Intent-Chatbot

Intents Chatbot (Chatlee) 🤖

Chatlee is a simple AI-powered chatbot built with TensorFlow, Keras, and Streamlit. It uses Natural Language Processing (NLP) to detect user intent and respond accordingly. This project is designed as a hands-on, practical introduction to building and deploying a basic intent-based chatbot.

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✨ Features

• Intent Detection: Classifies user queries into pre-defined intents using a deep learning model.
• Text Preprocessing: Uses NLTK for tokenization, stopword removal, and lemmatization.
• Pre-trained Embeddings: Integrates GloVe word vectors for richer language understanding.
• Interactive Chat UI: Built with Streamlit for a clean, browser-based chat experience.
• Session Chat History: Remembers the full conversation in the browser session.
• Easily Expandable: Add more intents and responses by editing intents.json.
• Simple, Fast, and Fun to Use!

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🚀 Quick Start

1. Clone this repo or download all files.
2. Install dependencies:

   pip install -r requirements.txt

   (If requirements.txt is missing, install: streamlit tensorflow nltk numpy pickle5 and others as needed.)
3. Run the app:

   streamlit run app.py

4. Open your browser to:
   http://localhost:8501
5. Start chatting!

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🛠️ Files & Folders

• app.py - Streamlit web app code
• intents.json - Your intent patterns and responses
• intents_chatbot_2025.keras - Trained model
• tokenizer.pkl - Fitted tokenizer object
• label_encoder.pkl - Fitted label encoder
• intents_chatbot_logo.png - (Optional) Logo for your bot
• README.md - This file!

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🧠 How It Works

• Data Preparation: All possible user inputs and bot responses are defined in intents.json.
• NLP Preprocessing: Each user message is tokenized, lowercased, cleaned, and lemmatized.
• Model Prediction: The message is vectorized and fed to a Keras LSTM model using GloVe embeddings.
• Intent Matching: The model predicts the intent tag, and a matching response is randomly chosen from the tag’s responses.
• Persistent Chat: Conversation history is stored in the browser with Streamlit’s session_state.

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📈 Limitations

• Not a “true AI” chatbot—can only answer intents it’s trained on.
• Cannot answer open-domain or highly complex questions.
• Best used as a starting point for learning/development.

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👤 Author

Built by Khush Thakkar.

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Pull requests and suggestions are welcome!