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A dynamic AI chatbot leveraging Python, Flask, and PyTorch for natural language understanding. Features include real-time interaction, API integration, and Postman testing. Scalable and designed for seamless website or app integration. Perfect for inquiries, bookings, and customer support.

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Python Flask AI Chatbot

Python Flask AI Chatbot Postman

A dynamic and responsive AI Chatbot built with Python, Flask, and neural networks. Designed to handle inquiries about services, bookings, and customer support with ease and accuracy.

Features

  • Real-time Interaction: Engage with users through a conversational interface.
  • Custom Neural Network: Powered by a bespoke model for natural language understanding.
  • Easy Integration: Seamless incorporation into websites or applications.
  • Scalable & Flexible: Designed to grow with your business needs.
  • API Testing with Postman: Easily test and interact with the chatbot API.

Installation

  1. Clone the Repository

    git gh repo clone Gayan-Sachintha/Riverston-Life-AI-Chatbot-flask
    cd yourprojectname
  2. Create a Virtual Environment (Optional)

    python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
  3. Install Requirements

    pip install -r requirements.txt
  4. Set Up Environment Variables

    Create a .env file in the project root directory and add the necessary configurations.

Usage

Start the server with the following command:

python app.py

The chatbot is now live at http://localhost:5000/. Interact with the chatbot through the provided endpoints.

Testing with Postman

To test the chatbot API with Postman, follow these steps:

  1. Install Postman

    Download and install Postman from the official site.

  2. Import Collection

    Create a new collection in Postman for your chatbot APIs.

  3. Create a POST Request

    • URL: http://localhost:5000/predict
    • Body type: raw (JSON)
    • Request Body Example: {"message": "Hello"}
  4. Send Request

    Hit the send button to receive a response from your chatbot.

This setup allows you to easily test and debug your chatbot's responses.

Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the repository.
  2. Create a new branch: git checkout -b feature-yourfeaturename.
  3. Make your changes and commit them: git commit -m 'Add some feature'.
  4. Push to the branch: git push origin feature-yourfeaturename.
  5. Submit a pull request.

License

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

Contact

For support or inquiries, reach out to us at gayansachintha2000@gmail.com.


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A dynamic AI chatbot leveraging Python, Flask, and PyTorch for natural language understanding. Features include real-time interaction, API integration, and Postman testing. Scalable and designed for seamless website or app integration. Perfect for inquiries, bookings, and customer support.

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