Chatbot - Multi-Model Conversational AI with Flask

A ChatGPT-like chatbot built with Flask, supporting rule-based responses, machine learning conversation models, and transformer-based dialogue systems. Persistent chat history, sentiment analysis, and FAQ analytics are included. Easily deployable with Docker or on traditional platforms like Render.

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✨ Features

• 🐩 Three Chatbot Modes:
  • Rule-Based Chatbot (simple if-else logic)
  • Machine Learning Chatbot (using scikit-learn models)
  • Transformer-Based Chatbot (using HuggingFace Transformers)
• 💬 Room-specific conversations
• 🧠 Persistent chat history with MongoDB
• 📊 Sentiment analysis for each message (TextBlob)
• 🚀 Analytics for most frequently asked questions
• 🚨 Real-time communication with Flask-SocketIO
• 🚧 Dockerized for seamless deployment

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💡 Technology Stack

• Backend: Flask, Flask-SocketIO
• Database: MongoDB
• NLP: TextBlob, scikit-learn, Transformers (HuggingFace)
• Deployment: Render / Docker

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/JPdev6/chatbot.git
cd chatbot

2. Set up the environment

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

3. Run locally

python app.py

Visit http://localhost:5000 in your browser.

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🚧 Docker Deployment (Optional)

You can run the entire chatbot inside a Docker container:

docker build -t chatbot .
docker run -p 5000:5000 chatbot

Or use docker-compose to spin up with MongoDB:

docker-compose up --build

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👀 Live Demo

Easily deployable on Render:

• Configure gunicorn as the start command: gunicorn app:app
• Use the provided Procfile and requirements.txt.

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💪 Contributions

Contributions, issues, and feature requests are welcome!

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© License

This project is licensed under the MIT License.