This project implements an Intent-Based Chatbot using Natural Language Processing (NLP) techniques. The chatbot is designed to understand and respond to user inputs in a natural way by detecting intents and providing relevant responses. The interface is built using Streamlit, allowing real-time interaction with the chatbot. It uses a Naive Bayes classifier for intent detection and TF-IDF vectorization for converting text into numerical features.
- Understand the basics of Intent-Based Chatbots and their applications.
- Learn how to process user inputs using NLP techniques such as tokenization, intent detection, and entity recognition.
- Build a chatbot using Python and deploy it with Streamlit.
- Manage chat sessions dynamically using Streamlit's session state.
- Enhance user experience with a clean and interactive interface.
- Apply machine learning techniques such as Naive Bayes for intent classification and TF-IDF vectorization for text representation.
- Python: The core programming language used to build the chatbot and NLP logic.
- Natural Language Processing (NLP): Used for processing user inputs and detecting intents.
- Streamlit: A framework for building interactive web applications, used to deploy the chatbot interface.
- Naive Bayes: A machine learning algorithm used for intent classification.
- TF-IDF Vectorizer: A technique used for converting text into numerical features.
- Session State (Streamlit): To maintain and manage chat history dynamically.
- Cloud Platforms (Future Deployment): Deployment on platforms like AWS or Heroku for public access.
- Real-Time Interaction: Chatbot can handle dynamic, real-time conversations.
- Intent Detection: Detects predefined user intents and responds accordingly.
- Session Management: Stores chat history to provide a continuous interaction flow.
- User-Friendly Interface: Simple, clean, and responsive chatbot interface built with Streamlit.
- Machine Learning-Powered: Utilizes Naive Bayes for intent classification and TF-IDF for text vectorization.
- Future Enhancements: Plans to integrate advanced NLP models (like BERT) and deploy the chatbot to cloud platforms.
To run the chatbot locally, follow these steps but before this *Make Sure you had installed Git in your sytem to run git commands:
- Clone the repository:
git clone https://github.com/Ayan16105/Intent_base_Chatbot.git
- Change dirctory to your cloned Directory:
cd Intent_base_Chatbot - Install required dependencies:
pip install -r requirements.txt
- Run the Streamlit app:
streamlit run app.py
Feel free to fork the repository and submit pull requests. Contributions are welcome!
This project is licensed under the MIT License - see the file for details.
- Streamlit: For providing an easy-to-use interface for deploying the chatbot.
- NLP libraries: For enabling text processing and intent recognition.
- Open Source Community: For sharing resources and tools that made this project possible.