ChatBot Using TensorFlow

Overview

This project is a simple chatbot implementation using TensorFlow and Keras. The bot is trained on a set of predefined intents, which allows it to understand user input and respond with appropriate responses. The model is a neural network that uses word embeddings and is designed to classify the user's input into one of several intent categories.

Features

• Text Classification: The bot can classify input sentences into predefined categories (intents).
• Word Embeddings: The model uses embeddings to convert words into numerical vectors that can be processed by the neural network.
• Sequential Model: The bot is built using a simple Sequential model from Keras.
• Customizable: You can easily update the intents and responses in the intents.json file.

Project Structure

.
├── chat_model                    # Saved model file after training
├── intents.json                  # JSON file containing the intents, patterns, and responses
├── label_encoder.pickle          # Pickle file containing the trained label encoder
├── tokenizer.pickle              # Pickle file containing the trained tokenizer
├── chatbot.py                    # Main script to train and run the chatbot
└── README.md                     # This README file

Getting Started

Prerequisites

• Python 3.x
• TensorFlow
• Keras
• scikit-learn
• numpy
• colorama

2. Install the required packages:

   pip install -r requirements.txt
   

3. Prepare your intents:

   • The intents.json file contains the training data for the bot. You can modify it to include more intents, patterns, and responses.

Training the Model

To train the model, simply run the chatbot.py script:

python chatbot.py

This will load the training data, preprocess it, and train a neural network model. The trained model, along with the tokenizer and label encoder, will be saved for future use.

Using the Chatbot

After training the model, you can start a conversation with the bot by running the chatbot.py script:

python chatbot.py

The bot will continuously take user input until you type "quit" to exit the chat.

Customization

• Adding New Intents: To add new intents, update the intents.json file with new patterns and responses. Make sure to retrain the model after making changes to this file.

• Modifying the Model: If you want to experiment with different architectures, you can modify the neural network defined in the chatbot.py script.

Example Intents

Here's a snippet of what the intents.json file looks like:

{
    "intents": [
        {
            "tag": "greeting",
            "patterns": ["Hi", "Hello", "How are you?"],
            "responses": ["Hello!", "Hi there!", "Greetings!"]
        },
        {
            "tag": "goodbye",
            "patterns": ["Bye", "See you later", "Goodbye"],
            "responses": ["Goodbye!", "See you later!", "Take care!"]
        }
        // Add more intents here
    ]
}

Contributing

If you'd like to contribute to this project, feel free to fork the repository and submit a pull request. Please ensure that your code adheres to the project's coding standards and is well-documented.

License

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

Acknowledgements

This project was inspired by various chatbot tutorials and TensorFlow examples.