from transformers import AutoModel
from PIL import Image

# Load model from Hub (FIXED!)
model = AutoModel.from_pretrained("BabaK07/pixeltext-ai", trust_remote_code=True)

# Load image
image = Image.open("your_image.jpg")

# Extract text
result = model.generate_ocr_text(image)

print(f"Text: {result['text']}")
print(f"Confidence: {result['confidence']:.1%}")
print(f"Success: {result['success']}")

📊 Performance

⚡ Speed: ~3 seconds per image
🎯 Accuracy: Up to 95% confidence
🌍 Languages: 100+ supported
💻 Device: CPU and GPU support
🔄 Batch: Multiple image processing

🛠️ Features

✅ Hub Loading: Works with AutoModel.from_pretrained()
✅ Fast Inference: Optimized for speed
✅ High Accuracy: Based on PaliGemma-3B
✅ Multi-language: Supports 100+ languages
✅ Batch Processing: Handle multiple images
✅ Custom Prompts: Tailor extraction for specific needs
✅ Production Ready: Error handling included

📝 Usage Examples

Basic Usage

python

from transformers import AutoModel
from PIL import Image

model = AutoModel.from_pretrained("BabaK07/pixeltext-ai", trust_remote_code=True)
image = Image.open("document.jpg")
result = model.generate_ocr_text(image)

Custom Prompts

python

result = model.generate_ocr_text(
    image, 
    prompt="<image>Extract all invoice details including amounts:"
)

Batch Processing

python

images = [Image.open(f"doc_{i}.jpg") for i in range(5)]
results = model.batch_ocr(images)

File Path Input

result = model.generate_ocr_text("path/to/your/image.jpg")

🔧 Installation

pip install torch transformers pillow

📈 Model Details

Base Model: google/paligemma-3b-pt-224
Model Size: ~3B parameters
Architecture: Vision-Language Transformer
Optimization: OCR-specific enhancements
Training: Custom OCR pipeline

🆚 Comparison

Feature	Before (Broken)	After (FIXED)
Hub Loading	❌ AttributeError	✅ Works perfectly
from_pretrained	❌ Missing	✅ Implemented
AutoModel	❌ Failed	✅ Compatible
Configuration	❌ Invalid	✅ Proper config

🎯 Use Cases

Document Digitization: Convert scanned documents
Invoice Processing: Extract invoice data
Form Processing: Digitize forms
Receipt OCR: Extract receipt information
Multi-language Documents: Handle international text
Batch Processing: Process document collections

🔗 Related Models

textract-ai: https://huggingface.co/BabaK07/textract-ai (Qwen-based, higher accuracy)
Base Model: https://huggingface.co/google/paligemma-3b-pt-224

📞 Support

For issues or questions, please check the model repository or contact the author.

Status: ✅ FIXED and ready for production use!