Add comprehensive inference example script

inference_example.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+"""
+CardVault+ Inference Example
+Simple example showing how to use the CardVault+ model for card extraction
+"""
+
+import torch
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from PIL import Image, ImageDraw
+import json
+
+def create_sample_card():
+    """Create a sample credit card image for testing"""
+    # Create card-like image
+    img = Image.new('RGB', (400, 250), color='lightblue')
+    draw = ImageDraw.Draw(img)
+    
+    # Add card elements
+    draw.text((20, 50), "SAMPLE BANK", fill='black')
+    draw.text((20, 100), "1234 5678 9012 3456", fill='black')
+    draw.text((20, 150), "JOHN DOE", fill='black') 
+    draw.text((300, 150), "12/25", fill='black')
+    
+    return img
+
+def extract_card_info(image_path_or_pil=None):
+    """Extract structured information from a card image"""
+    
+    # Load the model
+    print("Loading CardVault+ model...")
+    model_id = "sugiv/cardvaultplus" 
+    processor = AutoProcessor.from_pretrained(model_id)
+    model = AutoModelForVision2Seq.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    
+    # Load image
+    if image_path_or_pil is None:
+        print("Creating sample card image...")
+        image = create_sample_card()
+    elif isinstance(image_path_or_pil, str):
+        image = Image.open(image_path_or_pil)
+    else:
+        image = image_path_or_pil
+    
+    # Prepare extraction prompt
+    prompt = "<image>Extract structured information from this card/document in JSON format."
+    
+    # Process the image and prompt
+    inputs = processor(text=prompt, images=image, return_tensors="pt")
+    
+    # Move to GPU if available
+    device = next(model.parameters()).device
+    inputs = {k: v.to(device) if hasattr(v, 'to') else v for k, v in inputs.items()}
+    
+    # Generate extraction
+    print("Extracting information...")
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=150,
+            do_sample=False,
+            pad_token_id=processor.tokenizer.eos_token_id
+        )
+    
+    # Decode response
+    response = processor.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract JSON if present
+    extracted_json = None
+    if '{' in response and '}' in response:
+        try:
+            json_start = response.find('{')
+            json_end = response.rfind('}') + 1
+            json_str = response[json_start:json_end]
+            extracted_json = json.loads(json_str)
+        except:
+            pass
+    
+    return {
+        'full_response': response,
+        'extracted_json': extracted_json,
+        'success': extracted_json is not None
+    }
+
+if __name__ == "__main__":
+    # Example usage
+    result = extract_card_info()  # Uses sample card
+    
+    print("="*50)
+    print("CardVault+ Extraction Results")
+    print("="*50)
+    print(f"Success: {result['success']}")
+    print(f"Full Response: {result['full_response']}")
+    
+    if result['extracted_json']:
+        print("Extracted JSON:")
+        print(json.dumps(result['extracted_json'], indent=2))
+    
+    # Example with your own image:
+    # result = extract_card_info("path/to/your/card.jpg")