Update README.md

README.md

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-# AI Source Detector
 
-## وصف النموذج
-هذا النموذج يعتمد على معمارية **XLM-RoBERTa** وتم تصميمه لتحديد مصدر النصوص المولدة، مثل التعرف على النصوص التي تم إنشاؤها بواسطة الذكاء الاصطناعي مقابل النصوص الحقيقية المكتوبة بواسطة البشر. يدعم النموذج مهام تصنيف النصوص متعددة اللغات.
+AI IMAGE AND SOURCE DETECTOR
+============================
 
----
+1. AI IMAGE DETECTOR
+--------------------
+- Detects if an image is real or AI-generated.
+- Uses Vision Transformer (ViT) architecture.
+- High accuracy for various image types.
 
-## التفاصيل التقنية
+2. AI SOURCE DETECTOR
+---------------------
+- Identifies the source of AI-generated images.
+- Supports: Midjourney, DALL-E, Stable Diffusion.
+- Provides confidence scores.
 
-- **المعمارية:** XLMRobertaForSequenceClassification
-- **نوع النموذج:** XLM-RoBERTa
-- **عدد الطبقات المخفية:** 12
-- **عدد رؤوس الانتباه (Attention Heads):** 12
-- **الحجم المخفي:** 768
-- **نوع المشكلة:** Single Label Classification
-- **الإصدار المستخدم من Transformers:** 4.44.2
+USAGE
+-----
+### Python Code Example for AI Image Detector
+```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):
+    image = Image.open(image_path)
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    predictions = outputs.logits.softmax(dim=-1)
+    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
 
-## طريقة الاستخدام
+# Example usage
+# result, confidence = detect_image("path/to/image.jpg")
+# print(f"Result: {result} (Confidence: {confidence:.2f}%)")
+```
 
-### Python
+### Python Code Example for AI Source Detector
 ```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
 import torch
 
-# تحميل النموذج والمُرمِّز
-model_name = "yaya36095/ai-source-detector"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# Load model and processor
+processor = ViTImageProcessor.from_pretrained("yaya36095/ai-source-detector")
+model = ViTForImageClassification.from_pretrained("yaya36095/ai-source-detector")
+
+def detect_source(image_path):
+    image = Image.open(image_path)
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    predictions = outputs.logits.softmax(dim=-1)
+    prediction_id = torch.argmax(predictions).item()
+    confidence = predictions[0][prediction_id].item() * 100
+    sources = ["Real Image", "Midjourney", "DALL-E", "Stable Diffusion"]
+    result = sources[prediction_id]
+    return result, confidence
+
+# Example usage
+# result, confidence = detect_source("path/to/image.jpg")
+# print(f"Source: {result} (Confidence: {confidence:.2f}%)")
+```
+
+### Combined Analysis Code Example
+```python
+def analyze_image(image_path):
+    """
+    Analyzes an image to detect if it is AI-generated and its source.
+
+    Args:
+        image_path: Path to the image file
+
+    Returns:
+        dict: Results containing detection and source information
+    """
+    # Load models
+    detector = ViTForImageClassification.from_pretrained("yaya36095/ai-image-detector")
+    source_detector = ViTForImageClassification.from_pretrained("yaya36095/ai-source-detector")
+    processor = ViTImageProcessor.from_pretrained("yaya36095/ai-image-detector")
+    
+    # Open and process image
+    image = Image.open(image_path)
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Get AI detection result
+    with torch.no_grad():
+        outputs = detector(**inputs)
+        predictions = outputs.logits.softmax(dim=-1)
+    
+    is_ai = torch.argmax(predictions).item() == 1
+    ai_confidence = predictions[0][1].item() * 100
+    
+    # Get source detection if it is AI
+    source = "Not AI Generated"
+    source_confidence = 0
+    
+    if is_ai:
+        with torch.no_grad():
+            outputs = source_detector(**inputs)
+            predictions = outputs.logits.softmax(dim=-1)
+        
+        source_id = torch.argmax(predictions).item()
+        sources = ["Real Image", "Midjourney", "DALL-E", "Stable Diffusion"]
+        source = sources[source_id]
+        source_confidence = predictions[0][source_id].item() * 100
+    
+    return {
+        "is_ai": is_ai,
+        "ai_confidence": ai_confidence,
+        "source": source,
+        "source_confidence": source_confidence
+    }
+
+# Example usage:
+# result = analyze_image("path/to/image.jpg")
+# print(f"AI Generated: {result['is_ai']}")
+# print(f"AI Confidence: {result['ai_confidence']:.2f}%")
+# print(f"Source: {result['source']}")
+# print(f"Source Confidence: {result['source_confidence']:.2f}%")
+```
 
-# تحليل النص
-text = "This is a sample text to classify."
-inputs = tokenizer(text, return_tensors="pt")
-outputs = model(**inputs)
+FEATURES
+--------
+- Supports all image formats.
+- Automatic image resizing.
+- Confidence scores for predictions.
+- Combined analysis (AI detection + Source).
 
-# النتيجة
-logits = outputs.logits
-predicted_class = logits.argmax(-1).item()
+LIMITATIONS
+-----------
+- Best with clear, high-quality images.
+- May vary with heavily edited images.
+- Requires good internet connection for first load.
 
-# التصنيفات
-id2label = {0: "real", 1: "ai_generated"}
-print(f"The text is classified as: {id2label[predicted_class]}")
+For more information visit:
+- [AI Image Detector](https://huggingface.co/yaya36095/ai-image-detector)
+- [AI Source Detector](https://huggingface.co/yaya36095/ai-source-detector)