🤖 Chatbot with Python and Machine Learning

This project demonstrates how to build an intelligent chatbot using Python, TensorFlow, and Natural Language Processing (NLP). The chatbot is trained to respond to various user queries based on predefined intents.

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🧠 Features

• NLP-based intent recognition using nltk
• Neural Network classifier using TensorFlow / Keras
• Interactive terminal chat interface
• Easily customizable intent dataset (intents.json)
• Persistence using model/tokenizer/encoder saving
• Lightweight Flask integration possible (for future deployment)

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🗂️ Folder Structure

📁 chatbot-ml/
│
├── intents.json              # Intents dataset (patterns + responses)
├── chatbot.ipynb             # Jupyter notebook with all training code
├── tokenizer.pickle          # Saved tokenizer for inference
├── label\_encoder.pickle      # Saved label encoder
├── chat\_model/               # Saved TensorFlow model
└── README.md                 # You're reading this!

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🧰 Requirements

Install the required packages:

pip install tensorflow==2.3.1
pip install nltk==3.5
pip install colorama==0.4.3
pip install numpy==1.18.5
pip install scikit-learn==0.23.2
pip install Flask==1.1.2

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📝 How It Works

1. Data Preparation

   • Load and process the intents.json file.
   • Use LabelEncoder to encode intent tags.
   • Tokenize and pad text using Keras tokenizer.

2. Model Training

   • Create a simple feed-forward neural network with:

     • Embedding Layer
     • GlobalAveragePooling1D
     • Dense Layers

   • Train for 500 epochs.

3. Model Saving

   • Save the trained model (chat_model)
   • Save the tokenizer and label encoder for inference.

4. Chat Function

   • Load saved model and tokenizer.
   • Predict intent of the user's message.
   • Reply with an appropriate response.

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🧪 Example Interaction

User: Hi
ChatBot: Hello!

User: What is your name?
ChatBot: I'm RISHU, your virtual assistant.

User: I forgot my password
ChatBot: Please click 'Forgot Password' on the login page to reset it.

User: quit

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🗃️ Customizing Intents

You can add or modify intents inside intents.json like:

{
  "tag": "greeting",
  "patterns": ["Hello", "Hi", "Hey"],
  "responses": ["Hi there!", "Hello!", "Hey!"]
}

Don't forget to re-train the model after editing!

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🚀 Future Improvements

• Add support for multiple languages
• Integrate with Flask API
• Deploy on web/mobile
• Contextual conversation memory

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💡 Credits

Built using:

• TensorFlow
• Scikit-Learn
• NLTK
• Keras

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📜 License

This project is for educational purposes and is open-source. Feel free to fork and improve!