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---
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
```bash
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
```json
{
"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:**
```bash
pip install -r requirements.txt
```
2. **Place your model:**
Ensure `contextClassifier.onnx` is in the project root
3. **Run the API:**
```bash
python app.py
```
4. **Visit:** `http://localhost:7860/docs`
## 🐳 Docker
```bash
# 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!
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