nofunciona

src/app.py

@@ -1,4 +1,61 @@
-from utils import db_connect
-engine = db_connect()
+from flask import Flask, request, render_template 
+import os
+from joblib import load
 
-# your code here
+app = Flask(__name__)
+
+model =load(open("/workspaces/Appwebs/models/salary_predictor_model.sav","rb"))
+print("modelo cargado")
+
+
+# Generar opciones dinámicas desde dataset
+options = {
+    "job_title": sorted(df["job_title"].dropna().unique()),
+    "education_level": sorted(df["education_level"].dropna().unique()),
+    "industry": sorted(df["industry"].dropna().unique()),
+    "company_size": sorted(df["company_size"].dropna().unique()),
+    "location": sorted(df["location"].dropna().unique())
+}
+
+def preprocess_input(data):
+    df_input = pd.DataFrame([data])
+
+    if columns is not None:
+        df_input = pd.get_dummies(df_input)
+
+        for col in columns:
+            if col not in df_input:
+                df_input[col] = 0
+
+        df_input = df_input[columns]
+
+    return df_input
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    prediction = None
+
+    if request.method == "POST":
+        try:
+            input_data = {
+                "job_title": request.form.get("job_title"),
+                "experience_years": int(request.form.get("experience_years")),
+                "education_level": request.form.get("education_level"),
+                "skills_count": int(request.form.get("skills_count")),
+                "industry": request.form.get("industry"),
+                "company_size": request.form.get("company_size"),
+                "location": request.form.get("location"),
+                "remote_work": int(request.form.get("remote_work")),
+                "certifications": int(request.form.get("certifications"))
+            }
+
+            processed = preprocess_input(input_data)
+            prediction = round(model.predict(processed)[0], 2)
+
+        except Exception as e:
+            prediction = f"Error: {e}"
+
+    return render_template("index.html", prediction=prediction, options=options)
+
+if __name__ == "__main__":
+    app.run(debug=True)