Personal AI Chatbot

This project is a Personal AI Chatbot that allows users to interact with an AI in real time. Powered by Hugging Face’s DialoGPT-medium model, Flask backend, and a ReactJS frontend (built with Vite), this chatbot is responsive, scalable, and perfect for exploring conversational AI.

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Features

• Real-Time Conversations: Responds to user inputs instantly using DialoGPT.
• Clean User Interface: Built with ReactJS for a modern and responsive chat experience.
• Scalable Backend: Flask handles API requests and processes AI responses.

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Tech Stack

Frontend

• ReactJS with Vite for fast development and HMR.
• Axios for API communication.

Backend

• Flask for serving the API.
• Hugging Face’s Transformers library for AI model inference.

AI Model

• DialoGPT-medium from Hugging Face for conversational AI.

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Installation and Setup

Clone the Repository

Start by cloning the repository from GitHub:

git clone https://github.com/allanninal/personal-ai-chatbot.git
cd personal-ai-chatbot

Backend Setup

1. Navigate to the backend folder:

   cd backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate     # Windows

3. Install the dependencies from requirements.txt:

   pip install -r requirements.txt

4. Run the Flask server:

   python app.py

Frontend Setup

1. Navigate to the frontend folder:

   cd frontend

2. Install dependencies:

   npm install

3. Run the React development server:

   npm run dev

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Usage

1. Start Chatting: Open the app in your browser at http://localhost:5173.
2. Real-Time Responses: Type a message in the chat input, and the chatbot will respond instantly.
3. Explore Conversations: Continue chatting with the AI to explore its capabilities.

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How It Works

1. Frontend (ReactJS): Provides a clean interface for user interaction.
2. Backend (Flask): Processes user inputs and generates AI responses using Hugging Face’s DialoGPT-medium model.
3. AI Model (DialoGPT-medium): Converts user inputs into context-aware, human-like responses.

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What’s Next?

1. Conversation History: Add persistent storage to maintain and display chat history.
2. Fine-Tuning the Model: Train the chatbot with custom datasets for specific use cases.
3. Voice Interaction: Incorporate speech-to-text and text-to-speech functionality for a voice-driven chatbot experience.
4. Advanced UI Styling: Enhance the frontend using CSS frameworks like Tailwind CSS or Material-UI.
5. Deployment: Host the chatbot on platforms like AWS, Heroku, or Vercel.
6. Alternative Models: Explore other conversational AI models:
   • OpenAI’s GPT-3.5 for advanced and nuanced responses.
   • BlenderBot for long-form conversations.
   • T5 (Text-to-Text Transfer Transformer) for versatile NLP tasks.

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License

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

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Support

If you find this project helpful, consider supporting me on Ko-fi:
ko-fi.com/allanninal