# Desklib Pre-trained Model Integration

## ✅ What Was Added

Instead of training your own model (which hits PyTorch MPS bugs on M2 Mac), the project now uses **Desklib's pre-trained AI text detector** - a state-of-the-art model that leads the RAID Benchmark.

## 🎯 Model Details

- **Model**: `desklib/ai-text-detector-v1.01`
- **Base**: microsoft/deberta-v3-large
- **Architecture**: DeBERTa with mean pooling + classifier head
- **Performance**: Top performer on RAID benchmark
- **No Training Needed**: Pre-trained and ready to use!

## 📝 Changes Made

### 1. `ai_text_detector/models.py`
- ✅ Added `DesklibAIDetectionModel` class (custom architecture)
- ✅ Updated `DetectorModel` to support Desklib model
- ✅ Added `predict()` method for easy inference
- ✅ Automatic CPU placement for macOS compatibility

### 2. `gradio_app.py`
- ✅ Now uses Desklib model by default (instead of RoBERTa-base)
- ✅ Updated detection logic to use new `predict()` method
- ✅ Better error handling

## 🚀 Usage

### In Gradio App
```bash
python gradio_app.py
```
The app will automatically use the Desklib model!

### In Your Code
```python
from ai_text_detector.models import DetectorModel

# Load Desklib model
model = DetectorModel("desklib/ai-text-detector-v1.01", use_desklib=True)

# Predict
ai_prob, label = model.predict("Your text here")
print(f"AI Probability: {ai_prob:.2%}")
print(f"Label: {'AI-generated' if label == 1 else 'Human-written'}")
```

### Test It
```bash
python test_desklib.py
```

## 🎉 Benefits

- ✅ **No Training Needed** - Pre-trained model ready to use
- ✅ **Better Accuracy** - State-of-the-art performance
- ✅ **Works on M2 Mac** - Avoids PyTorch MPS training bugs
- ✅ **Easy to Use** - Same interface as before
- ✅ **Production Ready** - Already fine-tuned and optimized

## 📊 Model Performance

- **RAID Benchmark**: Top performer
- **Robust**: Handles adversarial attacks well
- **Domain Generalization**: Works across different text types
- **Fast Inference**: Optimized for production use

## 🔄 Fallback

If Desklib model fails to load, the code falls back to:
- Your trained model (if exists in `models/ai_detector`)
- RoBERTa-base (standard classification model)

## 📚 References

- **Model Card**: https://huggingface.co/desklib/ai-text-detector-v1.01
- **GitHub**: https://github.com/desklib/ai-text-detector
- **Try Online**: https://desklib.com/ai-detector

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**You now have a production-ready AI text detector without needing to train!** 🎉