LogiPredict

LogiPredict is a full-stack web application that predicts shipment transit times based on historical routing data. It combines a Machine Learning prediction engine with a RESTful backend and a custom frontend interface.

Overview

• Predictive Engine: Uses a trained Random Forest Regressor to estimate delivery windows.
• API-First Architecture: The backend serves JSON data, decoupling the logic from the UI.
• Persistent Auditing: Logs every estimation request to a database using SQLAlchemy.
• Environment Agnostic: Falls back to a local SQLite database if a cloud PostgreSQL URL is not provided.
• Custom UI: A zero-dependency frontend built with Vanilla JS, CSS, and the Fetch API.

Tech Stack

• Backend: Python, Flask, Flask-SQLAlchemy, Flask-Marshmallow
• Machine Learning: Scikit-Learn, Pandas, Joblib
• Frontend: HTML5, CSS3, Vanilla JavaScript
• Server: Gunicorn

Running the Application Locally

1. Clone the repository:

   git clone https://github.com/shakeelsaga/LogiPredict.git
   cd LogiPredict

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate

   On Windows, use venv\Scripts\activate

3. Install the dependencies:

   pip install -r requirements.txt

4. Configure your environment variables: Create a .env file in the root directory and add a secret key:

   SECRET_KEY=your_generated_secret_key_here
   

   Leave DATABASE_URL blank to default to local SQLite.

5. Run the server:

   python run.py

   The application will be available at http://127.0.0.1:5000.

Using the API

The backend exposes a RESTful endpoint for transit time calculation.

POST /api/predict

Calculates the estimated transit time and logs the request to the database.

Request Body (JSON):

{
  "origin": "DELHI",
  "destination": "CHENNAI",
  "weight": 5.3,
  "service": "FEDEX_EXPRESS_SAVER"
}

Success Response (200 OK):

{
  "id": 1,
  "created_at": "2026-03-06T15:10:31.440476",
  "origin_city": "DELHI",
  "destination_city": "CHENNAI",
  "weight_kg": 5.3,
  "service_type": "FEDEX_EXPRESS_SAVER",
  "predicted_hours": 12.88
}

License

This project is licensed under the MIT License.