Update README.md

README.md

@@ -15,33 +15,37 @@ This model is designed to detect whether an image is real or AI-generated. It us
 ## Model Usage
 
 ```python
-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}%)")
+## 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();
+}