| ---
|
| 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
|
|
|
