README.md

metadata

title: Content Classifier
emoji: 🔍
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
app_port: 7860

🔍 Content Classifier

A powerful FastAPI-based content classification service using ONNX for threat detection and sentiment analysis.

🚀 Features

• Threat Detection: Classify content for potential threats
• Sentiment Analysis: Analyze text sentiment (positive/negative)
• ONNX Runtime: High-performance model inference
• REST API: Easy-to-use HTTP endpoints
• Auto Documentation: Interactive Swagger UI at /docs
• Health Monitoring: Built-in health checks

📡 API Endpoints

Endpoint	Method	Description
/predict	POST	Classify text content
/health	GET	Check API health status
/model-info	GET	Get model information
/docs	GET	Interactive API documentation

🔧 Usage

Example Request

curl -X POST "https://YOUR-SPACE-NAME.hf.space/predict" \
     -H "Content-Type: application/json" \
     -d '{"text": "This is a sample text to classify"}'

Example Response

{
    "is_threat": false,
    "final_confidence": 0.75,
    "threat_prediction": 0.25,
    "sentiment_analysis": {
        "label": "POSITIVE",
        "score": 0.5
    },
    "onnx_prediction": {
        "threat_probability": 0.25,
        "raw_output": [[0.75, 0.25]],
        "output_shape": [1, 2]
    },
    "models_used": ["contextClassifier.onnx"],
    "raw_predictions": {
        "onnx": {
            "threat_probability": 0.25,
            "raw_output": [[0.75, 0.25]]
        },
        "sentiment": {
            "label": "POSITIVE",
            "score": 0.5
        }
    }
}

📊 Response Format

The API returns a structured response with:

• is_threat: Boolean indicating if content is threatening
• final_confidence: Confidence score (0.0 to 1.0)
• threat_prediction: Raw threat probability
• sentiment_analysis: Sentiment classification and score
• onnx_prediction: Raw ONNX model output
• models_used: List of models used for prediction
• raw_predictions: Complete prediction data

🛠️ Local Development

1. Install dependencies:

   pip install -r requirements.txt
   

2. Place your model: Ensure contextClassifier.onnx is in the project root

3. Run the API:

   python app.py
   

4. Visit: http://localhost:7860/docs

🐳 Docker

# Build
docker build -t content-classifier .

# Run
docker run -p 7860:7860 content-classifier

📝 Model Requirements

Your contextClassifier.onnx model should:

• Accept text-based inputs
• Return classification predictions
• Be compatible with ONNX Runtime

⚙️ Configuration

Customize the preprocessing and postprocessing functions in app.py based on your specific model requirements.

🔍 Monitoring

• Health Check: /health - Monitor API status
• Model Info: /model-info - View model details
• Logs: Check application logs for debugging

📜 License

MIT License - feel free to use and modify!