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README.md

@@ -1,12 +1,132 @@
----
-title: Content Classifier
-emoji: 📉
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Content Classifier
+emoji: 🔍
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app_hf.py
+pinned: false
+license: mit
+---
+
+# Content Classifier
+
+This Space provides a content classification service using an ONNX model. It categorizes text as either "safe" or "unsafe" content.
+
+## Features
+
+- **Single Text Classification**: Classify individual pieces of text
+- **Batch Processing**: Process multiple texts at once
+- **API Access**: Use as a web service via HTTP requests
+- **Real-time Interface**: Interactive Gradio web interface
+
+## Usage
+
+### Web Interface
+Simply enter text in the interface and click "Classify" to get predictions.
+
+### API Usage
+
+#### Single Text Classification
+```bash
+curl -X POST https://your-space-name.hf.space/predict \
+  -H "Content-Type: application/json" \
+  -d '{"text": "Your content to classify"}'
+```
+
+#### Batch Processing
+```bash
+curl -X POST https://your-space-name.hf.space/predict \
+  -H "Content-Type: application/json" \
+  -d '{"text": ["Text 1", "Text 2", "Text 3"]}'
+```
+
+### Response Format
+```json
+{
+  "is_threat": false,
+  "final_confidence": 0.85,
+  "threat_prediction": "safe",
+  "onnx_prediction": {
+    "safe": 0.85,
+    "unsafe": 0.15
+  },
+  "models_used": ["onnx"]
+}
+```
+
+## Model Information
+
+The classifier uses an ONNX model (`contextClassifier.onnx`) for efficient inference. The model processes text and outputs probability scores for "safe" and "unsafe" classifications.
+
+## Local Development
+
+1. Clone this repository
+2. Install dependencies: `pip install -r requirements.txt`
+3. Run the application: `python app_hf.py`
+4. Access the interface at `http://localhost:7860`
+
+## Basic Python Usage
+
+```python
+from inference import ContentClassifierInference
+
+# Initialize classifier
+classifier = ContentClassifierInference()
+
+# Classify single text
+result = classifier.predict("Your text here")
+print(f"Threat: {result['is_threat']}, Confidence: {result['final_confidence']}")
+
+# Classify multiple texts
+texts = ["Text 1", "Text 2"]
+results = classifier.predict_batch(texts)
+```
+
+### Response Format
+
+The model returns predictions in the following format:
+
+```json
+{
+  "is_threat": false,
+  "final_confidence": 0.75,
+  "threat_prediction": "safe",
+  "sentiment_analysis": null,
+  "onnx_prediction": {
+    "safe": 0.75,
+    "unsafe": 0.25
+  },
+  "models_used": ["onnx"],
+  "raw_predictions": {
+    "onnx": {
+      "safe": 0.75,
+      "unsafe": 0.25
+    },
+    "sentiment": null
+  }
+}
+```
+
+### Configuration
+
+Modify `config.json` to adjust:
+- `labels`: Class labels for your model
+- `max_length`: Maximum input sequence length
+- `threshold`: Classification confidence threshold
+
+## Testing
+
+Run the test script:
+```bash
+python test_inference.py
+```
+
+## Model Requirements
+
+- Input: Text string
+- Output: Classification probabilities
+- Format: ONNX model file
+
+Note: You may need to adjust the `preprocess` method in `inference.py` based on your specific model's input requirements (tokenization, encoding, etc.).