This Python script automates the process of classifying images. It reads image URLs from a CSV file, classifies them using the Clarifai API, and writes the results to another CSV file. This project showcases skills in API integration, error handling, and file operations in Python.

• Automated Image Tagger: Streamlines the process of tagging the images based on their content.
• API Integration: Utilizes the Clarifai API for advanced image recognition.
• Error Handling: Implements robust error handling and retry strategies for API requests.
• CSV Operations: Reads from and writes to CSV files, enabling easy data manipulation and storage.
• Parallel Processing: Uses concurrent processing to speed up image classification.
• URL Validation: Ensures that URLs are valid and accessible before processing.
• Logging: Provides detailed logging for monitoring and debugging

• Python 3.x
• clarifai_grpc for API access
• python-dotenv for environment variable management
• backoff for retry logic
• protobuf essential for the underlying functionality of the clarifai_grpc library
• requests used during url validation

1. Clone this repository.
2. Install dependencies: pip install -r requirements.txt
3. Create a .env file with your Clarifai API key: API_KEY=<Your_API_Key>

1. Prepare a CSV with image URLs in the first column.
2. Execute the script: python pypic.py
3. View results in classified_images.csv, including tags and confidence scores.

• pypic.py: The main script that orchestrates the classification process.
• .env: An .env file.
• requirements.txt: Lists all the project dependencies.

We welcome contributions! If you're interested in improving this project, check the issues page for open tasks. To contribute:

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.