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--- |
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license: apache-2.0 |
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title: Facebook Ad Analytics |
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sdk: docker |
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emoji: 📊 |
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colorFrom: green |
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colorTo: blue |
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pinned: true |
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short_description: facebook-ad-analytics |
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--- |
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# Facebook Ad Analytics |
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A comprehensive tool for scraping, analyzing, and visualizing Facebook ads using AI and machine learning. |
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## Features |
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- **Ad Scraping**: Scrape Facebook ads using Selenium. |
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- **Sentiment Analysis**: Analyze ad sentiment using AI (Hugging Face Transformers). |
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- **Image Analysis**: Extract text from images (OCR) and detect objects using YOLOv4. |
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- **Compliance Reporting**: Generate compliance reports and anonymize ads. |
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- **Predictive Analytics**: Forecast ad performance using machine learning. |
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- **Google Ads Analysis**: Scrape and analyze Google Search and Display ads. |
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## Requirements |
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- Python 3.9+ |
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- PostgreSQL (recommended for production) |
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- Redis (for Celery task queue) |
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- Tesseract OCR |
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## Setup |
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### Quick Setup |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/yourusername/facebook-ad-analytics.git |
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cd facebook-ad-analytics |
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``` |
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2. Run the setup script: |
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```bash |
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python setup.py |
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``` |
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3. Update the `.env` file with your settings. |
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4. Run the application: |
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```bash |
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python manage.py run |
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``` |
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### Manual Setup |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/yourusername/facebook-ad-analytics.git |
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cd facebook-ad-analytics |
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``` |
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2. Create a virtual environment and install dependencies: |
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```bash |
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python -m venv venv |
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source venv/bin/activate # On Windows: venv\Scripts\activate |
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pip install -r requirements.txt |
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``` |
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3. Copy the example environment file and update it: |
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```bash |
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cp .env.example .env |
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# Edit .env with your settings |
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``` |
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4. Initialize the database: |
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```bash |
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python manage.py init-db |
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``` |
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5. Download YOLOv4 models: |
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```bash |
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mkdir -p app/models |
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wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights -O app/models/yolov4.weights |
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wget https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg -O app/models/yolov4.cfg |
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``` |
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6. Run the application: |
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```bash |
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python manage.py run |
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``` |
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## Docker Deployment |
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For production deployment, use Docker Compose: |
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```bash |
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# Set environment variables in .env file first |
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docker-compose up -d |
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``` |
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If you don't have a .env file, default development values will be used, but you should create one for production. |
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## Troubleshooting |
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### Missing Dependencies |
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If you encounter errors about missing dependencies, run the dependency checker: |
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```bash |
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python check_dependencies.py |
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``` |
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This will identify any missing packages. You can install them with: |
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```bash |
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pip install -r requirements.txt |
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``` |
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### Common Issues |
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#### ModuleNotFoundError: No module named 'ratelimit' |
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This error occurs when the ratelimit package is missing. Install it with: |
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```bash |
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pip install ratelimit |
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``` |
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#### AI Model Warnings |
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Warnings about missing PyTorch, TensorFlow, or Flax are normal if you don't need the full AI capabilities. For full functionality, install: |
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```bash |
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pip install torch==2.0.1 |
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``` |
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#### Docker Startup Issues |
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If you encounter issues with Docker startup: |
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1. Check Docker logs for detailed error messages: |
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```bash |
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docker-compose logs web |
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``` |
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2. Try rebuilding the Docker image: |
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```bash |
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docker-compose build --no-cache web |
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docker-compose up -d |
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``` |
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3. If you see syntax errors with parentheses in the command, the issue has been fixed in the latest version by removing the parentheses from the gunicorn command. |
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4. Ensure all required packages are in requirements.txt: |
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```bash |
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# Check if ratelimit is in requirements.txt |
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grep ratelimit requirements.txt |
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# If not, add it |
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echo "ratelimit==2.2.1" >> requirements.txt |
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``` |
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## Testing |
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Run the test suite: |
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```bash |
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python manage.py test |
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``` |
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## API Documentation |
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The API endpoints are available at `/api/v1/` and include: |
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- `/api/v1/ads` - List and create ads |
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- `/api/v1/ads/<id>` - Get, update, or delete an ad |
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- `/api/v1/analyze` - Analyze ad content |
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## License |
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This project is licensed under the Apache 2.0 License - see the LICENSE file for details. |
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