🤖 AI Chatbot Assistant

An intelligent chatbot based on DistilBERT with advanced features for weather forecasts, news, stocks, cryptocurrencies, and TODO list management. The model has been trained using fine-tuning techniques and data augmentation to provide accurate and contextual responses in Italian language.

🚀 Live Demo

The application is deployed on Hugging Face Spaces:
https://huggingface.co/spaces/sPappalard/AI-Chatbot

✨ Key Features

🇮🇹 Italian Language

• Model completely trained on Italian dataset
• Message handling logic optimized for Italian language
• Native support for Italian expressions and idioms

🌤️ Weather Services

• Real-time weather forecasts for 100+ cities worldwide
• Support for Italian, European, and international cities
• Data provided by OpenWeatherMap API
• Fallback with simulated data in case of API errors/limits exceeded

📊 Financial Quotes

• Real-time stock prices (50+ supported symbols)
• Cryptocurrencies (Bitcoin, Ethereum, Cardano, etc.)
• Data from Alpha Vantage API and CoinGecko API
• Categorization by sectors (Tech, Finance, Healthcare, etc.)
• Fallback with simulated data in case of API errors/limits exceeded

📰 News Updates

• Latest news from the United States
• Real-time updates via NewsAPI
• Display of headlines and sources
• Fallback with simulated data in case of API errors/limits exceeded

📝 TODO List Management

• Complete task management system
• Priority system (High, Medium, Low)
• Commands: Aggiungi [task], mostra lista, completa [id]
• Session-based data persistence

🎯 Additional Features

• Current time and date
• Interactive jokes and entertainment
• Contextual help system

🏗️ Technical Architecture

AI Model

• Base Model: DistilBERT (distilbert-base-uncased)
• Architecture: 3-layer classifier with BatchNorm and Dropout
• Training: Partial fine-tuning of last 2 BERT layers
• Techniques: Advanced data augmentation, class balancing, early stopping
• Intent Classes: The model is trained on 12 main intents:
  • greeting, goodbye, thanks, help
  • weather, news, stocks, time, joke
  • todo, name, mood

Backend

• Framework: Flask with CORS support
• Database: SQLite for conversation memory and TODO lists
• Rate Limiting: Smart API usage management
• Session Management: UUID-based user sessions

Frontend

• Design: Modern responsive interface
• Features: Dark/Light mode toggle, typing indicators, auto-scroll
• Accessibility: Mobile-friendly design

📱 Screenshots

Main Interface (Light mode and Dark mode)

Weather Service

Stock/Crypto Prices

TODO List Management

API Statistics Panel

Help menu

🔧 Installation & Setup

Prerequisites

Python 3.8+
pip

1. Clone the Repository

git clone https://github.com/sPappalard/AI-Chatbot.git
cd AI-Chatbot

2. Install Dependencies

pip install -r requirements.txt

3. Download NLTK Data

python -c "import nltk; nltk.download('punkt'); nltk.download('wordnet')"

4. Set Up Environment Variables

Create a .env file in the root directory:

SECRET_KEY=your_secret_key_here
OPENWEATHER_API_KEY=your_openweather_api_key
NEWS_API_KEY=your_news_api_key
ALPHA_VANTAGE_API_KEY=your_alpha_vantage_api_key
DEPLOYMENT_ENV=local
DB_PATH=/app/data/conversations.db

5. Train the Model (Optional)

The trained model is already included in the repository (chatbot_distilbert_robust.pth). If you want to retrain it:

python training.py

6. Run the Application

python app.py

The application will be available at http://localhost:7860

📁 Project Structure

├── app.py                          # Main Flask application
├── training.py                     # Model training script
├── intents.json                    # Training dataset (intents and patterns)
├── templates/
│   └── index.html                  # Frontend interface
├── data/                          # Data directory (created automatically)
│   ├── conversations.db           # SQLite database
│   └── rate_limits.db            # API rate limiting data
├── chatbot_distilbert_robust.pth  # Trained model (generated after training)
├── Dockerfile                     # Docker configuration
├── runtime.txt                    # Python runtime version for Hugging Face
├── requirements.txt               # Python dependencies
├── .env                          # Environment variables (create manually)
└── README.md                     # This file

🔗 API Endpoints

Endpoint	Method	Description
/	GET	Main interface
/chat	POST	Send message to chatbot
/clear-chat	POST	Clear conversation history
/new-session	POST	Start new session
/history	GET	Get conversation history
/api-stats	GET	View API usage statistics
/health	GET	Health check and system status

🛡️ Rate Limiting

The application implements intelligent rate limiting to prevent API quota exhaustion:

• Weather API: 1,000 requests/day
• News API: 100 requests/day
• Stock API: 25 requests/day, 5 requests/minute
• Fallback System: Simulated data when limits are exceeded

🎨 Features Showcase

Multi-Intent Recognition

The chatbot can understand various request types:

• "Che tempo fa a Roma?" → Weather service
• "Prezzo dell'azione APPLE?" → Financial data
• "Aggiungi: comprare il pane" → TODO management
• "Fammi ridere" → Entertainment

Smart City Recognition

Supports 100+ cities with intelligent parsing:

• Italian cities: Roma, Milano, Napoli, etc.
• European capitals: London, Paris, Berlin, etc.
• World cities: New York, Tokyo, Sydney, etc.

Financial Data Coverage

Comprehensive stock market coverage:

• Tech Giants: AAPL, MSFT, GOOGL, META, TSLA
• Finance: JPM, BAC, V, MA, PYPL
• Healthcare: JNJ, PFE, MRNA
• And 40+ more symbols

🧠 Model Training Details

Data Augmentation Techniques

• Synonym replacement
• Politeness variations
• Question reformulation
• Punctuation variations
• Article removal/addition

Training Configuration

• Epochs: Up to 100 (with early stopping)
• Batch Size: 32
• Learning Rates: 1e-5 (BERT), 3e-4 (classifier)
• Validation Split: 20%
• Early Stopping: Patience 15, min 30 epochs

Performance Metrics

The model achieves excellent accuracy across all intent categories with balanced precision, recall, and F1-scores. Training completed in 12 epochs with the following results:

Final Training Results:

• Accuracy: 99.52%
• Training Loss: 0.0355
• Validation Loss: 0.0253
• Training Time: 42.5 minutes

Detailed Classification Report:

• greeting: P=1.000 R=0.982 F1=0.991
• goodbye: P=1.000 R=1.000 F1=1.000
• thanks: P=0.995 R=1.000 F1=0.997
• weather: P=1.000 R=0.992 F1=0.996
• stocks: P=1.000 R=0.994 F1=0.997
• news: P=0.987 R=0.993 F1=0.990
• todo: P=1.000 R=0.993 F1=0.996
• help: P=1.000 R=0.986 F1=0.993
• name: P=0.986 R=1.000 F1=0.993
• mood: P=0.977 R=1.000 F1=0.989
• time: P=1.000 R=1.000 F1=1.000
• joke: P=1.000 R=1.000 F1=1.000

🌍 Deployment

Local Development

python app.py

Hugging Face Spaces

The application is optimized for deployment on Hugging Face Spaces with:

• Memory-based storage for shared environments
• Environment detection
• Automatic fallbacks

Docker Support

Docker configuration is already included in the project repository:

# Build the Docker image
docker build -t ai-chatbot .

# Run the container
docker run -p 7860:7860 ai-chatbot

🔧 Configuration Options

Environment Variables

• DEPLOYMENT_ENV: Set to 'huggingface' for Hugging Face Spaces
• DB_PATH: Custom database path
• PORT: Application port (default: 7860)

Model Parameters

• Dropout rates, hidden sizes, and learning rates can be adjusted in training.py
• Intent categories can be modified in intents.json

Contributions

Contributions are welcome! Feel free to:

• Open issues for bugs or suggestions
• Submit pull requests with improvements
• Share your experience using the chatbot!

License

This project is licensed under the MIT License --->