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---
title: Content Classifier
emoji: 🔍
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
app_port: 7860
---
# Content Classifier API
A FastAPI-based content classification service using an ONNX model for threat detection and sentiment analysis.
## Features
- Content threat classification
- Sentiment analysis
- RESTful API with automatic documentation
- Health check endpoints
- Model information endpoints
- Docker support for easy deployment
## API Endpoints
- `POST /predict` - Classify text content
- `GET /` - API status
- `GET /health` - Health check
- `GET /model-info` - Model information
- `GET /docs` - Interactive API documentation (Swagger)
## Installation
1. Install dependencies:
```bash
pip install -r requirements.txt
```
2. Run the application:
```bash
python app.py
```
The API will be available at `http://localhost:8000`
## Usage
### Example Request
```bash
curl -X POST "http://localhost:8000/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]]
},
"models_used": ["contextClassifier.onnx"],
"raw_predictions": {
"onnx": {
"threat_probability": 0.25,
"raw_output": [[0.75, 0.25]]
},
"sentiment": {
"label": "POSITIVE",
"score": 0.5
}
}
}
```
## Docker Deployment
1. Build the Docker image:
```bash
docker build -t content-classifier .
```
2. Run the container:
```bash
docker run -p 8000:8000 content-classifier
```
## Hugging Face Spaces Deployment
To deploy on Hugging Face Spaces:
1. Create a new Space on Hugging Face
2. Upload all files to your Space repository
3. The Space will automatically build and deploy
## Model Requirements
The ONNX model should accept text inputs and return classification predictions. You may need to adjust the preprocessing and postprocessing functions in `app.py` based on your specific model requirements.
## Configuration
You can modify the following in `app.py`:
- `MODEL_PATH`: Path to your ONNX model file
- `max_length`: Maximum text length for processing
- Preprocessing and postprocessing logic based on your model's requirements
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