README.md

metadata

title: Reputation Monitor
emoji: 📊
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false
app_port: 7860

📊 End-to-End MLOps Pipeline for Real-Time Reputation Monitoring

🚀 Project Overview

MachineInnovators Inc. focuses on scalable, production-ready machine learning applications. This project is a comprehensive MLOps solution designed to monitor online company reputation through automated sentiment analysis of real-time news.

Unlike standard static notebooks, this repository demonstrates a full-cycle ML workflow. The system scrapes live data from Google News, analyzes sentiment using a RoBERTa Transformer model, and visualizes insights via an interactive dashboard, all orchestrate within a Dockerized environment.

Key Features

• Real-Time Data Ingestion: Automated scraping of Google News for target brand keywords.
• State-of-the-Art NLP: Utilizes twitter-roberta-base-sentiment for high-accuracy classification.
• Full-Stack Architecture: Integrates a FastAPI backend for inference and a Streamlit frontend for visualization in a single container.
• CI/CD Automation: Robust GitHub Actions pipeline for automated testing, building, and deployment to Hugging Face Spaces.
• Embedded Monitoring: Basic logging system to track model predictions and sentiment distribution over time.

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🛠️ Tech Stack & Tools

• Core: Python 3.9+
• Machine Learning: Hugging Face Transformers, PyTorch, Scikit-learn.
• Backend: FastAPI, Uvicorn (REST API).
• Frontend: Streamlit (Interactive Dashboard).
• Data Ingestion: GoogleNews library (Real-time scraping).
• DevOps: Docker, GitHub Actions (CI/CD).
• Deployment: Hugging Face Spaces (Docker SDK).

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⚙️ Architecture & MLOps Workflow

The project follows a rigorous MLOps pipeline to ensure reliability and speed of delivery:

1. Data & Modeling:

   • Input: Real-time news titles and descriptions fetched dynamically.
   • Model: Pre-trained RoBERTa model optimized for social media and short-text sentiment.

2. Containerization (Docker):

   • The application is containerized using a custom Dockerfile.
   • Implements a custom entrypoint.sh script to run both the FastAPI backend (port 8000) and Streamlit frontend (port 7860) simultaneously.

3. CI/CD Pipeline (GitHub Actions):

   • Trigger: Pushes to the main branch.
   • Test: Executes pytest suite to verify API endpoints (/health, /analyze) and model loading.
   • Build: Verifies Docker image creation.
   • Deploy: Automatically pushes the validated code to Hugging Face Spaces.

4. Monitoring:

   • The system logs every prediction to a local CSV file, which is visualized in the "Monitoring" tab of the dashboard.

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📂 Repository Structure

├── .github/workflows/   # CI/CD configurations (GitHub Actions)
├── app/                 # Backend Application Code
│   ├── api/             # FastAPI endpoints (main.py)
│   ├── model/           # Model loader logic (RoBERTa)
│   └── services/        # Google News scraping logic
├── streamlit_app/       # Frontend Application Code (app.py)
├── src/                 # Training simulation scripts
├── tests/               # Unit and integration tests (Pytest)
├── Dockerfile           # Container configuration
├── entrypoint.sh        # Startup script for dual-process execution
├── requirements.txt     # Project dependencies
└── README.md            # Project documentation

💻 Installation & Usage
To run this project locally using Docker (Recommended):

1. Clone the repository
Bash

git clone [https://github.com/YOUR_USERNAME/SentimentAnalysis.git](https://github.com/YOUR_USERNAME/SentimentAnalysis.git)
cd SentimentAnalysis
2. Build the Docker Image
Bash

docker build -t reputation-monitor .
3. Run the Container
Bash

docker run -p 7860:7860 reputation-monitor
Access the application at http://localhost:7860

Manual Installation (No Docker)
If you prefer running it directly with Python:

Install dependencies:

Bash

pip install -r requirements.txt
Start the Backend (FastAPI):

Bash

uvicorn app.api.main:app --host 0.0.0.0 --port 8000 --reload
Start the Frontend (Streamlit) in a new terminal:

Bash

streamlit run streamlit_app/app.py
⚠️ Limitations & Future Roadmap
Data Persistence: Currently, monitoring logs are stored in an ephemeral CSV file. In a production environment, this would be replaced by a persistent database (e.g., PostgreSQL) to ensure data retention across container restarts.

Scalability: The current Google News scraper is synchronous. Future versions will implement asynchronous scraping (aiohttp) or a message queue (RabbitMQ/Celery) for high-volume processing.

Model Retraining: A placeholder pipeline (src/train.py) is included. Full implementation would require GPU resources and a labeled dataset for fine-tuning.

🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.

📝 License
Distributed under the MIT License. See LICENSE for more information.

### 👤 Author

**[Fabio Celaschi]**
* [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/fabio-celaschi-4371bb92)
* [![Instagram](https://img.shields.io/badge/Instagram-E4405F?style=for-the-badge&logo=instagram&logoColor=white)](https://www.instagram.com/fabiocelaschi/)