README.md

---
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