AI-Powered Chatbot

Overview

This AI-Powered Chatbot project is designed to provide users with intelligent, real-time responses to queries. It features natural language processing (NLP) capabilities, conversation history storage, and integrations with external APIs for weather, news, and more.

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Key Features

1. Natural Language Processing (NLP):

   • Uses SpaCy for processing user input.
   • Pre-trained TensorFlow/Keras models for generating intelligent responses.

2. Conversation History:

   • Stores user-bot interactions in an SQLite database.
   • Easily retrievable for future reference or analysis.

3. External API Integration:

   • Examples include weather and news retrieval using external APIs.

4. Lightweight and Scalable:

   • Built on Flask, making it easy to deploy and scale.

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Technologies Used

• Programming Language: Python
• Framework: Flask
• Database: SQLite
• NLP Libraries: SpaCy, TensorFlow/Keras
• APIs: Placeholder for weather and news APIs

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Setup Instructions

Prerequisites

• Python 3.8+
• Pip (Python package manager)

Installation Steps

1. Clone the repository:

   git clone https://github.com/stephenombuya/-AI-Powered-Chatbot
   cd ai-powered-chatbot

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Initialize the SQLite database:

   python
   >>> from app import init_db
   >>> init_db()
   >>> exit()

5. Run the application:

   python app.py

6. Access the chatbot at http://127.0.0.1:5000.

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API Endpoints

1. Chat Endpoint

   • URL: /chat
   • Method: POST
   • Payload Example:

     {
       "message": "Hello, chatbot!"
     }

   • Response Example:

     {
       "response": "I'm here to help!"
     }

2. History Endpoint

   • URL: /history
   • Method: GET
   • Response Example:

     [
       {
         "user_message": "Hello",
         "bot_response": "Hi there!",
         "timestamp": "2025-01-08 12:30:45"
       }
     ]

3. External API Endpoint

   • URL: /api
   • Method: GET
   • Query Parameters:
     • type: weather or news
   • Response Example:

     {
       "weather": "Sunny, 25°C"
     }

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File Structure

• app.py: Main application logic
• chat_history.db: SQLite database for storing conversation history
• requirements.txt: Dependencies for the project
• README.md: Project documentation
• chatbot_model.h5: Pre-trained model (assumed to be in the root directory)

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Future Improvements

1. Enhance NLP capabilities with advanced models (e.g., GPT, BERT).
2. Add more external API integrations (e.g., stock market, sports updates).
3. Implement a frontend interface for better user interaction.
4. Improve database management and add analytics.

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Contribution Guidelines

1. Fork the repository and create your branch:

   git checkout -b feature-name

2. Commit your changes and push them:

   git commit -m "Add feature-name"
   git push origin feature-name

3. Submit a pull request for review.

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License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.

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Contact

For questions or support, contact Stephen Ombuya.