Upload app and requirements

app.py

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+# -*- coding: utf-8 -*-
+import gradio as gr
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from ultralytics import YOLO
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from huggingface_hub import hf_hub_download
+import warnings
+warnings.filterwarnings('ignore')
+
+# ============================================================
+# 1. تحميل النموذجين (مرة واحدة عند بدء التشغيل)
+# ============================================================
+print("Loading YOLOv11 model from Hugging Face Hub...")
+# !! غيّر "YOUR_USERNAME" إلى اسم المستخدم الحقيقي الخاص بك !!
+model_path = hf_hub_download(
+    repo_id="Isralnfo2004/drone-detection-yolov11",  # <- غيّر هذا
+    filename="best.pt"
+)
+model = YOLO(model_path)
+print("YOLO model loaded successfully.")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+print("Loading BLIP model...")
+blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
+blip_model.eval()
+print("BLIP model loaded successfully.")
+
+# ============================================================
+# 2. دوال الخريطة الحرارية (نفس كودك الأصلي، بدون تغيير)
+# ============================================================
+layer_outputs = {}
+
+def hook_fn(module, input, output):
+    layer_outputs['feature_map'] = output.detach()
+
+def get_best_layer(model):
+    best_layer = None
+    best_depth = 0
+    pytorch_model = model.model if hasattr(model, 'model') else model
+    for name, layer in pytorch_model.named_modules():
+        if isinstance(layer, torch.nn.Conv2d):
+            depth = name.count('.')
+            if depth > best_depth:
+                best_depth = depth
+                best_layer = layer
+    return best_layer
+
+def generate_heatmap(model, image):
+    try:
+        layer_outputs.clear()
+        # تأكد من أن الصورة من نوع RGB numpy array
+        if isinstance(image, Image.Image):
+            image = np.array(image)
+        img_resized = cv2.resize(image, (640, 640))
+        pytorch_model = model.model if hasattr(model, 'model') else model
+        target_layer = get_best_layer(pytorch_model)
+        if target_layer is None:
+            return None
+        hook = target_layer.register_forward_hook(hook_fn)
+        results = model(img_resized)
+        hook.remove()
+        if 'feature_map' not in layer_outputs:
+            return None
+        feature_map = layer_outputs['feature_map']
+        if feature_map.dim() == 4:
+            heatmap = feature_map[0].mean(dim=0).cpu().numpy()
+        else:
+            heatmap = feature_map.cpu().numpy()
+        heatmap = cv2.GaussianBlur(heatmap, (5, 5), 0)
+        min_val = heatmap.min()
+        max_val = heatmap.max()
+        if max_val - min_val > 1e-8:
+            heatmap = (heatmap - min_val) / (max_val - min_val)
+        else:
+            heatmap = np.zeros_like(heatmap)
+        heatmap = cv2.resize(heatmap, (640, 640))
+        threshold = np.percentile(heatmap, 70)
+        heatmap = np.where(heatmap > threshold, heatmap, 0)
+        if heatmap.max() > 0:
+            heatmap = heatmap / heatmap.max()
+        heatmap_colored = cv2.applyColorMap((heatmap * 255).astype(np.uint8), cv2.COLORMAP_JET)
+        heatmap_colored = cv2.cvtColor(heatmap_colored, cv2.COLOR_BGR2RGB)
+        overlay = cv2.addWeighted(img_resized, 0.6, heatmap_colored, 0.4, 0)
+        return overlay
+    except Exception as e:
+        print(f"Heatmap error: {e}")
+        return None
+
+# ============================================================
+# 3. دالة الوصف النصي (BLIP)
+# ============================================================
+def generate_dynamic_caption(image):
+    try:
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image).convert('RGB')
+        elif isinstance(image, Image.Image):
+            image = image.convert('RGB')
+        inputs = blip_processor(image, return_tensors="pt").to(device)
+        with torch.no_grad():
+            out = blip_model.generate(
+                **inputs,
+                max_length=60,
+                num_beams=5,
+                temperature=0.7,
+                repetition_penalty=1.2
+            )
+        caption = blip_processor.decode(out[0], skip_special_tokens=True)
+        return caption
+    except Exception as e:
+        print(f"Caption error: {e}")
+        return "AI model is analyzing the scene."
+
+# ============================================================
+# 4. دالة بناء التقرير (مطابقة لكودك الأصلي)
+# ============================================================
+def build_xai_report(is_drone, confidence, drone_count, processing_time, image_caption):
+    confidence_percent = confidence * 100
+    if is_drone:
+        drone_text = "a drone" if drone_count == 1 else f"{drone_count} drones"
+        if confidence >= 0.8:
+            confidence_level = "VERY HIGH"
+            confidence_assessment = "excellent"
+        elif confidence >= 0.6:
+            confidence_level = "HIGH"
+            confidence_assessment = "good"
+        elif confidence >= 0.5:
+            confidence_level = "MODERATE"
+            confidence_assessment = "acceptable"
+        else:
+            confidence_level = "LOW"
+            confidence_assessment = "limited"
+        
+        report = f"""
+================================================================================
+                         XAI DRONE DETECTION REPORT
+================================================================================
+
+[DYNAMIC IMAGE ANALYSIS]
+{image_caption}
+
+[DETECTION RESULTS]
+• Status: CONFIRMED
+• Confidence: {confidence:.1%} (Level: {confidence_level})
+• Drones Detected: {drone_count}
+• Processing Time: {processing_time:.0f} milliseconds
+• Model: YOLOv11
+• XAI Method: Convolutional Feature Map Extraction
+
+[XAI HEATMAP INTERPRETATION]
+The heatmap shows red regions where the neural network focused its attention.
+Strong red activation on {drone_text} confirms the model successfully learned
+discriminative features for drone detection.
+
+The model demonstrates {confidence_assessment} confidence, as evidenced by
+the concentrated activation pattern in the heatmap.
+
+[TECHNICAL DETAILS]
+• Heatmap: Extracted from deepest convolutional layer of YOLOv11
+• Color Code: Red = High activation (model focus) | Blue = Low activation
+• Processing: Gaussian blur (5x5 kernel)
+• Threshold: Top 30% activation retained
+
+[XAI CONCLUSION]
+The YOLOv11 model has successfully detected {drone_text} in this image with
+{confidence_assessment} confidence. The heatmap confirms correct feature
+learning as the neural network focused on the drone's location.
+"""
+    else:
+        report = f"""
+================================================================================
+                         XAI DRONE DETECTION REPORT
+================================================================================
+
+[DYNAMIC IMAGE ANALYSIS]
+{image_caption}
+
+[DETECTION RESULTS]
+• Status: NOT CONFIRMED
+• Highest Confidence: {confidence:.1%}
+• Processing Time: {processing_time:.0f} milliseconds
+• Model: YOLOv11
+• XAI Method: Convolutional Feature Map Extraction
+
+[XAI HEATMAP INTERPRETATION]
+The heatmap shows scattered or unfocused activation patterns without strong
+concentration on any specific region. This indicates the model did not identify
+strong drone-like features in this image.
+
+[POSSIBLE REASONS]
+• No drone is present in the image
+• Drone is too small or too far from the camera
+• Poor lighting conditions reduce feature visibility
+• Image blur or motion blur affects detection quality
+• Drone is partially occluded by other objects
+
+[RECOMMENDATIONS]
+• Ensure adequate lighting in the scene
+• Position the drone closer to the camera
+• Use higher resolution images without motion blur
+• Avoid cluttered backgrounds that may confuse the model
+
+[TECHNICAL NOTE]
+The heatmap was extracted from the deepest convolutional layer of YOLOv11.
+Scattered activation pattern confirms absence of strong drone-like features.
+"""
+    return report
+
+# ============================================================
+# 5. الدالة الرئيسية التي سيربطها Gradio
+# ============================================================
+def drone_detection_pipeline(input_image):
+    """
+    المدخلات: صورة (PIL Image أو numpy array)
+    المخرجات: (صورة النتيجة, صورة الخريطة الحرارية, تقرير نصي)
+    """
+    try:
+        # تحويل الصورة إلى numpy array (RGB)
+        if isinstance(input_image, Image.Image):
+            img = np.array(input_image)
+        else:
+            img = input_image.copy()
+        
+        original_h, original_w = img.shape[:2]
+        
+        # 1. تنفيذ الكشف
+        results = model(img)
+        
+        # 2. إنشاء الخريطة الحرارية
+        heatmap_overlay = generate_heatmap(model, img)
+        
+        # 3. استخراج معلومات الكشف
+        is_drone = False
+        confidence = 0.0
+        drone_count = 0
+        if results and len(results) > 0 and hasattr(results[0], 'boxes'):
+            boxes_data = results[0].boxes
+            if boxes_data and boxes_data.data is not None:
+                data = boxes_data.data.cpu().numpy()
+                for det in data:
+                    # تنسيق det: x1, y1, x2, y2, conf, cls
+                    if len(det) >= 6:
+                        conf = float(det[4])
+                        cls = int(det[5])
+                        class_name = model.names[cls]
+                        if class_name.lower() == 'drone' and conf >= 0.3:
+                            drone_count += 1
+                            confidence = max(confidence, conf)
+        is_drone = drone_count > 0
+        
+        # 4. إنشاء الصورة المعلّمة (مع المربعات)
+        result_img = results[0].plot() if len(results) > 0 else img
+        result_img_rgb = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
+        
+        # 5. إنشاء الوصف النصي
+        caption = generate_dynamic_caption(img)
+        
+        # 6. حساب وقت المعالجة (تقريبي)
+        processing_time_ms = 0  # يمكن تركه صفراً أو حسابه فعلياً
+        
+        # 7. بناء التقرير
+        report = build_xai_report(is_drone, confidence, drone_count, processing_time_ms, caption)
+        
+        # 8. معالجة الخريطة الحرارية لتتناسب مع أبعاد الصورة الأصلية
+        if heatmap_overlay is not None:
+            heatmap_resized = cv2.resize(heatmap_overlay, (original_w, original_h))
+        else:
+            heatmap_resized = np.zeros_like(result_img_rgb)
+        
+        return result_img_rgb, heatmap_resized, report
+    
+    except Exception as e:
+        error_msg = f"An error occurred during processing: {str(e)}"
+        print(error_msg)
+        # إرجاع صور فارغة مع رسالة الخطأ
+        blank = np.zeros((480, 640, 3), dtype=np.uint8)
+        return blank, blank, error_msg
+
+# ============================================================
+# 6. بناء واجهة Gradio (جميلة واحترافية)
+# ============================================================
+with gr.Blocks(title="Drone Detection with XAI", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    <div style="text-align: center;">
+        <h1>🚁 نظام كشف الطائرات بدون طيار مع الذكاء الاصطناعي القابل للتفسير (XAI)</h1>
+        <p>يستخدم النظام نموذج <strong>YOLOv11</strong> للكشف، مع <strong>خريطة حرارية</strong> لتوضيح مناطق التركيز في الشبكة العصبية، بالإضافة إلى <strong>وصف نصي ديناميكي</strong> للصورة باستخدام نموذج BLIP.</p>
+        <p>📌 <strong>ملاحظة:</strong> الخريطة الحرارية تستخرج من أعمق طبقة تلافيفية في YOLOv11، وتظهر المناطق التي ركز عليها النموذج لاتخاذ القرار.</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image = gr.Image(label="📸 رفع صورة للتحليل", type="pil")
+            submit_btn = gr.Button("ابدأ التحليل", variant="primary", size="lg")
+        with gr.Column(scale=2):
+            with gr.Tabs():
+                with gr.TabItem("🔍 نتيجة الكشف"):
+                    output_image = gr.Image(label="الصورة مع المربعات المحيطة")
+                with gr.TabItem("🔥 خريطة XAI الحرارية"):
+                    heatmap_image = gr.Image(label="مناطق التركيز العصبي (الأحمر = تركيز عالٍ)")
+                with gr.TabItem("📄 تقرير XAI التفصيلي"):
+                    report_text = gr.Markdown(label="التقرير الكامل")
+    
+    submit_btn.click(
+        fn=drone_detection_pipeline,
+        inputs=[input_image],
+        outputs=[output_image, heatmap_image, report_text]
+    )
+    
+    gr.Markdown("""
+    <div style="text-align: center; margin-top: 30px; font-size: 12px; color: gray;">
+        <hr>
+        <p>تم التطوير باستخدام YOLOv11, Gradio, Hugging Face Spaces 🤗 | نموذج الكشف مستضاف على Hugging Face Hub</p>
+    </div>
+    """)
+
+# ============================================================
+# 7. تشغيل التطبيق
+# ============================================================
+if __name__ == "__main__":
+    demo.launch()

requirements.txt.txt

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+gradio>=4.0.0
+ultralytics>=8.0.0
+transformers>=4.35.0
+torch>=2.0.0
+torchvision>=0.15.0
+Pillow>=10.0.0
+opencv-python-headless>=4.8.0
+huggingface_hub>=0.20.0