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| title: 'Multilingual Sentiment Analyzer' | |
| emoji: 📉 | |
| colorFrom: gray | |
| colorTo: pink | |
| sdk: docker | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| 🌍 Multilingual Sentiment Analyzer | |
| A comprehensive AI-powered sentiment analysis application with explainable AI features (SHAP & LIME) supporting multiple languages. | |
| 🚀 Features | |
| Multi-language Support: Auto-detect or manually select from English, Chinese, Spanish, French, German, Swedish | |
| Advanced Analysis: SHAP and LIME explainable AI integration | |
| Batch Processing: Analyze multiple texts simultaneously with parallel processing | |
| Interactive Visualizations: Real-time charts and dashboards with Plotly | |
| History Management: Track and export analysis history | |
| Multiple Themes: Customizable color themes for visualizations | |
| File Upload: Support for CSV and TXT file uploads | |
| Export Functionality: Export results in CSV and JSON formats | |
| 📁 Project Structure | |
| sentiment_analyzer/ | |
| ├── config.py # Configuration settings | |
| ├── models.py # Model management & sentiment engine | |
| ├── analysis.py # SHAP/LIME explainable AI | |
| ├── visualization.py # Plotly visualizations | |
| ├── data_utils.py # Data processing & history management | |
| ├── app.py # Application logic | |
| ├── main.py # Gradio interface & startup | |
| ├── requirements.txt # Python dependencies | |
| ├── Dockerfile # Docker configuration | |
| └── README.md # This file | |
| 🛠 Installation | |
| Option 1: Local Installation | |
| bash# Clone the repository | |
| git clone <repository-url> | |
| cd sentiment_analyzer | |
| # Install dependencies | |
| pip install -r requirements.txt | |
| # Download NLTK data | |
| python -c "import nltk; nltk.download('stopwords'); nltk.download('punkt')" | |
| # Run the application | |
| python main.py | |
| Option 2: Docker | |
| bash# Build the Docker image | |
| docker build -t sentiment-analyzer . | |
| # Run the container | |
| docker run -p 7860:7860 sentiment-analyzer | |
| Option 3: Hugging Face Spaces | |
| Create a new Space on Hugging Face | |
| Select "Docker" as the SDK | |
| Upload all project files | |
| The app will automatically deploy | |
| 🎯 Usage | |
| Single Text Analysis | |
| Navigate to the "Single Analysis" tab | |
| Enter your text in any supported language | |
| Select language (or use Auto Detect) | |
| Choose visualization theme | |
| Configure preprocessing options | |
| Click "Analyze" | |
| Advanced Analysis (SHAP/LIME) | |
| Go to "Advanced Analysis" tab | |
| Enter text for explainable AI analysis | |
| Adjust number of samples (50-300) | |
| Click "SHAP Analysis" or "LIME Analysis" | |
| View feature importance visualizations | |
| Batch Processing | |
| Switch to "Batch Analysis" tab | |
| Upload a file or enter multiple texts (one per line) | |
| Configure analysis settings | |
| Click "Analyze Batch" | |
| View summary statistics and detailed results | |
| History & Analytics | |
| Open "History & Analytics" tab | |
| View comprehensive analysis dashboard | |
| Export data in CSV or JSON format | |
| Clear history when needed | |
| 🔧 Configuration | |
| Edit config.py to customize: | |
| Model Settings: Change supported models and languages | |
| Processing Limits: Adjust batch sizes and memory limits | |
| UI Themes: Modify color schemes | |
| Cache Settings: Configure caching parameters | |
| 📊 Supported Models | |
| English: CardiffNLP Twitter RoBERTa | |
| Multilingual: CardiffNLP XLM-RoBERTa | |
| Chinese: UER RoBERTa Chinese | |
| 🎨 Themes | |
| Default: Green/Red/Orange | |
| Ocean: Blue/Orange/Cyan | |
| Dark: Darker variants | |
| Rainbow: Purple/Pink/Red | |
| ⚡ Performance Features | |
| Model Caching: LRU cache for efficient model management | |
| Parallel Processing: Multi-threaded batch analysis | |
| Memory Optimization: Automatic cleanup and GPU management | |
| Lazy Loading: Models loaded on-demand | |
| 🐛 Troubleshooting | |
| Common Issues | |
| SHAP/LIME Errors: Reduce sample size or text length | |
| Memory Issues: Lower batch size or enable text cleaning | |
| Model Loading: Check internet connection for model downloads | |
| Port Conflicts: Change port in main.py if 7860 is occupied | |
| Performance Tips | |
| Use GPU if available for faster processing | |
| Enable text cleaning for better preprocessing | |
| Reduce sample sizes for faster explainable AI analysis | |
| Clear history periodically to save memory | |
| 📄 License | |
| This project is open source and available under the MIT License. | |
| 🤝 Contributing | |
| Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests. | |
| 📞 Support | |
| For support and questions, please open an issue in the repository. | |
| Built with ❤️ using Gradio, Transformers, SHAP, LIME, and Plotly | |