Update README.md

README.md

@@ -13,39 +13,34 @@ license: mit
 This model is designed to detect whether an image is real or AI-generated. It uses Vision Transformer (VIT) architecture to provide accurate classification.
 
 ## Model Usage
-
 ```python
-## Classes
-The model classifies images into two categories:
-- Real Image (0)
-- AI Generated (1)
-
-## Technical Details
-- Model Architecture: Vision Transformer (ViT)
-- Input: Images (RGB)
-- Output: Binary classification with confidence score
-- Max Image Size: 224x224 (automatically resized)
-
-## Requirements
-- transformers>=4.30.0
-- torch>=2.0.0
-- Pillow>=9.0.0
-
-## Limitations
-- Best performance with clear, high-quality images
-- May have reduced accuracy with heavily edited photos
-- Designed for general image detection
-
-## Web Integration Example
-```javascript
-async function detectImage(imageFile) {
-  const formData = new FormData();
-  formData.append('image', imageFile);
-
-  const response = await fetch('YOUR_API_ENDPOINT', {
-    method: 'POST',
-    body: formData
-  });
-
-  return await response.json();
-}
+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+processor = ViTImageProcessor.from_pretrained("yaya36095/ai-image-detector")
+model = ViTForImageClassification.from_pretrained("yaya36095/ai-image-detector")
+
+def detect_image(image_path):
+    # Open image
+    image = Image.open(image_path)
+    
+    # Process image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Get predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = outputs.logits.softmax(dim=-1)
+    
+    # Get result
+    prediction_id = torch.argmax(predictions).item()
+    confidence = predictions[0][prediction_id].item() * 100
+    
+    result = "AI Generated" if prediction_id == 1 else "Real Image"
+    return result, confidence
+
+# Example usage
+# result, confidence = detect_image("path/to/image.jpg")
+# print(f"Result: {result} (Confidence: {confidence:.2f}%)")