Update README.md

README.md

@@ -15,44 +15,53 @@ This model is designed to detect whether an image is real or AI-generated. It us
 
 ## Model Usage
 ```python
+```python
 from transformers import ViTImageProcessor, ViTForImageClassification
 from PIL import Image
 import torch
 
-# Load model and processor
+# تحميل النموذج والمعالج
 processor = ViTImageProcessor.from_pretrained("yaya36095/ai-image-detector")
 model = ViTForImageClassification.from_pretrained("yaya36095/ai-image-detector")
 
-# Define class labels (Replace these with the labels used during training)
-class_labels = ["Real Image", "AI Generated"]
-
 def detect_image(image_path):
-    # Open image
+    # فتح وتجهيز الصورة
     image = Image.open(image_path)
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
     
-    # Process image
+    # معالجة الصورة
     inputs = processor(images=image, return_tensors="pt")
     
-    # Get predictions
+    # الحصول على التنبؤات
     with torch.no_grad():
         outputs = model(**inputs)
         predictions = outputs.logits.softmax(dim=-1)
     
-    # Map predictions to class labels
+    # تحليل النتائج
+    scores = predictions[0].tolist()
     results = [
-        {"label": class_labels[i], "confidence": f"{(predictions[0][i].item() * 100):.2f}%"}
-        for i in range(len(class_labels))
+        {"label": "REAL", "score": scores[0]},
+        {"label": "FAKE", "score": scores[1]}
     ]
     
-    # Sort results by confidence (optional)
-    results = sorted(results, key=lambda x: float(x["confidence"].strip('%')), reverse=True)
+    # ترتيب النتائج حسب درجة الثقة
+    results.sort(key=lambda x: x["score"], reverse=True)
     
-    return results
-
-# Example usage
+    return {
+        "prediction": results[0]["label"],
+        "confidence": f"{results[0]['score']*100:.2f}%",
+        "detailed_scores": [
+            f"{r['label']}: {r['score']*100:.2f}%" 
+            for r in results
+        ]
+    }
+
+# مثال للاستخدام
 # result = detect_image("path/to/image.jpg")
-# for r in result:
-#     print(f"Class: {r['label']}, Confidence: {r['confidence']}")
+# print(f"النتيجة: {result['prediction']}")
+# print(f"درجة الثقة: {result['confidence']}")
+# print("التفاصيل:", *result['detailed_scores'], sep="\n- ")
 
 ```