🤖 Chatbot — Neural Network Chatbot using TensorFlow & Flask

A simple Neural Network (NN)-based chatbot built in Python, trained using TensorFlow and deployed locally using Flask.
This chatbot responds to user messages based on trained conversational intents and can be easily run on your machine.

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🧠 Features

• 🗨️ Conversational chatbot powered by a neural network
• 📦 Trained using TensorFlow
• 🚀 Served locally via a Flask web app
• 🔌 Easy to extend with more intents
• 🛠 Uses NLP preprocessing and intent classification

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🛠 Prerequisites

Make sure you have the following installed:

• Python 3.8+
• pip (Python package installer)

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📥 Installation

Clone the repository:

git clone https://github.com/ShreedharG/Chatbot.git
cd Chatbot

Create and activate a virtual environment (recommended):

Linux / macOS

python -m venv venv
source venv/bin/activate

Windows

python -m venv venv
venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

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▶️ Running the Chatbot

Start the Flask application:

python app.py

The chatbot will be available at localhost.

Open this URL in your browser to interact with the chatbot.

🧪 Training the Model (Optional)

If you want to retrain the chatbot with updated intents:

Modify intents.json with new patterns and responses.

Run the training script:

python nlp_model.py

This will generate updated model and tokenizer files.

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💡How It Works

• User input is tokenized and cleaned using NLP techniques
• Input is vectorized using a bag-of-words approach
• A neural network predicts the intent class
• A response is selected from the predicted intent
• Flask serves the chatbot through a web interface

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📌Use Cases

• Learning NLP and neural networks
• Academic mini-project
• Base template for intent-based chatbots
• Backend chatbot logic for web applications

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🙌Contributing

Contributions are welcome! Open a Pull Request

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📁 Repository Structure

.
├── static/
├── templates/
├── app.py              # Flask app
├── chatbot.py          # Chatbot logic & response generation
├── chatbot_model.h5    # Trained neural network model
├── classes.pkl         # Label encoder classes
├── db_helper.py        # Database helper (if used)
├── function.py         # Utility/helper functions
├── intents.json        # Intent definitions (training data)
├── nlp_model.py        # NLP preprocessing & model training
├── requirements.txt    # Python dependencies
├── welcome_msg.txt     # Startup / welcome message
├── words.pkl           # Tokenized vocabulary
└── README.md