AI Chatbot with Flask and GPT-2

Introduction

This project is a web-based AI chatbot built using Flask and GPT-2, with a simple HTML frontend for user interaction. The chatbot processes user input, generates responses using a pretrained GPT-2 model, and communicates with the backend via AJAX.

Table of Contents

• Features
• Installation
• Usage
• File Structure
• API Endpoints
• Dependencies
• Configuration

Features

• Real-Time Chat: Interactive chat interface for users.
• GPT-2 Responses: Generates human-like responses using GPT-2.
• Flask Backend: Handles API requests and integrates with the chatbot model.
• AJAX Communication: Sends and receives messages asynchronously.

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/ai-chatbot.git
   cd ai-chatbot

2. Install dependencies:

   pip install Flask transformers nltk

3. Download NLTK resources:

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')

4. Run the Flask server:

   python app.py

5. Open the browser and navigate to:

   http://127.0.0.1:5000/
   

File Structure

• app.py: Flask backend handling requests and generating responses.
• index.html: Frontend HTML for the chat interface.
• static/: Directory for static files (CSS, JS).

API Endpoints

/chat (POST)

• Request: JSON payload with a message field.
• Response: JSON object containing the bot's response.
• Example:

  curl -X POST http://127.0.0.1:5000/chat -H "Content-Type: application/json" -d '{"message": "Hello"}'

Dependencies

• Flask: For the web server and handling HTTP requests.
• transformers: For GPT-2 model usage.
• NLTK: For preprocessing input text.
• SQLite: For storing conversation history (in-memory database).

Install dependencies with:

pip install Flask transformers nltk

Configuration

• Model: The GPT-2 model is loaded from Hugging Face.
• Database: Uses an in-memory SQLite database.