AI-Enhanced Web Chat Application

A modern web-based chat application enhanced with advanced artificial intelligence features to provide a smarter, more engaging chat experience. This application uses MongoDB for data storage, providing improved performance, scalability, and reliability, all wrapped in an intuitive web interface.

Features

Modern Web Interface

• Responsive design that works on desktop and mobile devices
• Real-time message updates without page refreshes
• Interactive UI with modals, tooltips, and dynamic content
• Visualizations for sentiment analysis and user interests
• Clean, intuitive navigation and user experience

Core Chat Functionality

• Create and join password-protected chat rooms with descriptions and tags
• Send and receive messages in real-time with timestamp tracking
• View chat history with enhanced formatting
• User authentication via room passwords
• Room categorization and metadata management
• Search and filtering capabilities

AI Enhancements

1. Advanced Sentiment Analysis

• Real-time analysis of message sentiment with compound scoring
• Visual sentiment indicators for messages
• Interactive sentiment reports with charts and graphs
• Emotional tone detection and tracking
• Identification of most positive and negative messages
• User sentiment statistics and visualization

2. Multi-Algorithm Recommendation System

• Personalized room recommendations using multiple algorithms:
  • Content-based filtering using TF-IDF vectorization
  • Collaborative filtering based on user behavior
  • Topic modeling using Latent Dirichlet Allocation (LDA)
  • Hybrid recommendations combining multiple approaches
• Explanation of why rooms are recommended
• Similar room discovery
• Trending topics identification across all chat rooms
• Recommendation history tracking and analysis

3. AI Chatbot Assistant

• Intelligent chatbot that can participate in conversations
• Command-based interaction system
• Conversation summarization
• Knowledge base for common questions
• Learning capabilities from user interactions

4. Predictive Text

• Smart text completion as you type
• Personalized suggestions based on your writing style
• Context-aware word predictions
• Learning system that improves over time

5. User Interest Profiling

• Automatic interest extraction from messages
• Interactive interest visualization with charts
• Personalized user profiles
• Activity tracking and statistics

Technical Implementation

Web Architecture

• Flask web framework for backend
• Bootstrap 5 for responsive frontend design
• JavaScript for interactive features
• RESTful API for client-server communication
• WebSockets for real-time updates

MongoDB Database Architecture

• Document-based NoSQL database for flexible schema
• Collections for users, rooms, messages, and AI data
• Efficient indexing for improved query performance
• Structured data model with relationships
• Automatic backup and restore capabilities
• Advanced search and filtering functionality
• Seamless migration from previous file-based storage

Advanced AI/ML Technologies

• Natural Language Processing (NLP) for sentiment analysis and interest extraction
• Multiple recommendation algorithms (content-based, collaborative, topic modeling)
• TF-IDF and Count vectorization for text processing
• Latent Dirichlet Allocation (LDA) for topic modeling
• N-gram models for predictive text
• Rule-based and retrieval-based chatbot system
• Interactive data visualization with Chart.js

Getting Started

Prerequisites

• Python 3.6 or higher
• MongoDB (local installation or MongoDB Atlas)
• Required packages:
  • Flask
  • pymongo
  • dnspython
  • python-dotenv
  • nltk
  • scikit-learn
  • matplotlib
  • numpy
  • pandas

Installation

1. Clone the repository
2. Install required packages:

   pip install -r requirements.txt
   

3. Configure MongoDB:
   • Create a .env file in the project root with the following content:

     MONGO_URI=mongodb://localhost:27017/
     DB_NAME=ai_chat_app
     SECRET_KEY=your_secret_key_here
     

   • For MongoDB Atlas, use the connection string provided by Atlas:

     MONGO_URI=mongodb+srv://<username>:<password>@<cluster>.mongodb.net/
     

4. Run the web application:

   python app.py
   

   • On first run, the application will automatically migrate data from the old file-based storage to MongoDB
5. Open your browser and navigate to:

   http://localhost:5000
   

Usage

Web Interface

1. Login: Enter a username to identify yourself in the chat
2. Browse Rooms: View available chat rooms with descriptions and tags
3. Create Room: Create a new room with password, description, and tags
4. Join Room: Enter an existing room using its password
5. Chat: Send and receive messages in real-time
6. AI Features: Access sentiment analysis, recommendations, and more through the web interface

AI Features in Web Interface

• Sentiment Analysis: View sentiment reports with interactive charts
• Recommendations: Get personalized room suggestions
• Predictive Text: See word suggestions as you type
• AI Assistant: Chat with the AI bot using @AIBot mentions
• User Stats: View your activity and interest profile

Project Structure

Backend Files

• app.py - Flask web application with routes and API endpoints
• mongodb_connector.py - MongoDB database connector with data models
• sentiment_analyzer.py - Sentiment analysis module with visualization
• recommendation_system.py - Multi-algorithm recommendation system
• chatbot_assistant.py - AI chatbot implementation with learning capabilities
• predictive_text.py - Predictive text system with personalization
• requirements.txt - Package dependencies
• .env - Environment variables for MongoDB connection

Frontend Files

• templates/ - HTML templates for web pages
  • base.html - Base template with common elements
  • index.html - Home page
  • login.html - User login page
  • rooms.html - Room listing page
  • create_room.html - Room creation form
  • join_room.html - Room joining form
  • chat_room.html - Main chat interface
• static/ - Static assets
  • css/style.css - Custom styles
  • js/main.js - JavaScript for interactive features
  • images/ - Images and illustrations

Data Storage

The application uses MongoDB for data storage with the following collections:

• users - Stores user profiles, interests, and activity
• rooms - Stores room information, metadata, and settings
• messages - Stores all chat messages with timestamps and sentiment
• ai_data - Stores AI-related data like chatbot knowledge base

The application will automatically migrate data from the old file-based storage to MongoDB on first run. Backups are stored in the backups/ directory.

API Endpoints

The application provides a RESTful API for client-server communication:

• GET /api/messages/<room_name> - Get messages from a room
• POST /api/messages/<room_name> - Send a message to a room
• GET /api/sentiment/<room_name> - Get sentiment analysis for a room
• GET /api/recommendations/<username> - Get room recommendations
• GET /api/trending - Get trending topics
• GET /api/user/<username> - Get user statistics
• GET /api/predict - Get word predictions

Advanced Features

User Authentication

• Secure user registration and login with email and password
• Password hashing with bcrypt
• Session management with MongoDB and Redis
• User profile management
• Password reset functionality

MongoDB Integration

• Document-based NoSQL database for flexible schema
• Collections for users, rooms, messages, and AI data
• Advanced queries with MongoDB's aggregation framework
• Efficient indexing for improved query performance
• Automatic data migration from file-based storage

Redis Caching

• High-performance in-memory caching
• Cached room messages for faster loading
• User data caching to reduce database load
• Analytics caching for expensive computations
• Session storage for improved authentication performance
• Rate limiting implementation

WebSockets

• Real-time message delivery without page refreshes
• User presence indicators (online/offline status)
• Typing indicators
• Join/leave notifications
• Room-based communication channels

Cloud Deployment

• Docker containerization for consistent environments
• Docker Compose for local development
• Deployment guides for Heroku, AWS, and Azure
• Environment configuration for different platforms
• Scaling considerations for high traffic

Future Enhancements

• Integration with external AI APIs (OpenAI, Hugging Face)
• Advanced NLP with transformer models
• Image generation from text descriptions
• Voice message transcription
• Mobile application with React Native
• User authentication with OAuth
• End-to-end encryption for messages

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Flask for the web framework
• MongoDB for the database system
• PyMongo for MongoDB integration
• Bootstrap for responsive design
• Chart.js for interactive visualizations
• NLTK for natural language processing
• Scikit-learn for machine learning components
• Matplotlib and Pandas for data visualization
• NumPy for numerical operations