RailSaathi - AI-Powered Railway Assistance System

Project Overview

RailSaathi is an AI-powered railway assistance system that simplifies ticket booking for the Indian Railways. It features user authentication, train search, real-time seat booking, and AI-driven suggestions. Built with Flask, SQLite, and JavaScript, this project aims to enhance user experience and streamline railway operations. Deployable on Render.

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Features

• User Authentication: Secure login and registration processes.
• Train Search Functionality: Search for trains between specified source and destination stations.
• Seat Booking: Real-time seat booking with immediate confirmation.
• AI-Powered Suggestions: Intelligent recommendations for seat selection.
• User Dashboard: Manage bookings and view travel history.
• Responsive Design: Mobile-friendly interface for all devices.

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Technologies Used

• Backend: Flask (Python)
• Database: SQLite with SQLAlchemy ORM
• Frontend: HTML, CSS, JavaScript
• AI Model: Machine learning algorithms for seat suggestions
• Deployment: Render

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Setup and Installation

Prerequisites

• Python 3.13.0 installed
• SQLite3 installed
• Git installed

Steps to Run Locally

1. Clone the repository:

   git clone https://github.com/yourusername/RailSaathi.git

2. Change into the project directory:

   cd RailSaathi

3. Create a virtual environment and activate it:

   python3 -m venv venv

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the required dependencies:

   pip install -r requirements.txt

5. Set up the database:

   • Import the provided SQL script to set up the database:

     railsaathi.sql

   • Update the .env file with your SQLite credentials:

     DB_URI=sqlite:///railsathi.db
     

6. Run the application:

   python app.py

7. Access the application: Open your browser and visit:

   http://127.0.0.1:5000
   

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Deployment

Render

1. Add a .env file with your environment variables:

   DB_URI=sqlite:///railsathi.db
   DATABASE_URL=sqlite:///railsaathi.db
   SECRET_KEY=your_secret_key
   FLASK_APP=app.app
   

2. Update the render.yaml with necessary deployment configurations.
3. Push the changes to your repository and link it with Render.

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Environment Variables

• Make sure .env is added to .gitignore for security.
• Required variables:
  • DB_URI: SQLite database connection string.
  • DATABASE_URL: SQLite database connection string.
  • SECRET_KEY: Your secret key for Flask application.
  • FLASK_APP: Entry point of your Flask application.

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File Structure Overview

• app/: Contains all Python scripts and core logic.
  • ai_logic.py: AI recommendation system logic.
  • auth.py: User authentication.
  • bookings.py: Booking management.
  • forms.py: Web forms.
  • models.py: Database models.
  • trains.py: Train search and management.
• static/: Static assets like CSS, JavaScript, and images.
• templates/: HTML templates for rendering pages.
• migrations/: Alembic migration files for database changes.

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Future Work

• Real-time train updates and notifications.
• Expand the AI model for personalized recommendations.
• Add multi-language support.
• Integrate third-party APIs for food ordering and other services.

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Team Members

• Ayush Goel - Backend, Database Integration, AI Logic, Deployment
• Meghna Singh - Frontend UI/UX, CSS Design, Template Structure
• Rajeev Mishra - Frontend JavaScript & Dynamic Features
• Azad Tiwari - User Dashboard, Interactive Components

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Contributing

Contributions are welcome! Please open an issue or submit a pull request for any suggestions or improvements.

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License

This project is licensed under the MIT License - see the LICENSE file for details.