamic_output_ports,\n",
-    "        }\n",
-    "        if view_result:\n",
-    "            operator[\"viewResult\"] = True\n",
-    "        return operator\n",
-    "\n",
-    "\n",
-    "class WorkflowLinkBuilder:\n",
-    "    def __init__(self):\n",
-    "        self.ids = WorkflowIdFactory()\n",
-    "\n",
-    "    def build_links(self, stages: list[StageInvocation]) -> list[dict[str, Any]]:\n",
-    "        stage_by_id = {s.stage_id: s for s in stages}\n",
-    "        links: list[dict[str, Any]] = []\n",
-    "        for stage in stages:\n",
-    "            for inp in stage.data_inputs:\n",
-    "                upstream = stage_by_id[inp.upstream_stage_id]\n",
-    "                links.append({\n",
-    "                    \"linkID\": self.ids.link_id(),\n",
-    "                    \"source\": {\"operatorID\": upstream.operator_id, \"portID\": \"output-0\"},\n",
-    "                    \"target\": {\"operatorID\": stage.operator_id, \"portID\": f\"input-{inp.input_port}\"},\n",
-    "                })\n",
-    "        return links\n",
-    "\n",
-    "\n",
-    "class WorkflowLayoutBuilder:\n",
-    "    def build_positions(self, stages: list[StageInvocation], start_x: int = 240, start_y: int = 180, x_gap: int = 300, y_gap: int = 180) -> dict[str, dict[str, int]]:\n",
-    "        preds: dict[str, list[str]] = {s.stage_id: [inp.upstream_stage_id for inp in s.data_inputs] for s in stages}\n",
-    "        layers: dict[str, int] = {}\n",
-    "        remaining = {s.stage_id for s in stages}\n",
-    "        while remaining:\n",
-    "            progressed = False\n",
-    "            for sid in list(remaining):\n",
-    "                if all(p in layers for p in preds[sid]):\n",
-    "                    layers[sid] = 0 if not preds[sid] else max(layers[p] for p in preds[sid]) + 1\n",
-    "                    remaining.remove(sid)\n",
-    "                    progressed = True\n",
-    "            if not progressed:\n",
-    "                for sid in list(remaining):\n",
-    "                    layers[sid] = 0\n",
-    "                    remaining.remove(sid)\n",
-    "        by_layer: dict[int, list[StageInvocation]] = {}\n",
-    "        for s in stages:\n",
-    "            by_layer.setdefault(layers[s.stage_id], []).append(s)\n",
-    "        positions: dict[str, dict[str, int]] = {}\n",
-    "        for layer, items in sorted(by_layer.items()):\n",
-    "            for idx, stage in enumerate(items):\n",
-    "                positions[stage.operator_id] = {\"x\": start_x + layer * x_gap, \"y\": start_y + idx * y_gap}\n",
-    "        return positions\n",
-    "\n",
-    "\n",
-    "class WorkflowJsonAssembler:\n",
-    "    def __init__(self):\n",
-    "        self.operator_builder = WorkflowOperatorBuilder()\n",
-    "        self.link_builder = WorkflowLinkBuilder()\n",
-    "        self.layout_builder = WorkflowLayoutBuilder()\n",
-    "\n",
-    "    def assemble(self, stages: list[StageInvocation]) -> dict[str, Any]:\n",
-    "        operators = [\n",
-    "            self.operator_builder.build(stage, view_result=(i == len(stages) - 1))\n",
-    "            for i, stage in enumerate(stages)\n",
-    "        ]\n",
-    "        return {\n",
-    "            \"operators\": operators,\n",
-    "            \"operatorPositions\": self.layout_builder.build_positions(stages),\n",
-    "            \"links\": self.link_builder.build_links(stages),\n",
-    "            \"commentBoxes\": [],\n",
-    "            \"settings\": {\"dataTransferBatchSize\": 400},\n",
-    "        }\n",
-    "\n",
-    "\n",
-    "class WorkflowTranslator:\n",
-    "    def __init__(self):\n",
-    "        self.parser = SourceModuleParser()\n",
-    "        self.imports = ImportCollector()\n",
-    "        self.functions = FunctionIndex()\n",
-    "        self.call_graph = CallGraphBuilder()\n",
-    "        self.recursion = RecursiveAnalyzer()\n",
-    "        self.facts = FunctionFactsAnalyzer()\n",
-    "        self.constant_env = ConstantEnvBuilder()\n",
-    "        self.dag_extractor = EntryDagExtractor()\n",
-    "        self.ids = WorkflowIdFactory()\n",
-    "        self.code_emitter = FunctionBodyCompiler()\n",
-    "        self.workflow = WorkflowJsonAssembler()\n",
-    "        self.schema_inferer = SourceSchemaInferer()\n",
-    "        self.table_schema_inferer = TableOutputSchemaInferer()\n",
-    "\n",
-    "    def translate(self, source: str, entry: str = \"main\") -> dict[str, Any]:\n",
-    "        tree = self.parser.parse(source)\n",
-    "        import_nodes = self.imports.collect_nodes(tree)\n",
-    "        functions = self.functions.collect(tree)\n",
-    "        if entry not in functions:\n",
-    "            raise ValueError(f\"Entry function {entry!r} not found. Available: {sorted(functions)}\")\n",
-    "\n",
-    "        graph = self.call_graph.build(functions)\n",
-    "        recursive = self.recursion.detect(graph)\n",
-    "        facts = self.facts.analyze(tree, functions, graph, recursive)\n",
-    "        const_env = self.constant_env.build(functions[entry])\n",
-    "        stages = self.dag_extractor.extract(functions[entry], functions, facts, const_env)\n",
-    "        if not stages:\n",
-    "            raise ValueError(f\"Entry function {entry!r} does not contain a detectable pipeline of helper calls.\")\n",
-    "\n",
-    "        stage_by_id: dict[str, StageInvocation] = {s.stage_id: s for s in stages}\n",
-    "        for stage in stages:\n",
-    "            fn = functions[stage.function_name]\n",
-    "            if len(stage.data_inputs) > 2:\n",
-    "                raise ValueError(f\"Current workflow JSON emitter supports at most 2 data inputs per stage, got {len(stage.data_inputs)} for {stage.function_name!r}.\")\n",
-    "            stage.operator_type = UdfBaseSelector().workflow_operator_type(stage.family, stage.is_source, len(stage.data_inputs))\n",
-    "            stage.operator_id = self.ids.operator_id(stage.operator_type)\n",
-    "            stage.display_name = \" \".join(part.capitalize() for part in stage.function_name.split(\"_\"))\n",
-    "            stage.source_columns = self.schema_inferer.infer(fn, stage)\n",
-    "            if stage.operator_type == \"PythonUDFSourceV2\" and stage.family == \"table\" and not stage.source_columns:\n",
-    "                raise ValueError(\n",
-    "                    f\"Could not infer output columns for source stage {stage.function_name!r}. \"\n",
-    "                    \"Make the source schema explicit or use a source that can be sampled at translation time.\"\n",
-    "                )\n",
-    "            if stage.family == \"table\":\n",
-    "                input_columns: list[dict[str, str]] = []\n",
-    "                for di in stage.data_inputs:\n",
-    "                    upstream = stage_by_id[di.upstream_stage_id]\n",
-    "                    input_columns.extend(upstream.output_columns or upstream.source_columns)\n",
-    "                if stage.is_source:\n",
-    "                    stage.output_columns = list(stage.source_columns)\n",
-    "                    stage.retain_input_columns = False\n",
-    "                else:\n",
-    "                    stage.output_columns, stage.retain_input_columns = self.table_schema_inferer.infer(fn, stage, input_columns)\n",
-    "            stage.code = self.code_emitter.emit_class(import_nodes, fn, stage)\n",
-    "\n",
-    "        workflow_json = self.workflow.assemble(stages)\n",
-    "        metadata = {\n",
-    "            \"entry\": entry,\n",
-    "            \"stages\": [\n",
-    "                {\n",
-    "                    \"stage_id\": s.stage_id,\n",
-    "                    \"function\": s.function_name,\n",
-    "                    \"operatorID\": s.operator_id,\n",
-    "                    \"operator_type\": s.operator_type,\n",
-    "                    \"family\": s.family,\n",
-    "                    \"base_class\": UdfBaseSelector().select(s.family, s.is_source)[1],\n",
-    "                    \"method\": UdfBaseSelector().select(s.family, s.is_source)[2],\n",
-    "                    \"is_source\": s.is_source,\n",
-    "                    \"is_sink\": s.is_sink,\n",
-    "                    \"recursive\": s.recursive,\n",
-    "                    \"has_loop\": s.has_loop,\n",
-    "                    \"assigned_to\": s.assigned_to,\n",
-    "                    \"data_inputs\": [\n",
-    "                        {\n",
-    "                            \"param_name\": di.param_name,\n",
-    "                            \"upstream_stage_id\": di.upstream_stage_id,\n",
-    "                            \"upstream_var\": di.upstream_var,\n",
-    "                            \"input_port\": di.input_port,\n",
-    "                        }\n",
-    "                        for di in s.data_inputs\n",
-    "                    ],\n",
-    "                    \"ui_parameters\": [\n",
-    "                        {\n",
-    "                            \"name\": p.name,\n",
-    "                            \"attr_type\": p.attr_type,\n",
-    "                            \"default\": ast.unparse(p.default_ast) if p.default_ast is not None else None,\n",
-    "                        }\n",
-    "                        for p in s.ui_params\n",
-    "                    ],\n",
-    "                    \"source_columns\": s.source_columns,\n",
-    "                    \"output_columns\": s.output_columns,\n",
-    "                    \"retain_input_columns\": s.retain_input_columns,\n",
-    "                }\n",
-    "                for s in stages\n",
-    "            ],\n",
-    "        }\n",
-    "        return {\"workflow_json\": workflow_json, \"metadata\": metadata}\n",
-    "\n",
-    "\n",
-    "def translate_to_texera_workflow_json(source: str, entry: str = \"main\") -> dict[str, Any]:\n",
-    "    return WorkflowTranslator().translate(source, entry=entry)\n",
-    "\n",
-    "\n",
-    "def write_translation(result: dict[str, Any], out_prefix: str | Path) -> dict[str, str]:\n",
-    "    out_prefix = Path(out_prefix)\n",
-    "    base = out_prefix.with_suffix(\"\")\n",
-    "    workflow_path = base.parent / f\"{base.name}.workflow.json\"\n",
-    "    meta_path = base.parent / f\"{base.name}.workflow.meta.json\"\n",
-    "    workflow_path.write_text(json.dumps(result[\"workflow_json\"], indent=2) + \"\\n\", encoding=\"utf-8\")\n",
-    "    meta_path.write_text(json.dumps(result[\"metadata\"], indent=2) + \"\\n\", encoding=\"utf-8\")\n",
-    "    return {\"workflow_json\": str(workflow_path), \"workflow_metadata\": str(meta_path)}\n",
-    "\n",
-    "# =============================\n",
-    "# Native-operator knowledge base\n",
-    "# =============================\n",
-    "\n",
-    "@dataclass\n",
-    "class NativeOperatorMatch:\n",
-    "    operator_type: str\n",
-    "    properties: dict[str, Any]\n",
-    "    display_name: str | None = None\n",
-    "    confidence: float = 1.0\n",
-    "    output_columns: list[dict[str, str]] | None = None\n",
-    "    retain_input_columns: bool | None = None\n",
-    "    notes: list[str] = field(default_factory=list)\n",
-    "\n",
-    "\n",
-    "class NativeOperatorCatalog:\n",
-    "    def __init__(self, catalog_json_path: str | Path | None = None):\n",
-    "        self.catalog_json_path = Path(catalog_json_path) if catalog_json_path else None\n",
-    "        self.templates: dict[str, dict[str, Any]] = {}\n",
-    "        self._load_default_if_available()\n",
-    "\n",
-    "    def _load_default_if_available(self):\n",
-    "        if self.catalog_json_path is None:\n",
-    "            default = Path('/mnt/data/Untitled workflow (11).json')\n",
-    "            if default.exists():\n",
-    "                self.catalog_json_path = default\n",
-    "        if self.catalog_json_path and self.catalog_json_path.exists():\n",
-    "            self.load(self.catalog_json_path)\n",
-    "\n",
-    "    def load(self, path: str | Path):\n",
-    "        path = Path(path)\n",
-    "        obj = json.loads(path.read_text(encoding='utf-8'))\n",
-    "        templates: dict[str, dict[str, Any]] = {}\n",
-    "        for op in obj.get('operators', []):\n",
-    "            op_type = op.get('operatorType')\n",
-    "            if op_type and op_type not in templates:\n",
-    "                templates[op_type] = copy.deepcopy(op)\n",
-    "        self.templates = templates\n",
-    "\n",
-    "    def has(self, operator_type: str) -> bool:\n",
-    "        return operator_type in self.templates\n",
-    "\n",
-    "    def template(self, operator_type: str) -> dict[str, Any]:\n",
-    "        if operator_type not in self.templates:\n",
-    "            raise KeyError(f'Operator type {operator_type!r} not found in native operator catalog')\n",
-    "        return copy.deepcopy(self.templates[operator_type])\n",
-    "\n",
-    "\n",
-    "class ImportAliasIndex:\n",
-    "    def build(self, tree: ast.Module) -> dict[str, str]:\n",
-    "        aliases: dict[str, str] = {}\n",
-    "        for node in tree.body:\n",
-    "            if isinstance(node, ast.Import):\n",
-    "                for alias in node.names:\n",
-    "                    aliases[alias.asname or alias.name.split('.')[0]] = alias.name\n",
-    "            elif isinstance(node, ast.ImportFrom):\n",
-    "                mod = node.module or ''\n",
-    "                for alias in node.names:\n",
-    "                    aliases[alias.asname or alias.name] = f\"{mod}.{alias.name}\" if mod else alias.name\n",
-    "        return aliases\n",
-    "\n",
-    "\n",
-    "class QualifiedNameResolver:\n",
-    "    def __init__(self, aliases: dict[str, str]):\n",
-    "        self.aliases = aliases\n",
-    "\n",
-    "    def resolve(self, node: ast.AST | None) -> str | None:\n",
-    "        if node is None:\n",
-    "            return None\n",
-    "        if isinstance(node, ast.Name):\n",
-    "            return self.aliases.get(node.id, node.id)\n",
-    "        if isinstance(node, ast.Attribute):\n",
-    "            base = self.resolve(node.value)\n",
-    "            if base is None:\n",
-    "                return None\n",
-    "            return f\"{base}.{node.attr}\"\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class CallSignature:\n",
-    "    qualified_names: tuple[str, ...]\n",
-    "    positional_params: tuple[str, ...] = ()\n",
-    "    keyword_aliases: dict[str, str] = field(default_factory=dict)\n",
-    "\n",
-    "\n",
-    "class CallArgumentBinder:\n",
-    "    def bind(self, call: ast.Call, sig: CallSignature) -> dict[str, ast.AST]:\n",
-    "        bound: dict[str, ast.AST] = {}\n",
-    "        for idx, arg in enumerate(call.args):\n",
-    "            if idx < len(sig.positional_params):\n",
-    "                bound[sig.positional_params[idx]] = arg\n",
-    "        for kw in call.keywords:\n",
-    "            if kw.arg is None:\n",
-    "                continue\n",
-    "            canonical = sig.keyword_aliases.get(kw.arg, kw.arg)\n",
-    "            bound[canonical] = kw.value\n",
-    "        return bound\n",
-    "\n",
-    "\n",
-    "class StageSemanticFacts:\n",
-    "    def __init__(self, fn: ast.FunctionDef, resolver: QualifiedNameResolver):\n",
-    "        self.fn = fn\n",
-    "        self.resolver = resolver\n",
-    "        self.calls: list[ast.Call] = []\n",
-    "        self.assignments: list[ast.Assign] = []\n",
-    "        self.returns: list[ast.Return] = []\n",
-    "        self.name_to_value: dict[str, ast.AST] = {}\n",
-    "        self._scan()\n",
-    "\n",
-    "    def _scan(self):\n",
-    "        for stmt in self.fn.body:\n",
-    "            if isinstance(stmt, ast.Assign):\n",
-    "                self.assignments.append(stmt)\n",
-    "                if len(stmt.targets) == 1 and isinstance(stmt.targets[0], ast.Name):\n",
-    "                    self.name_to_value[stmt.targets[0].id] = stmt.value\n",
-    "            elif isinstance(stmt, ast.Return):\n",
-    "                self.returns.append(stmt)\n",
-    "            for node in ast.walk(stmt):\n",
-    "                if isinstance(node, ast.Call):\n",
-    "                    self.calls.append(node)\n",
-    "\n",
-    "    def call_qname(self, call: ast.Call) -> str | None:\n",
-    "        return self.resolver.resolve(call.func)\n",
-    "\n",
-    "    def all_call_names(self) -> list[str]:\n",
-    "        out = []\n",
-    "        for call in self.calls:\n",
-    "            name = self.call_qname(call)\n",
-    "            if name:\n",
-    "                out.append(name)\n",
-    "        return out\n",
-    "\n",
-    "    def final_return_value(self) -> ast.AST | None:\n",
-    "        for stmt in reversed(self.fn.body):\n",
-    "            if isinstance(stmt, ast.Return):\n",
-    "                return stmt.value\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "class NativeMatchContext:\n",
-    "    def __init__(self, stage: StageInvocation, fn: ast.FunctionDef, facts: StageSemanticFacts,\n",
-    "                 input_columns: list[dict[str, str]], const_env: dict[str, ast.AST]):\n",
-    "        self.stage = stage\n",
-    "        self.fn = fn\n",
-    "        self.facts = facts\n",
-    "        self.input_columns = input_columns\n",
-    "        self.const_env = const_env\n",
-    "\n",
-    "    @property\n",
-    "    def input_column_names(self) -> list[str]:\n",
-    "        return [c.get('attributeName', '') for c in self.input_columns if c.get('attributeName')]\n",
-    "\n",
-    "    def resolve_const(self, node: ast.AST | None) -> ast.AST | None:\n",
-    "        if isinstance(node, ast.Name) and node.id in self.const_env:\n",
-    "            return self.const_env[node.id]\n",
-    "        return node\n",
-    "\n",
-    "    def const_value(self, node: ast.AST | None) -> Any:\n",
-    "        node = self.resolve_const(node)\n",
-    "        if isinstance(node, ast.Constant):\n",
-    "            return node.value\n",
-    "        return None\n",
-    "\n",
-    "    def subscript_column(self, node: ast.AST | None) -> str | None:\n",
-    "        if isinstance(node, ast.Subscript):\n",
-    "            sl = node.slice\n",
-    "            if isinstance(sl, ast.Constant) and isinstance(sl.value, str):\n",
-    "                return sl.value\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "class NativeMatcher:\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        raise NotImplementedError\n",
-    "\n",
-    "\n",
-    "class PandasReadCsvMatcher(NativeMatcher):\n",
-    "    SIG = CallSignature(\n",
-    "        qualified_names=('pandas.read_csv', 'pd.read_csv'),\n",
-    "        positional_params=('filepath_or_buffer',),\n",
-    "        keyword_aliases={'filepath_or_buffer': 'filepath_or_buffer', 'sep': 'sep', 'delimiter': 'sep', 'header': 'header'},\n",
-    "    )\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        self.binder = CallArgumentBinder()\n",
-    "\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        if not ctx.stage.is_source:\n",
-    "            return None\n",
-    "        for call in ctx.facts.calls:\n",
-    "            qn = ctx.facts.call_qname(call)\n",
-    "            if qn not in self.SIG.qualified_names:\n",
-    "                continue\n",
-    "            args = self.binder.bind(call, self.SIG)\n",
-    "            file_node = ctx.resolve_const(args.get('filepath_or_buffer'))\n",
-    "            file_value = ctx.const_value(file_node)\n",
-    "            if isinstance(file_value, str) and file_value:\n",
-    "                file_name = file_value\n",
-    "            elif file_node is not None:\n",
-    "                file_name = ast.unparse(file_node)\n",
-    "            else:\n",
-    "                file_name = ''\n",
-    "            sep = ctx.const_value(args.get('sep')) or ','\n",
-    "            header = args.get('header')\n",
-    "            has_header = ctx.const_value(header)\n",
-    "            if has_header is None:\n",
-    "                has_header = True\n",
-    "            props = {\n",
-    "                'fileEncoding': 'UTF_8',\n",
-    "                'customDelimiter': sep,\n",
-    "                'hasHeader': bool(has_header),\n",
-    "                'fileName': file_name,\n",
-    "            }\n",
-    "            return NativeOperatorMatch(\n",
-    "                operator_type='CSVFileScan',\n",
-    "                properties=props,\n",
-    "                display_name='CSV File Scan',\n",
-    "                confidence=0.99,\n",
-    "                output_columns=ctx.stage.source_columns or None,\n",
-    "                retain_input_columns=False,\n",
-    "                notes=['Matched pandas.read_csv to CSVFileScan'],\n",
-    "            )\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "class HistPlotMatcher(NativeMatcher):\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        for call in ctx.facts.calls:\n",
-    "            qn = ctx.facts.call_qname(call)\n",
-    "            col = None\n",
-    "            if qn in ('matplotlib.pyplot.hist', 'plt.hist') and call.args:\n",
-    "                col = ctx.subscript_column(call.args[0])\n",
-    "            elif qn in ('seaborn.histplot', 'sns.histplot'):\n",
-    "                for kw in call.keywords:\n",
-    "                    if kw.arg == 'x':\n",
-    "                        if isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str):\n",
-    "                            col = kw.value.value\n",
-    "                        else:\n",
-    "                            col = ctx.subscript_column(kw.value)\n",
-    "            elif qn in ('plotly.express.histogram', 'px.histogram'):\n",
-    "                for kw in call.keywords:\n",
-    "                    if kw.arg in ('x', 'y') and isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str):\n",
-    "                        col = kw.value.value\n",
-    "                        break\n",
-    "            elif qn is None and isinstance(call.func, ast.Attribute) and call.func.attr == 'plot':\n",
-    "                base = call.func.value\n",
-    "                if isinstance(base, ast.Call) and isinstance(base.func, ast.Attribute) and base.func.attr == 'dropna':\n",
-    "                    if call.keywords:\n",
-    "                        for kw in call.keywords:\n",
-    "                            if kw.arg == 'kind' and isinstance(kw.value, ast.Constant) and kw.value.value == 'hist':\n",
-    "                                col = ctx.subscript_column(base.func.value)\n",
-    "                                break\n",
-    "            if col:\n",
-    "                return NativeOperatorMatch(\n",
-    "                    operator_type='Histogram',\n",
-    "                    properties={'value': col},\n",
-    "                    display_name='Histogram',\n",
-    "                    confidence=0.95,\n",
-    "                    output_columns=ctx.input_columns,\n",
-    "                    retain_input_columns=True,\n",
-    "                    notes=[f'Matched histogram on column {col!r}'],\n",
-    "                )\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "class ScatterPlotMatcher(NativeMatcher):\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        for call in ctx.facts.calls:\n",
-    "            qn = ctx.facts.call_qname(call)\n",
-    "            x = y = None\n",
-    "            if qn in ('matplotlib.pyplot.scatter', 'plt.scatter') and len(call.args) >= 2:\n",
-    "                x = ctx.subscript_column(call.args[0])\n",
-    "                y = ctx.subscript_column(call.args[1])\n",
-    "            elif qn in ('seaborn.scatterplot', 'sns.scatterplot'):\n",
-    "                for kw in call.keywords:\n",
-    "                    if kw.arg == 'x':\n",
-    "                        x = kw.value.value if isinstance(kw.value, ast.Constant) else ctx.subscript_column(kw.value)\n",
-    "                    elif kw.arg == 'y':\n",
-    "                        y = kw.value.value if isinstance(kw.value, ast.Constant) else ctx.subscript_column(kw.value)\n",
-    "            elif qn in ('plotly.express.scatter', 'px.scatter'):\n",
-    "                for kw in call.keywords:\n",
-    "                    if kw.arg == 'x' and isinstance(kw.value, ast.Constant):\n",
-    "                        x = kw.value.value\n",
-    "                    elif kw.arg == 'y' and isinstance(kw.value, ast.Constant):\n",
-    "                        y = kw.value.value\n",
-    "            if x and y:\n",
-    "                return NativeOperatorMatch(\n",
-    "                    operator_type='Scatterplot',\n",
-    "                    properties={'xColumn': x, 'yColumn': y, 'alpha': 1.0, 'xLogScale': False, 'yLogScale': False},\n",
-    "                    display_name='Scatter Plot',\n",
-    "                    confidence=0.95,\n",
-    "                    output_columns=ctx.input_columns,\n",
-    "                    retain_input_columns=True,\n",
-    "                    notes=[f'Matched scatter plot on {x!r}, {y!r}'],\n",
-    "                )\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "class PieChartMatcher(NativeMatcher):\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        for call in ctx.facts.calls:\n",
-    "            qn = ctx.facts.call_qname(call)\n",
-    "            value = name = None\n",
-    "            if qn in ('matplotlib.pyplot.pie', 'plt.pie') and call.args:\n",
-    "                value = ctx.subscript_column(call.args[0])\n",
-    "                for kw in call.keywords:\n",
-    "                    if kw.arg == 'labels':\n",
-    "                        name = ctx.subscript_column(kw.value)\n",
-    "            elif qn in ('plotly.express.pie', 'px.pie'):\n",
-    "                for kw in call.keywords:\n",
-    "                    if kw.arg == 'values' and isinstance(kw.value, ast.Constant):\n",
-    "                        value = kw.value.value\n",
-    "                    elif kw.arg == 'names' and isinstance(kw.value, ast.Constant):\n",
-    "                        name = kw.value.value\n",
-    "            if value and name:\n",
-    "                return NativeOperatorMatch(\n",
-    "                    operator_type='PieChart',\n",
-    "                    properties={'value': value, 'name': name},\n",
-    "                    display_name='Pie Chart',\n",
-    "                    confidence=0.93,\n",
-    "                    output_columns=ctx.input_columns,\n",
-    "                    retain_input_columns=True,\n",
-    "                )\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "class WordCloudMatcher(NativeMatcher):\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        # Look for WordCloud().generate(_from_text)?(table['col'])\n",
-    "        for call in ctx.facts.calls:\n",
-    "            qn = ctx.facts.call_qname(call)\n",
-    "            if qn not in ('wordcloud.WordCloud', 'WordCloud'):\n",
-    "                continue\n",
-    "            generated_col = None\n",
-    "            top_n = 200\n",
-    "            for kw in call.keywords:\n",
-    "                if kw.arg in ('max_words', 'top_n'):\n",
-    "                    v = ctx.const_value(kw.value)\n",
-    "                    if isinstance(v, int):\n",
-    "                        top_n = v\n",
-    "            # search users of constructor result\n",
-    "        for node in ast.walk(ctx.fn):\n",
-    "            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) and node.func.attr in ('generate', 'generate_from_text') and node.args:\n",
-    "                generated_col = ctx.subscript_column(node.args[0])\n",
-    "                if generated_col:\n",
-    "                    return NativeOperatorMatch(\n",
-    "                        operator_type='WordCloud',\n",
-    "                        properties={'topN': top_n, 'textColumn': generated_col},\n",
-    "                        display_name='Word Cloud',\n",
-    "                        confidence=0.92,\n",
-    "                        output_columns=ctx.input_columns,\n",
-    "                        retain_input_columns=True,\n",
-    "                    )\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "class NativeOperatorKnowledgeBase:\n",
-    "    def __init__(self):\n",
-    "        self.matchers: list[NativeMatcher] = [\n",
-    "            PandasReadCsvMatcher(),\n",
-    "            HistPlotMatcher(),\n",
-    "            ScatterPlotMatcher(),\n",
-    "            PieChartMatcher(),\n",
-    "            WordCloudMatcher(),\n",
-    "        ]\n",
-    "\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        best: NativeOperatorMatch | None = None\n",
-    "        for matcher in self.matchers:\n",
-    "            match = matcher.match(ctx)\n",
-    "            if match is not None and (best is None or match.confidence > best.confidence):\n",
-    "                best = match\n",
-    "        return best\n",
-    "\n",
-    "\n",
-    "class NativeStagePlanner:\n",
-    "    def __init__(self, catalog: NativeOperatorCatalog | None = None):\n",
-    "        self.catalog = catalog or NativeOperatorCatalog()\n",
-    "        self.aliases = ImportAliasIndex()\n",
-    "        self.knowledge = NativeOperatorKnowledgeBase()\n",
-    "\n",
-    "    def plan(self, tree: ast.Module, fn: ast.FunctionDef, stage: StageInvocation,\n",
-    "             input_columns: list[dict[str, str]], const_env: dict[str, ast.AST]) -> NativeOperatorMatch | None:\n",
-    "        alias_map = self.aliases.build(tree)\n",
-    "        resolver = QualifiedNameResolver(alias_map)\n",
-    "        facts = StageSemanticFacts(fn, resolver)\n",
-    "        ctx = NativeMatchContext(stage, fn, facts, input_columns, const_env)\n",
-    "        match = self.knowledge.match(ctx)\n",
-    "        if match is None:\n",
-    "            return None\n",
-    "        if not self.catalog.has(match.operator_type):\n",
-    "            return None\n",
-    "        return match\n",
-    "\n",
-    "\n",
-    "# =============================\n",
-    "# Override workflow builders with native support\n",
-    "# =============================\n",
-    "\n",
-    "class WorkflowOperatorBuilder:\n",
-    "    def __init__(self, native_catalog: NativeOperatorCatalog | None = None):\n",
-    "        self.native_catalog = native_catalog or NativeOperatorCatalog()\n",
-    "\n",
-    "    def _operator_properties(self, stage: StageInvocation) -> dict[str, Any]:\n",
-    "        if getattr(stage, 'native_operator_type', None):\n",
-    "            props = copy.deepcopy(getattr(stage, 'native_operator_properties', {}) or {})\n",
-    "            return props\n",
-    "        props: dict[str, Any] = {\n",
-    "            'code': stage.code,\n",
-    "            'workers': 1,\n",
-    "        }\n",
-    "        if stage.operator_type == 'PythonUDFSourceV2':\n",
-    "            if stage.source_columns:\n",
-    "                props['columns'] = stage.source_columns\n",
-    "        else:\n",
-    "            props['retainInputColumns'] = stage.retain_input_columns\n",
-    "            if stage.output_columns:\n",
-    "                props['outputColumns'] = stage.output_columns\n",
-    "        return props\n",
-    "\n",
-    "    def _input_ports(self, stage: StageInvocation) -> list[dict[str, Any]]:\n",
-    "        if getattr(stage, 'native_operator_type', None):\n",
-    "            template = self.native_catalog.template(stage.native_operator_type)\n",
-    "            ports = copy.deepcopy(template.get('inputPorts', []))\n",
-    "            count = len(stage.data_inputs)\n",
-    "            if count and len(ports) >= count:\n",
-    "                for i in range(count):\n",
-    "                    if i < len(ports):\n",
-    "                        ports[i]['portID'] = f'input-{i}'\n",
-    "                        if count > 1 and i < len(stage.data_inputs):\n",
-    "                            ports[i]['displayName'] = stage.data_inputs[i].param_name\n",
-    "                            ports[i]['dependencies'] = [] if i == 0 else [{'id': i - 1, 'internal': False}]\n",
-    "                return ports[:count]\n",
-    "            return ports\n",
-    "        if stage.is_source:\n",
-    "            return []\n",
-    "        count = max(1, len(stage.data_inputs))\n",
-    "        ports = []\n",
-    "        for i in range(count):\n",
-    "            display_name = ''\n",
-    "            if count > 1 and i < len(stage.data_inputs):\n",
-    "                display_name = stage.data_inputs[i].param_name\n",
-    "            dependencies: list[dict[str, Any]] = []\n",
-    "            if count > 1 and i > 0:\n",
-    "                dependencies = [{'id': i - 1, 'internal': False}]\n",
-    "            ports.append({\n",
-    "                'portID': f'input-{i}',\n",
-    "                'displayName': display_name,\n",
-    "                'allowMultiInputs': True,\n",
-    "                'isDynamicPort': False,\n",
-    "                'dependencies': dependencies,\n",
-    "            })\n",
-    "        return ports\n",
-    "\n",
-    "    def _dynamic_ports_flags(self, stage: StageInvocation) -> tuple[bool, bool]:\n",
-    "        if getattr(stage, 'native_operator_type', None):\n",
-    "            template = self.native_catalog.template(stage.native_operator_type)\n",
-    "            return bool(template.get('dynamicInputPorts', False)), bool(template.get('dynamicOutputPorts', False))\n",
-    "        if stage.is_source:\n",
-    "            return False, False\n",
-    "        if stage.operator_type == 'DualInputPortsPythonUDFV2':\n",
-    "            return False, False\n",
-    "        return True, True\n",
-    "\n",
-    "    def build(self, stage: StageInvocation, view_result: bool = False) -> dict[str, Any]:\n",
-    "        if getattr(stage, 'native_operator_type', None):\n",
-    "            template = self.native_catalog.template(stage.native_operator_type)\n",
-    "            template['operatorID'] = stage.operator_id\n",
-    "            template['operatorType'] = stage.native_operator_type\n",
-    "            template['operatorVersion'] = 'N/A'\n",
-    "            template['operatorProperties'] = self._operator_properties(stage)\n",
-    "            template['inputPorts'] = self._input_ports(stage)\n",
-    "            template['outputPorts'] = copy.deepcopy(template.get('outputPorts', [\n",
-    "                {'portID': 'output-0', 'displayName': '', 'allowMultiInputs': False, 'isDynamicPort': False}\n",
-    "            ]))\n",
-    "            template['customDisplayName'] = stage.display_name\n",
-    "            if view_result:\n",
-    "                template['viewResult'] = True\n",
-    "            return template\n",
-    "\n",
-    "        dynamic_input_ports, dynamic_output_ports = self._dynamic_ports_flags(stage)\n",
-    "        operator = {\n",
-    "            'operatorID': stage.operator_id,\n",
-    "            'operatorType': stage.operator_type,\n",
-    "            'operatorVersion': 'N/A',\n",
-    "            'operatorProperties': self._operator_properties(stage),\n",
-    "            'inputPorts': self._input_ports(stage),\n",
-    "            'outputPorts': [\n",
-    "                {'portID': 'output-0', 'displayName': '', 'allowMultiInputs': False, 'isDynamicPort': False}\n",
-    "            ],\n",
-    "            'showAdvanced': False,\n",
-    "            'isDisabled': False,\n",
-    "            'customDisplayName': stage.display_name,\n",
-    "            'dynamicInputPorts': dynamic_input_ports,\n",
-    "            'dynamicOutputPorts': dynamic_output_ports,\n",
-    "        }\n",
-    "        if view_result:\n",
-    "            operator['viewResult'] = True\n",
-    "        return operator\n",
-    "\n",
-    "\n",
-    "class WorkflowJsonAssembler:\n",
-    "    def __init__(self, native_catalog: NativeOperatorCatalog | None = None):\n",
-    "        self.operator_builder = WorkflowOperatorBuilder(native_catalog=native_catalog)\n",
-    "        self.link_builder = WorkflowLinkBuilder()\n",
-    "        self.layout_builder = WorkflowLayoutBuilder()\n",
-    "\n",
-    "    def assemble(self, stages: list[StageInvocation]) -> dict[str, Any]:\n",
-    "        operators = [\n",
-    "            self.operator_builder.build(stage, view_result=(i == len(stages) - 1))\n",
-    "            for i, stage in enumerate(stages)\n",
-    "        ]\n",
-    "        return {\n",
-    "            'operators': operators,\n",
-    "            'operatorPositions': self.layout_builder.build_positions(stages),\n",
-    "            'links': self.link_builder.build_links(stages),\n",
-    "            'commentBoxes': [],\n",
-    "            'settings': {'dataTransferBatchSize': 400},\n",
-    "        }\n",
-    "\n",
-    "\n",
-    "class WorkflowTranslator:\n",
-    "    def __init__(self, catalog_json_path: str | Path | None = None):\n",
-    "        self.parser = SourceModuleParser()\n",
-    "        self.imports = ImportCollector()\n",
-    "        self.functions = FunctionIndex()\n",
-    "        self.call_graph = CallGraphBuilder()\n",
-    "        self.recursion = RecursiveAnalyzer()\n",
-    "        self.facts = FunctionFactsAnalyzer()\n",
-    "        self.constant_env = ConstantEnvBuilder()\n",
-    "        self.dag_extractor = EntryDagExtractor()\n",
-    "        self.ids = WorkflowIdFactory()\n",
-    "        self.code_emitter = FunctionBodyCompiler()\n",
-    "        self.catalog = NativeOperatorCatalog(catalog_json_path)\n",
-    "        self.native_planner = NativeStagePlanner(self.catalog)\n",
-    "        self.workflow = WorkflowJsonAssembler(native_catalog=self.catalog)\n",
-    "        self.schema_inferer = SourceSchemaInferer()\n",
-    "        self.table_schema_inferer = TableOutputSchemaInferer()\n",
-    "\n",
-    "    def translate(self, source: str, entry: str = 'main') -> dict[str, Any]:\n",
-    "        tree = self.parser.parse(source)\n",
-    "        import_nodes = self.imports.collect_nodes(tree)\n",
-    "        functions = self.functions.collect(tree)\n",
-    "        if entry not in functions:\n",
-    "            raise ValueError(f\"Entry function {entry!r} not found. Available: {sorted(functions)}\")\n",
-    "\n",
-    "        graph = self.call_graph.build(functions)\n",
-    "        recursive = self.recursion.detect(graph)\n",
-    "        facts = self.facts.analyze(tree, functions, graph, recursive)\n",
-    "        const_env = self.constant_env.build(functions[entry])\n",
-    "        stages = self.dag_extractor.extract(functions[entry], functions, facts, const_env)\n",
-    "        if not stages:\n",
-    "            raise ValueError(f\"Entry function {entry!r} does not contain a detectable pipeline of helper calls.\")\n",
-    "\n",
-    "        stage_by_id: dict[str, StageInvocation] = {s.stage_id: s for s in stages}\n",
-    "        selector = UdfBaseSelector()\n",
-    "        for stage in stages:\n",
-    "            fn = functions[stage.function_name]\n",
-    "            if len(stage.data_inputs) > 2:\n",
-    "                raise ValueError(f\"Current workflow JSON emitter supports at most 2 data inputs per stage, got {len(stage.data_inputs)} for {stage.function_name!r}.\")\n",
-    "            stage.display_name = ' '.join(part.capitalize() for part in stage.function_name.split('_'))\n",
-    "            # default UDF path\n",
-    "            stage.operator_type = selector.workflow_operator_type(stage.family, stage.is_source, len(stage.data_inputs))\n",
-    "            stage.operator_id = self.ids.operator_id(stage.operator_type)\n",
-    "            stage.source_columns = self.schema_inferer.infer(fn, stage)\n",
-    "            if stage.family == 'table':\n",
-    "                input_columns: list[dict[str, str]] = []\n",
-    "                for di in stage.data_inputs:\n",
-    "                    upstream = stage_by_id[di.upstream_stage_id]\n",
-    "                    input_columns.extend(getattr(upstream, 'output_columns', []) or getattr(upstream, 'source_columns', []))\n",
-    "                if stage.is_source:\n",
-    "                    stage.output_columns = list(stage.source_columns)\n",
-    "                    stage.retain_input_columns = False\n",
-    "                else:\n",
-    "                    stage.output_columns, stage.retain_input_columns = self.table_schema_inferer.infer(fn, stage, input_columns)\n",
-    "            else:\n",
-    "                input_columns = []\n",
-    "\n",
-    "            native_match = self.native_planner.plan(tree, fn, stage, input_columns, const_env)\n",
-    "            if native_match is not None:\n",
-    "                stage.native_operator_type = native_match.operator_type\n",
-    "                stage.native_operator_properties = native_match.properties\n",
-    "                stage.native_notes = native_match.notes\n",
-    "                stage.operator_type = native_match.operator_type\n",
-    "                stage.operator_id = self.ids.operator_id(native_match.operator_type)\n",
-    "                if native_match.display_name:\n",
-    "                    stage.display_name = native_match.display_name if stage.is_source else stage.display_name\n",
-    "                if native_match.output_columns is not None:\n",
-    "                    stage.output_columns = native_match.output_columns\n",
-    "                    if stage.is_source:\n",
-    "                        stage.source_columns = native_match.output_columns\n",
-    "                if native_match.retain_input_columns is not None:\n",
-    "                    stage.retain_input_columns = native_match.retain_input_columns\n",
-    "                stage.code = ''\n",
-    "            else:\n",
-    "                if stage.operator_type == 'PythonUDFSourceV2' and stage.family == 'table' and not stage.source_columns:\n",
-    "                    raise ValueError(\n",
-    "                        f\"Could not infer output columns for source stage {stage.function_name!r}. \"\n",
-    "                        'Make the source schema explicit or use a source that can be sampled at translation time.'\n",
-    "                    )\n",
-    "                stage.code = self.code_emitter.emit_class(import_nodes, fn, stage)\n",
-    "\n",
-    "        workflow_json = self.workflow.assemble(stages)\n",
-    "        metadata = {\n",
-    "            'entry': entry,\n",
-    "            'catalog_path': str(self.catalog.catalog_json_path) if self.catalog.catalog_json_path else None,\n",
-    "            'stages': [\n",
-    "                {\n",
-    "                    'stage_id': s.stage_id,\n",
-    "                    'function': s.function_name,\n",
-    "                    'operatorID': s.operator_id,\n",
-    "                    'operator_type': s.operator_type,\n",
-    "                    'native_operator_type': getattr(s, 'native_operator_type', None),\n",
-    "                    'family': s.family,\n",
-    "                    'base_class': None if getattr(s, 'native_operator_type', None) else selector.select(s.family, s.is_source)[1],\n",
-    "                    'method': None if getattr(s, 'native_operator_type', None) else selector.select(s.family, s.is_source)[2],\n",
-    "                    'is_source': s.is_source,\n",
-    "                    'is_sink': s.is_sink,\n",
-    "                    'recursive': s.recursive,\n",
-    "                    'has_loop': s.has_loop,\n",
-    "                    'assigned_to': s.assigned_to,\n",
-    "                    'data_inputs': [\n",
-    "                        {\n",
-    "                            'param_name': di.param_name,\n",
-    "                            'upstream_stage_id': di.upstream_stage_id,\n",
-    "                            'upstream_var': di.upstream_var,\n",
-    "                            'input_port': di.input_port,\n",
-    "                        }\n",
-    "                        for di in s.data_inputs\n",
-    "                    ],\n",
-    "                    'ui_parameters': [\n",
-    "                        {\n",
-    "                            'name': p.name,\n",
-    "                            'attr_type': p.attr_type,\n",
-    "                            'default': ast.unparse(p.default_ast) if p.default_ast is not None else None,\n",
-    "                        }\n",
-    "                        for p in s.ui_params\n",
-    "                    ],\n",
-    "                    'source_columns': s.source_columns,\n",
-    "                    'output_columns': s.output_columns,\n",
-    "                    'retain_input_columns': s.retain_input_columns,\n",
-    "                    'native_notes': getattr(s, 'native_notes', []),\n",
-    "                }\n",
-    "                for s in stages\n",
-    "            ],\n",
-    "        }\n",
-    "        return {'workflow_json': workflow_json, 'metadata': metadata}\n",
-    "\n",
-    "\n",
-    "def translate_to_texera_workflow_json(source: str, entry: str = 'main', catalog_json_path: str | Path | None = None) -> dict[str, Any]:\n",
-    "    return WorkflowTranslator(catalog_json_path=catalog_json_path).translate(source, entry=entry)\n",
-    "\n",
-    "# =============================\n",
-    "# Native matcher extensions added in v15\n",
-    "# =============================\n",
-    "\n",
-    "class TimeSeriesPlotMatcher(NativeMatcher):\n",
-    "    \"\"\"Match simple matplotlib/seaborn/plotly line plots over a column pair.\n",
-    "\n",
-    "    Conservative mapping:\n",
-    "      plt.plot(table['date'], table['close']) -> TimeSeriesPlot\n",
-    "    We avoid LineChart here because the catalog template exposes a less explicit\n",
-    "    nested `lines` shape, while TimeSeriesPlot has a stable flat property schema.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        for call in ctx.facts.calls:\n",
-    "            qn = ctx.facts.call_qname(call)\n",
-    "            x = y = None\n",
-    "\n",
-    "            if qn in ('matplotlib.pyplot.plot', 'plt.plot') and len(call.args) >= 2:\n",
-    "                x = ctx.subscript_column(call.args[0])\n",
-    "                y = ctx.subscript_column(call.args[1])\n",
-    "            elif qn in ('seaborn.lineplot', 'sns.lineplot'):\n",
-    "                for kw in call.keywords:\n",
-    "                    if kw.arg == 'x':\n",
-    "                        x = kw.value.value if isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str) else ctx.subscript_column(kw.value)\n",
-    "                    elif kw.arg == 'y':\n",
-    "                        y = kw.value.value if isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str) else ctx.subscript_column(kw.value)\n",
-    "            elif qn in ('plotly.express.line', 'px.line'):\n",
-    "                for kw in call.keywords:\n",
-    "                    if kw.arg == 'x' and isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str):\n",
-    "                        x = kw.value.value\n",
-    "                    elif kw.arg == 'y' and isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str):\n",
-    "                        y = kw.value.value\n",
-    "\n",
-    "            if x and y:\n",
-    "                return NativeOperatorMatch(\n",
-    "                    operator_type='TimeSeriesPlot',\n",
-    "                    properties={\n",
-    "                        'timeColumn': x,\n",
-    "                        'valueColumn': y,\n",
-    "                        'line': y,\n",
-    "                        'facetColumn': 'No Selection',\n",
-    "                        'categoryColumn': 'No Selection',\n",
-    "                        'slider': False,\n",
-    "                    },\n",
-    "                    display_name='Time Series Plot',\n",
-    "                    confidence=0.96,\n",
-    "                    output_columns=ctx.input_columns,\n",
-    "                    retain_input_columns=True,\n",
-    "                    notes=[f'Matched line/time-series plot on x={x!r}, y={y!r}'],\n",
-    "                )\n",
-    "        return None\n",
-    "\n",
-    "\n",
-    "class NativeOperatorKnowledgeBase:\n",
-    "    def __init__(self):\n",
-    "        self.matchers: list[NativeMatcher] = [\n",
-    "            PandasReadCsvMatcher(),\n",
-    "            TimeSeriesPlotMatcher(),\n",
-    "            HistPlotMatcher(),\n",
-    "            ScatterPlotMatcher(),\n",
-    "            PieChartMatcher(),\n",
-    "            WordCloudMatcher(),\n",
-    "        ]\n",
-    "\n",
-    "    def match(self, ctx: NativeMatchContext) -> NativeOperatorMatch | None:\n",
-    "        best: NativeOperatorMatch | None = None\n",
-    "        for matcher in self.matchers:\n",
-    "            match = matcher.match(ctx)\n",
-    "            if match is not None and (best is None or match.confidence > best.confidence):\n",
-    "                best = match\n",
-    "        return best\n",
-    "if __name__ == \"__main__\":\n",
-    "    result = translate_to_texera_workflow_json(sample, entry=\"main\", catalog_json_path=\"catalog.json\")\n",
-    "    paths = write_translation(result, \"demo_texera_workflow_vx2\")\n",
-    "    print(json.dumps(paths, indent=2))"
-   ],
-   "id": "419833dbad71acdf",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "  \"workflow_json\": \"demo_texera_workflow_vx2.workflow.json\",\n",
-      "  \"workflow_metadata\": \"demo_texera_workflow_vx2.workflow.meta.json\"\n",
-      "}\n"
-     ]
-    }
-   ],
-   "execution_count": 52
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T07:44:16.497856Z",
-     "start_time": "2026-03-04T07:44:15.182307Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "",
-   "id": "a8549cb2873a7b65",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "  \"workflow_json\": \"demo_texera_workflow_vx2.workflow.json\",\n",
-      "  \"workflow_metadata\": \"demo_texera_workflow_vx2.workflow.meta.json\"\n",
-      "}\n"
-     ]
-    }
-   ],
-   "execution_count": 40
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T05:37:27.140055Z",
-     "start_time": "2026-03-04T05:37:27.135017Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "sample = '''\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def extract_data(url: str) -> pd.DataFrame:\n",
-    "    df = pd.read_csv(url)\n",
-    "    print(\"Step 1: Extract\")\n",
-    "    print(df.head())\n",
-    "    return df\n",
-    "\n",
-    "def select_columns(df: pd.DataFrame) -> pd.DataFrame:\n",
-    "    selected = df[[\"date\", \"open\", \"high\", \"low\", \"close\", \"volume\"]].copy()\n",
-    "    print(\"\\\\nStep 2: Select columns\")\n",
-    "    print(selected.head())\n",
-    "    return selected\n",
-    "\n",
-    "def clean_data(df: pd.DataFrame) -> pd.DataFrame:\n",
-    "    cleaned = df.dropna().copy()\n",
-    "    print(\"\\\\nStep 3: Clean data\")\n",
-    "    print(cleaned.head())\n",
-    "    return cleaned\n",
-    "\n",
-    "def transform_data(df: pd.DataFrame) -> pd.DataFrame:\n",
-    "    transformed = df.copy()\n",
-    "    transformed[\"date\"] = pd.to_datetime(transformed[\"date\"], errors=\"coerce\")\n",
-    "    transformed = transformed.dropna(subset=[\"date\"]).copy()\n",
-    "    transformed = transformed.sort_values(\"date\")\n",
-    "    transformed[\"daily_return\"] = transformed[\"close\"].pct_change() * 100\n",
-    "    transformed[\"ma_30\"] = transformed[\"close\"].rolling(window=30).mean()\n",
-    "    print(\"\\\\nStep 4: Transform data\")\n",
-    "    print(transformed.head())\n",
-    "    return transformed\n",
-    "\n",
-    "def load_data(df: pd.DataFrame, output_file: str) -> None:\n",
-    "    df.to_csv(output_file, index=False)\n",
-    "    print(f\"\\\\nStep 5: Load data\")\n",
-    "    print(f\"Saved cleaned data to: {output_file}\")\n",
-    "\n",
-    "def plot_data(df: pd.DataFrame) -> None:\n",
-    "    plt.figure(figsize=(10, 5))\n",
-    "    plt.plot(df[\"date\"], df[\"close\"])\n",
-    "    plt.show()\n",
-    "\n",
-    "def main():\n",
-    "    url = \"https://raw.githubusercontent.com/vega/vega-datasets/main/data/sp500-2000.csv\"\n",
-    "    output_file = \"clean_sp500_2000.csv\"\n",
-    "\n",
-    "    raw_df = extract_data(url)\n",
-    "    selected_df = select_columns(raw_df)\n",
-    "    cleaned_df = clean_data(selected_df)\n",
-    "    transformed_df = transform_data(cleaned_df)\n",
-    "    load_data(transformed_df, output_file)\n",
-    "    plot_data(transformed_df)'''"
-   ],
-   "id": "85c4fdcfef2fa543",
-   "outputs": [],
-   "execution_count": 7
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "ab7e07559b2247ee"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From b36628f5c7d0140bec730a8e1f3731ec3177e428 Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Sat, 7 Mar 2026 02:04:14 -0600
Subject: [PATCH 08/12] v2.1

---
 amber/src/main/python/pytexera/__init__.py                      | 2 ++
 .../operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala   | 1 -
 .../amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala  | 1 -
 .../amber/operator/udf/python/PythonTableReducerOpDesc.scala    | 1 -
 .../texera/amber/operator/udf/python/PythonUDFOpDescV2.scala    | 1 -
 .../operator/udf/python/PythonUdfUiParameterInjector.scala      | 2 +-
 .../operator/udf/python/source/PythonUDFSourceOpDescV2.scala    | 2 +-
 7 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/amber/src/main/python/pytexera/__init__.py b/amber/src/main/python/pytexera/__init__.py
index e40d1a43fe0..8ad44fa55e3 100644
--- a/amber/src/main/python/pytexera/__init__.py
+++ b/amber/src/main/python/pytexera/__init__.py
@@ -30,6 +30,7 @@
     UDFSourceOperator,
 )
 from core.models.type.large_binary import largebinary
+from core.models.schema.attribute_type import *
 
 __all__ = [
     "State",
@@ -53,4 +54,5 @@
     "Iterator",
     "Optional",
     "Union",
+    "AttributeType",
 ]
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
index 975ff0d7edd..a7a02560901 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/DualInputPortsPythonUDFOpDescV2.scala
@@ -36,7 +36,6 @@ class DualInputPortsPythonUDFOpDescV2 extends LogicalOp {
       "# Choose from the following templates:\n" +
         "# \n" +
         "# from pytexera import *\n" +
-        "# from core.models.schema.attribute_type import *\n" +
         "# \n" +
         "# class ProcessTupleOperator(UDFOperatorV2):\n" +
         "#     \n" +
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala
index c18ed1ee8be..9b66dde0759 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonLambdaFunctionOpDesc.scala
@@ -78,7 +78,6 @@ class PythonLambdaFunctionOpDesc extends PythonOperatorDescriptor {
     // build the python udf code
     var code: String =
       "from pytexera import *\n" +
-        "from core.models.schema.attribute_type import *\n" +
         "class ProcessTupleOperator(UDFOperatorV2):\n" +
         "    @overrides\n" +
         "    def process_tuple(self, tuple_: Tuple, port: int) -> Iterator[Optional[TupleLike]]:\n"
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonTableReducerOpDesc.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonTableReducerOpDesc.scala
index 2221c6a1fa2..0c2bb07ff13 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonTableReducerOpDesc.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonTableReducerOpDesc.scala
@@ -57,7 +57,6 @@ class PythonTableReducerOpDesc extends PythonOperatorDescriptor {
 
     s"""
        |from pytexera import *
-       |from core.models.schema.attribute_type import *"+
        |class ProcessTableOperator(UDFTableOperator):
        |
        |    @overrides
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
index dcac72cc900..efac09d63b4 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUDFOpDescV2.scala
@@ -38,7 +38,6 @@ class PythonUDFOpDescV2 extends LogicalOp {
       "# Choose from the following templates:\n" +
         "# \n" +
         "# from pytexera import *\n" +
-        "# from core.models.schema.attribute_type import *\n" +
         "# \n" +
         "# class ProcessTupleOperator(UDFOperatorV2):\n" +
         "#     \n" +
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
index 189882b7dc6..07ad7cd6b0d 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
@@ -25,7 +25,7 @@ import scala.util.matching.Regex
 
 object PythonUdfUiParameterInjector {
 
-  private val ReservedHookMethod = "_texera_injected_ui_parameters"
+  private val ReservedHookMethod = "@overrides\n_texera_injected_ui_parameters"
 
   // Match user-facing UDF classes (the ones users write)
   private val SupportedUserClassRegex: Regex =
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
index 3a9eddd465e..23033621209 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/source/PythonUDFSourceOpDescV2.scala
@@ -34,7 +34,7 @@ class PythonUDFSourceOpDescV2 extends SourceOperatorDescriptor {
   @JsonProperty(
     required = true,
     defaultValue =
-      "# from pytexera import *\n" + "# from core.models.schema.attribute_type import *\n" +
+      "# from pytexera import *\n" +
         "# class GenerateOperator(UDFSourceOperator):\n" +
         "# \n" +
         "#     @overrides\n" +

From dd46609b13382aa9bdfb877ac52aa569b780e1a1 Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Sat, 7 Mar 2026 02:10:14 -0600
Subject: [PATCH 09/12] Update ui-udf-parameters.component.scss

---
 .../ui-udf-parameters.component.scss          | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
index 5b7e498731e..901edaf1fc9 100644
--- a/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
+++ b/frontend/src/app/workspace/component/ui-udf-parameters/ui-udf-parameters.component.scss
@@ -1,3 +1,22 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
 .ui-udf-param-row {
   display: grid;
   grid-template-columns: 250px 250px 1fr;

From 2c8167bbd0bb4d653286ba96e47506bd7674bab8 Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Sat, 7 Mar 2026 03:07:47 -0600
Subject: [PATCH 10/12] v2.1

---
 .../core/models/schema/attribute_type.py      |  2 +-
 .../texera/amber/engine/common/Utils.scala    |  5 ++++
 .../python/PythonUdfUiParameterInjector.scala | 29 +++++++++++++++----
 3 files changed, 29 insertions(+), 7 deletions(-)

diff --git a/amber/src/main/python/core/models/schema/attribute_type.py b/amber/src/main/python/core/models/schema/attribute_type.py
index 0e56ec10fb6..c76c8b4d9cd 100644
--- a/amber/src/main/python/core/models/schema/attribute_type.py
+++ b/amber/src/main/python/core/models/schema/attribute_type.py
@@ -82,7 +82,7 @@ class AttributeType(Enum):
     AttributeType.INT: int,
     AttributeType.LONG: int,
     AttributeType.DOUBLE: float,
-    AttributeType.BOOL: lambda v: str(v).strip().lower() in ("true", "1", "yes"),
+    AttributeType.BOOL: lambda v: str(v).strip().lower() in ("True", "true", "1", "yes"),
     AttributeType.BINARY: lambda v: v if isinstance(v, bytes) else str(v).encode(),
     AttributeType.TIMESTAMP: lambda v: datetime.datetime.fromisoformat(v),
     AttributeType.LARGE_BINARY: largebinary,
diff --git a/amber/src/main/scala/org/apache/texera/amber/engine/common/Utils.scala b/amber/src/main/scala/org/apache/texera/amber/engine/common/Utils.scala
index dc074c1094d..7319d7e824f 100644
--- a/amber/src/main/scala/org/apache/texera/amber/engine/common/Utils.scala
+++ b/amber/src/main/scala/org/apache/texera/amber/engine/common/Utils.scala
@@ -36,6 +36,11 @@ object Utils extends LazyLogging {
     *
     * @return the real absolute path to amber home directory
     */
+
+  import java.nio.file.{Files, Path, Paths}
+  import scala.jdk.CollectionConverters._
+  import scala.util.Using
+
   lazy val amberHomePath: Path = {
     val currentWorkingDirectory = Paths.get(".").toRealPath()
     // check if the current directory is the amber home path
diff --git a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
index 07ad7cd6b0d..67b9ca67bb9 100644
--- a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
+++ b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/udf/python/PythonUdfUiParameterInjector.scala
@@ -25,7 +25,7 @@ import scala.util.matching.Regex
 
 object PythonUdfUiParameterInjector {
 
-  private val ReservedHookMethod = "@overrides\n_texera_injected_ui_parameters"
+  private val ReservedHookMethod = "_texera_injected_ui_parameters"
 
   // Match user-facing UDF classes (the ones users write)
   private val SupportedUserClassRegex: Regex =
@@ -54,7 +54,7 @@ object PythonUdfUiParameterInjector {
       uiParameters: List[UiUDFParameter]
   ): PythonTemplateBuilder = {
     val entries = uiParameters.map { parameter =>
-      pyb"${parameter.attribute.getName()}: ${parameter.value}"
+      pyb"'${parameter.attribute.getName()}': ${parameter.value}"
     }
 
     entries.reduceOption((acc, entry) => acc + pyb", " + entry).getOrElse(pyb"")
@@ -64,11 +64,24 @@ object PythonUdfUiParameterInjector {
     val injectedParametersMap = buildInjectedParametersMap(uiParameters)
 
     // unindented method; we indent it when inserting into the class body
-    (pyb"""def """ + pyb"$ReservedHookMethod" + pyb"""(self):
-                                                     |    return {""" +
+    (pyb"""|@overrides
+            |def """ + pyb"$ReservedHookMethod" + pyb"""(self) -> typing.Dict[str, typing.Any]:
+                                                       |    return {""" +
       injectedParametersMap +
       pyb"""}
-           |""").encode
+             |""").encode
+  }
+
+  private def ensureTypingImport(encodedUserCode: String): String = {
+    val alreadyImported = encodedUserCode
+      .split("\n", -1)
+      .exists(_.trim == "import typing")
+
+    if (alreadyImported) {
+      encodedUserCode
+    } else {
+      "import typing\n" + encodedUserCode
+    }
   }
 
   private def indentBlock(block: String, indent: String): String = {
@@ -149,6 +162,7 @@ object PythonUdfUiParameterInjector {
       indentedHook +
       encodedUserCode.substring(classBlockEnd)
   }
+
   private def inferClassBodyIndent(classBlock: String, classIndent: String): Option[String] = {
     val lines = classBlock.split("\n", -1).toList.drop(1) // skip class header line
 
@@ -158,6 +172,7 @@ object PythonUdfUiParameterInjector {
         if (leading.length > classIndent.length) leading else classIndent + "    "
     }
   }
+
   def inject(code: String, uiParameters: List[UiUDFParameter]): String = {
     val params = Option(uiParameters).getOrElse(List.empty)
     validate(params)
@@ -170,10 +185,12 @@ object PythonUdfUiParameterInjector {
       return encodedUserCode
     }
 
+    val encodedUserCodeWithTypingImport = ensureTypingImport(encodedUserCode)
+
     // Build encoded hook method (contains self.decode_python_template(...))
     val hookMethod = buildInjectedHookMethod(params)
 
     // Inject hook into the UDF class body; Python base class will auto-call it before open()
-    injectHookIntoUserClass(encodedUserCode, hookMethod)
+    injectHookIntoUserClass(encodedUserCodeWithTypingImport, hookMethod)
   }
 }

From 06d7f5a93b027cb532a0d8c95d158b589b6fa5f3 Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Sat, 7 Mar 2026 03:16:57 -0600
Subject: [PATCH 11/12] v3

---
 .../scala/org/apache/texera/amber/engine/common/Utils.scala  | 5 -----
 .../apache/texera/web/service/ExecutionResultService.scala   | 2 +-
 2 files changed, 1 insertion(+), 6 deletions(-)

diff --git a/amber/src/main/scala/org/apache/texera/amber/engine/common/Utils.scala b/amber/src/main/scala/org/apache/texera/amber/engine/common/Utils.scala
index 7319d7e824f..dc074c1094d 100644
--- a/amber/src/main/scala/org/apache/texera/amber/engine/common/Utils.scala
+++ b/amber/src/main/scala/org/apache/texera/amber/engine/common/Utils.scala
@@ -36,11 +36,6 @@ object Utils extends LazyLogging {
     *
     * @return the real absolute path to amber home directory
     */
-
-  import java.nio.file.{Files, Path, Paths}
-  import scala.jdk.CollectionConverters._
-  import scala.util.Using
-
   lazy val amberHomePath: Path = {
     val currentWorkingDirectory = Paths.get(".").toRealPath()
     // check if the current directory is the amber home path
diff --git a/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala b/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
index 3f0362f8242..b7907a45f90 100644
--- a/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
+++ b/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
@@ -19,10 +19,10 @@
 
 package org.apache.texera.web.service
 
-import org.apache.pekko.actor.Cancellable
 import com.fasterxml.jackson.annotation.{JsonTypeInfo, JsonTypeName}
 import com.fasterxml.jackson.databind.node.ObjectNode
 import com.typesafe.scalalogging.LazyLogging
+import org.apache.pekko.actor.Cancellable
 import org.apache.texera.amber.config.ApplicationConfig
 import org.apache.texera.amber.core.storage.model.VirtualDocument
 import org.apache.texera.amber.core.storage.result._

From ac2b265fa9dee5733e2fca01b9a3b567dcab803a Mon Sep 17 00:00:00 2001
From: Carlos Ernesto Alvarez Berumen <carloea2@uci.edu>
Date: Sat, 7 Mar 2026 03:20:51 -0600
Subject: [PATCH 12/12] v3

---
 .../org/apache/texera/web/service/ExecutionResultService.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala b/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
index b7907a45f90..3f0362f8242 100644
--- a/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
+++ b/amber/src/main/scala/org/apache/texera/web/service/ExecutionResultService.scala
@@ -19,10 +19,10 @@
 
 package org.apache.texera.web.service
 
+import org.apache.pekko.actor.Cancellable
 import com.fasterxml.jackson.annotation.{JsonTypeInfo, JsonTypeName}
 import com.fasterxml.jackson.databind.node.ObjectNode
 import com.typesafe.scalalogging.LazyLogging
-import org.apache.pekko.actor.Cancellable
 import org.apache.texera.amber.config.ApplicationConfig
 import org.apache.texera.amber.core.storage.model.VirtualDocument
 import org.apache.texera.amber.core.storage.result._