Implementation-Of-Chatbot-Using-NLP

Implementation Of Chatbot Using NLP

Technology Stack

Uses Python
Leverages Streamlit for web interface
Implements Natural Language Processing (NLP)
Utilizes scikit-learn for machine learning

Core Components

TF-IDF Vectorization for text processing
Logistic Regression for intent classification
JSON-based intent management
Random response selection

Features

Chat interface with message input
Conversation history tracking
Date-based conversation filtering
Sidebar navigation
Custom styling

Key Functions

chatbot(): Processes user input, classifies intent, returns response
main(): Manages Streamlit application flow
Conversation logging to CSV

Workflow

Load predefined intents from JSON
Vectorize training patterns
Train machine learning model
Convert user input to vector
Predict intent
Select random response from matched intent

User Interface

Chat mode for real-time interaction
Conversation history view
About page with project details
Responsive design
Custom CSS styling

Limitations

Simple intent matching
Limited conversation complexity
No advanced context preservation

Potential Improvements

Advanced NLP techniques
Deep learning models
Enhanced intent coverage
Context management