Skip to content

README.md

Latest commit

 

History

History
63 lines (40 loc) · 1.68 KB

README.md

File metadata and controls

63 lines (40 loc) · 1.68 KB

🤖 Mini Chatbot Project in Python

A Python-based chatbot that uses Natural Language Processing (NLP) and a neural network to understand and respond to user inputs. This project demonstrates how to design, train, and deploy a simple chatbot using custom intents.

🚀 Features

  • Interactive chatbot for text-based queries.
  • Customizable intents in intents.json.
  • Uses Bag of Words (BoW) and one-hot encoding for NLP.
  • Neural network model built with TensorFlow/Keras.
  • Lightweight and easy to extend or adapt.

📁 File Structure

  • intents.json: Predefined patterns, tags, and responses.
  • ChatBot.ipynb: Jupyter Notebook with model training and chatbot interface.
  • README.md: Project documentation (this file).

🧰 Requirements

Install dependencies with:

pip install tensorflow numpy nltk

🛠️ How to Run

  1. Open ChatBot.ipynb in Jupyter Notebook or Google Colab.
  2. Run all cells step-by-step:
    • Load data from intents.json
    • Preprocess text
    • Train the neural network
    • Start chatting with the bot

🔧 Customization

  • Add or edit patterns/responses in intents.json.
  • Retrain the model to reflect changes using the notebook.

🧠 How It Works

  1. Text Preprocessing: User inputs and patterns are tokenized and transformed using Bag of Words.
  2. Model Training: A neural network learns to classify the intent of inputs.
  3. Response Generation: The predicted tag is used to fetch a matching response from intents.json.

📌 Note

This is a basic chatbot designed for educational purposes. It can be enhanced further using advanced NLP techniques like word embeddings or transformers.