NeuralNexus - AI Chatbot

NeuralNexus is an intelligent chatbot powered by TensorFlow and Natural Language Processing (NLP). It uses a neural network to understand and respond to user queries with context awareness and personality.

Features

• 🤖 Natural Language Understanding
• 🧠 Neural Network-based Response Generation
• 🔄 Context-Aware Conversations
• 💬 Modern Web Interface
• 🎨 Beautiful UI with Animations
• 📱 Responsive Design
• ⚡ Real-time Processing

Prerequisites

• Python 3.10 or higher
• pip (Python package installer)
• Virtual environment (recommended)

Installation

1. Clone the repository:

git clone https://github.com/tanishpoddar/NeuralNexus.git
cd NeuralNexus

2. Create and activate a virtual environment:

# Windows
python -m venv .venv
.venv\Scripts\activate

# Linux/MacOS
python3 -m venv .venv
source .venv/bin/activate

3. Install required packages:

pip install -r requirements.txt

Project Structure

NeuralNexus/
├── main-host.py          # Main application file
├── intents.json          # Training data and responses
├── model.weights.h5      # Trained model weights
├── data.pickle           # Processed training data
├── templates/
│   └── index.html        # Web interface
└── requirements.txt      # Python dependencies

Required Packages

• tensorflow==2.15.0
• numpy==1.24.3
• nltk==3.8.1
• flask==3.0.0
• python-dotenv==1.0.0

Running the Application

1. Ensure you're in the virtual environment
2. Run the Flask application:

python main-host.py

3. Open your browser and navigate to:

http://localhost:5000

Training the Model

The model is automatically trained when you run the application. It:

1. Processes the intents from intents.json
2. Creates a bag-of-words representation
3. Trains a neural network with:
   • Input layer: Size based on vocabulary
   • Hidden layers: Two layers with 16 neurons each
   • Dropout layers: To prevent overfitting
   • Output layer: Size based on number of intents

Customization

Adding New Intents

Edit intents.json to add new conversation patterns and responses:

{
    "tag": "new_intent",
    "patterns": ["pattern1", "pattern2"],
    "responses": ["response1", "response2"]
}

Troubleshooting

Common Issues

1. Import Errors

   • Ensure all packages are installed correctly
   • Check Python version compatibility
   • Verify virtual environment activation

2. Model Training Issues

   • Delete model.weights.h5 and data.pickle
   • Restart the application to retrain

3. Web Interface Issues

   • Clear browser cache
   • Check console for JavaScript errors
   • Verify Flask server is running

Error Messages

• "ModuleNotFoundError": Install missing packages
• "TypeError": Check input format in intents.json
• "ValueError": Verify model architecture matches data

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the GNU General Public License v3.O License - see the LICENSE file for details.