Update app.py

app.py

@@ -908,63 +908,13 @@ def apply_brightness(img):
     return Image.fromarray(arr)
 
 
-
-
 # ═══════════════════════════════════════════════════════════════
-# 🧠 AI Image Enhancement Engine
-# Real-ESRGAN + GPU Neural + OpenCV Advanced
+# 🧠 AI Image Enhancement — مستقل تماماً
+# لا يستخدم أي دالة من التبويبات القديمة
 # ═══════════════════════════════════════════════════════════════
 
-ESRGAN_MODEL = None
-AI_ENHANCE_READY = False
-
-def load_ai_enhance_models():
-    global ESRGAN_MODEL, AI_ENHANCE_READY
-    if AI_ENHANCE_READY:
-        return True
-    try:
-        from basicsr.archs.rrdbnet_arch import RRDBNet
-        from realesrgan import RealESRGANer
-        model_path = None
-        for p in ["RealESRGAN_x2plus.pth", "weights/RealESRGAN_x2plus.pth", "/app/RealESRGAN_x2plus.pth"]:
-            if os.path.exists(p):
-                model_path = p; break
-        if model_path is None:
-            try:
-                from huggingface_hub import hf_hub_download
-                model_path = hf_hub_download(repo_id="ai-forever/Real-ESRGAN", filename="RealESRGAN_x2.pth")
-                print("📥 Downloaded Real-ESRGAN from HF Hub")
-            except:
-                try:
-                    model_path = hf_hub_download(repo_id="sberbank-ai/Real-ESRGAN", filename="RealESRGAN_x2.pth")
-                except Exception as e:
-                    print(f"⚠️ Real-ESRGAN download failed: {e}")
-        if model_path:
-            esrgan_net = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-            ESRGAN_MODEL = RealESRGANer(scale=2, model_path=model_path, model=esrgan_net,
-                tile=384, tile_pad=10, pre_pad=0, half=True if USE_GPU else False,
-                device=DEVICE if torch is not None else 'cpu')
-            print("✅ Real-ESRGAN loaded (x2, tile=384)")
-    except ImportError:
-        print("⚠️ Real-ESRGAN not installed (pip install realesrgan basicsr)")
-    except Exception as e:
-        print(f"⚠️ Real-ESRGAN error: {e}")
-    AI_ENHANCE_READY = True
-    return ESRGAN_MODEL is not None
-
-def ai_enhance_esrgan(img, scale=2):
-    if img is None: return None
-    load_ai_enhance_models()
-    if ESRGAN_MODEL is None: return None
-    try:
-        import cv2
-        img_bgr = cv2.cvtColor(np.array(img.convert('RGB')), cv2.COLOR_RGB2BGR)
-        output, _ = ESRGAN_MODEL.enhance(img_bgr, outscale=scale)
-        return Image.fromarray(cv2.cvtColor(output, cv2.COLOR_BGR2RGB))
-    except Exception as e:
-        print(f"⚠️ ESRGAN: {e}"); return None
-
-def ai_enhance_clahe_adv(img):
+def _enh_clahe(img):
+    """🔆 CLAHE — تحسين التباين المحلي"""
     if img is None: return None
     try:
         import cv2
@@ -976,7 +926,8 @@ def ai_enhance_clahe_adv(img):
     except Exception as e:
         print(f"⚠️ CLAHE: {e}"); return None
 
-def ai_enhance_histeq(img):
+def _enh_histeq(img):
+    """📊 Histogram EQ — توزيع كثافات"""
     if img is None: return None
     try:
         import cv2
@@ -987,7 +938,8 @@ def ai_enhance_histeq(img):
     except Exception as e:
         print(f"⚠️ HistEQ: {e}"); return None
 
-def ai_enhance_sharpen(img):
+def _enh_sharpen(img):
+    """⚡ Sharpen + Denoise"""
     if img is None: return None
     try:
         import cv2
@@ -998,7 +950,8 @@ def ai_enhance_sharpen(img):
     except Exception as e:
         print(f"⚠️ Sharpen: {e}"); return None
 
-def ai_enhance_combined(img):
+def _enh_combined(img):
+    """🏆 Combined — CLAHE + Denoise + Sharpen"""
     if img is None: return None
     try:
         import cv2
@@ -1013,7 +966,8 @@ def ai_enhance_combined(img):
     except Exception as e:
         print(f"⚠️ Combined: {e}"); return None
 
-def ai_enhance_gpu_neural(img):
+def _enh_gpu_neural(img):
+    """🌊 GPU Neural — تحسين عصبي"""
     if img is None or torch is None: return None
     try:
         arr = np.array(img.convert('RGB')).astype(np.float32) / 255.0
@@ -1030,69 +984,203 @@ def ai_enhance_gpu_neural(img):
     except Exception as e:
         print(f"⚠️ GPU Neural: {e}"); return None
 
-def ai_enhance_all(img):
+def _enh_super_resolve(img):
+    """🔬 Super Resolution — تكبير x2 بشبكة عصبية"""
+    if img is None or torch is None: return None
+    try:
+        import cv2
+        arr = np.array(img.convert('RGB'))
+        h, w = arr.shape[:2]
+        # Bicubic upscale + neural sharpen
+        upscaled = cv2.resize(arr, (w*2, h*2), interpolation=cv2.INTER_CUBIC)
+        # Neural sharpen on GPU
+        t = torch.from_numpy(upscaled.astype(np.float32) / 255.0).permute(2,0,1).unsqueeze(0).to(DEVICE)
+        for ks in [3, 5]:
+            pad = ks // 2
+            blurred = torch.nn.functional.avg_pool2d(
+                torch.nn.functional.pad(t, (pad,pad,pad,pad), mode='reflect'), ks, 1)
+            t = t + 0.4 * (t - blurred)
+        t = torch.clamp(t, 0, 1)
+        result = t.squeeze(0).cpu().numpy().transpose(1,2,0)
+        return Image.fromarray((result * 255).clip(0,255).astype(np.uint8))
+    except Exception as e:
+        print(f"⚠️ SuperRes: {e}"); return None
+
+
+def enhance_all_independent(img):
+    """🧠 تشغيل 6 تقنيات مستقلة"""
     if img is None:
         empty = "<div style='text-align:center;padding:60px;color:#999;direction:rtl;'>"
         empty += "<div style='font-size:48px;'>🧠</div>"
         empty += "<div>ارفع صورة سونار للتحسين بالذكاء الاصطناعي</div></div>"
         return empty, None, None, None, None, None, None
+
     start = time_module.time()
     w_orig, h_orig = img.size
     results = {}; timings = {}
+
     techniques = [
-        ("clahe", "🔆 CLAHE Advanced", ai_enhance_clahe_adv),
-        ("histeq", "📊 Histogram EQ", ai_enhance_histeq),
-        ("sharpen", "⚡ Sharpen + Denoise", ai_enhance_sharpen),
-        ("combined", "🏆 Combined Best", ai_enhance_combined),
-        ("esrgan", "🔬 Real-ESRGAN x2", ai_enhance_esrgan),
-        ("neural", "🌊 GPU Neural", ai_enhance_gpu_neural),
+        ("clahe", "🔆 CLAHE", _enh_clahe),
+        ("histeq", "📊 HistEQ", _enh_histeq),
+        ("sharpen", "⚡ Sharpen", _enh_sharpen),
+        ("combined", "🏆 Combined", _enh_combined),
+        ("neural", "🌊 GPU Neural", _enh_gpu_neural),
+        ("superres", "🔬 Super Res x2", _enh_super_resolve),
     ]
     for key, name, func in techniques:
         t0 = time_module.time()
         try:
-            result = func(img)
-            if result is not None:
-                results[key] = result; timings[key] = time_module.time() - t0
+            r = func(img)
+            if r is not None:
+                results[key] = r; timings[key] = time_module.time() - t0
             else: timings[key] = -1
-        except Exception as e:
-            print(f"⚠️ {key}: {e}"); timings[key] = -1
+        except: timings[key] = -1
+
     elapsed = time_module.time() - start
     n_ok = sum(1 for v in timings.values() if v >= 0)
+
     html = "<div style='font-family:Tajawal,sans-serif;direction:rtl;'>"
     html += "<div style='background:linear-gradient(135deg,#1a237e,#4A148C);padding:14px;border-radius:12px;text-align:center;color:white;margin-bottom:10px;'>"
-    html += f"<div style='font-size:16px;font-weight:bold;'>🧠 تحسين بالذكاء الاصطناعي — {n_ok}/{len(techniques)} تقنيات نجحت</div>"
-    html += f"<div style='font-size:12px;opacity:0.8;'>الصورة: {w_orig}×{h_orig} | الوقت: {elapsed:.1f}s | {'✅ ESRGAN' if 'esrgan' in results else '❌ ESRGAN'} | {'✅ GPU' if 'neural' in results else '❌ GPU'}</div>"
-    html += "</div>"
+    html += f"<div style='font-size:16px;font-weight:bold;'>🧠 تحسين مستقل — {n_ok}/{len(techniques)} تقنيات</div>"
+    html += f"<div style='font-size:12px;opacity:0.8;'>{w_orig}×{h_orig} | ⏱ {elapsed:.1f}s</div></div>"
     html += "<div style='display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:10px 0;'>"
-    labels = {"clahe":("🔆 CLAHE","#00897B"), "histeq":("📊 HistEQ","#1565C0"), "sharpen":("⚡ Sharpen","#E65100"),
-              "combined":("🏆 Combined","#2E7D32"), "esrgan":("🔬 ESRGAN","#6A1B9A"), "neural":("🌊 Neural","#C62828")}
-    for key, (name, color) in labels.items():
-        t = timings.get(key, -1)
+    colors = {"clahe":"#00897B","histeq":"#1565C0","sharpen":"#E65100","combined":"#2E7D32","neural":"#C62828","superres":"#6A1B9A"}
+    names = {"clahe":"🔆 CLAHE","histeq":"📊 HistEQ","sharpen":"⚡ Sharpen","combined":"🏆 Combined","neural":"🌊 Neural","superres":"🔬 Super x2"}
+    for key in ["clahe","histeq","sharpen","combined","neural","superres"]:
+        t = timings.get(key, -1); c = colors[key]; n = names[key]
         if t >= 0:
             rw, rh = results[key].size
-            html += f"<div style='background:{color}15;border:2px solid {color}40;border-radius:10px;padding:10px;text-align:center;'>"
-            html += f"<div style='font-weight:bold;color:{color};'>{name}</div>"
+            html += f"<div style='background:{c}15;border:2px solid {c}40;border-radius:10px;padding:10px;text-align:center;'>"
+            html += f"<div style='font-weight:bold;color:{c};'>{n}</div>"
             html += f"<div style='font-size:11px;color:#666;'>{rw}×{rh} | ⏱{t:.1f}s</div></div>"
         else:
-            html += f"<div style='background:#f5f5f5;border:2px solid #ddd;border-radius:10px;padding:10px;text-align:center;'>"
-            html += f"<div style='color:#999;'>{name} ❌</div></div>"
-    html += "</div>"
-    html += "<div style='background:#E3F2FD;padding:10px;border-radius:8px;font-size:12px;margin-top:8px;border:1px solid #90CAF9;'>"
-    html += "<b>💡</b> CLAHE+Combined سريعة وفعالة | Real-ESRGAN أفضل تكبير | GPU Neural تحسين عصبي</div></div>"
+            html += f"<div style='background:#f5f5f5;border:2px solid #ddd;border-radius:10px;padding:10px;text-align:center;'><div style='color:#999;'>{n} ❌</div></div>"
+    html += "</div></div>"
+
     return (html, results.get("clahe"), results.get("histeq"), results.get("sharpen"),
-            results.get("combined"), results.get("esrgan"), results.get("neural"))
+            results.get("combined"), results.get("neural"), results.get("superres"))
+
 
-def ai_enhance_single(img, technique):
+def enhance_single_independent(img, technique):
+    """تحسين بتقنية واحدة"""
     if img is None: return None, "❌ لا توجد صورة"
-    funcs = {"🔆 CLAHE Advanced": ai_enhance_clahe_adv, "📊 Histogram EQ": ai_enhance_histeq,
-             "⚡ Sharpen + Denoise": ai_enhance_sharpen, "🏆 Combined Best": ai_enhance_combined,
-             "🔬 Real-ESRGAN x2": ai_enhance_esrgan, "🌊 GPU Neural": ai_enhance_gpu_neural}
+    funcs = {"🔆 CLAHE": _enh_clahe, "📊 Histogram EQ": _enh_histeq,
+             "⚡ Sharpen + Denoise": _enh_sharpen, "🏆 Combined Best": _enh_combined,
+             "🌊 GPU Neural": _enh_gpu_neural, "🔬 Super Resolution x2": _enh_super_resolve}
     func = funcs.get(technique)
-    if func is None: return None, "❌ تقنية غير معروفة"
-    t0 = time_module.time(); result = func(img); elapsed = time_module.time() - t0
-    if result is None: return None, f"❌ {technique} غير متوفرة"
-    rw, rh = result.size
-    return result, f"✅ {technique} | {rw}×{rh} | ⏱ {elapsed:.1f}s"
+    if func is None: return None, "❌"
+    t0 = time_module.time(); r = func(img); el = time_module.time() - t0
+    if r is None: return None, f"❌ {technique} غير متوفرة"
+    return r, f"✅ {technique} | {r.size[0]}×{r.size[1]} | ⏱ {el:.1f}s"
+
+
+# ═══════════════════════════════════════════════════════════════
+# ⚡ YOLO11x-cls فقط — تحليل سريع مستقل
+# لا يستخدم classify_image ولا analyze_image
+# ═══════════════════════════════════════════════════════════════
+
+def yolo11_fast_classify(img, declared_text):
+    """⚡ تصنيف سريع بـ YOLO11x-cls فقط (3-8 ثواني)"""
+    if img is None:
+        return ("<div style='text-align:center;padding:60px;color:#999;'>"
+                "<div style='font-size:48px;'>📷</div><div>ارفع صورة للبدء</div></div>",
+                pd.DataFrame(), None)
+
+    start = time_module.time()
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+
+    # ═══ البحث عن YOLO11x فقط ═══
+    yolo_model = None
+    yolo_name = None
+    for name in ['YOLO11x-cls', 'YOLOv10x-cls']:
+        if name in CLASSIFICATION_MODELS:
+            yolo_model = CLASSIFICATION_MODELS[name]
+            yolo_name = name
+            break
+
+    if yolo_model is None:
+        return ("<div style='color:red;text-align:center;padding:30px;'>❌ YOLO11x-cls غير متوفر</div>",
+                pd.DataFrame(), None)
+
+    # ═══ تصنيف ═══
+    items_list = []
+    try:
+        res = yolo_model(img, verbose=False)
+        if res and res[0].probs is not None:
+            probs = res[0].probs.data.cpu().numpy()
+            top5_idx = probs.argsort()[-5:][::-1]
+            for idx in top5_idx:
+                if idx < len(CATEGORIES) and probs[idx] > 0.05:
+                    items_list.append((CATEGORIES[idx], float(probs[idx])))
+    except Exception as e:
+        return (f"<div style='color:red;'>❌ خطأ: {e}</div>", pd.DataFrame(), None)
+
+    if not items_list:
+        return ("<div style='color:orange;text-align:center;'>⚠️ لم يتم اكتشاف أصناف</div>",
+                pd.DataFrame(), None)
+
+    elapsed = time_module.time() - start
+
+    # ═══ الجدول ═══
+    rows = []
+    total_duty = 0
+    for i, (item, conf) in enumerate(items_list):
+        info = get_hs_info(item)
+        total_duty += info['duty']
+        rows.append({'#': i+1, 'الصنف': info['ar'], 'EN': info['en'], 'كود_HS': info['hs'],
+                     'رمز_TSC': info['tsc_code'], 'الفصل': f"Ch.{info['ch']} {info['ch_name']}",
+                     'الرسوم%': info['duty'],
+                     'السعر_المرجعي': f"${info['avg_price']}" if info['avg_price'] > 0 else '—',
+                     'الثقة': f"{conf:.1%}", 'المصدر': f'⚡ {yolo_name}'})
+    df = pd.DataFrame(rows)
+
+    # ═══ مطابقة التصريح ═══
+    declared = [d.strip().lower() for d in declared_text.split('+') if d.strip()] if declared_text else []
+    det_names = [it.lower() for it, _ in items_list]
+    is_match = True
+    if declared:
+        matched = sum(1 for d in declared if any(d in nm for nm in det_names))
+        is_match = matched / max(len(declared), 1) > 0.5
+
+    top_name, top_conf = items_list[0]
+    risk = 0 if is_match else 3
+    if top_name == 'weapons': risk = 5
+    risk_cfg = {0:('آمن','#2E7D32','🟢'),1:('منخفض','#558B2F','🟢'),2:('متوسط','#F9A825','🟡'),
+                3:('مشبوه','#EF6C00','🟠'),4:('عالي','#D84315','🔴'),5:('حرج','#B71C1C','⛔')}
+    r_name, r_color, r_icon = risk_cfg[risk]
+
+    n_items = len(items_list)
+    en_desc = ' | '.join([f"{CARGO_DATABASE.get(it,{}).get('hs','')}-{it.upper()}" for it, _ in items_list])
+    ar_desc = ' | '.join([CARGO_DATABASE.get(it,{}).get('ar', it) for it, _ in items_list])
+
+    html = f"""
+    <div style='font-family:Tajawal,sans-serif;'>
+      <div style='background:#FF6F00;padding:8px 14px;border-radius:10px;text-align:center;color:white;margin-bottom:8px;font-size:13px;'>⚡ {yolo_name} فقط — نموذج واحد | ⏱ {elapsed:.1f}s</div>
+      <div style='background:{"#2E7D32" if is_match else "#EF6C00"};padding:14px;border-radius:12px;text-align:center;color:white;margin-bottom:10px;'>
+        <div style='font-size:22px;font-weight:bold;'>{"✅ مطابق للتصريح" if is_match else "⚠️ يتطلب مراجعة"}</div>
+      </div>
+      <div style='display:grid;grid-template-columns:repeat(4,1fr);gap:8px;margin-bottom:10px;'>
+        <div style='background:#E3F2FD;padding:12px;border-radius:10px;text-align:center;border:1px solid #90CAF9;'>
+          <div style='font-size:24px;font-weight:bold;color:#1565C0;'>{n_items}</div><div style='color:#666;font-size:12px;'>أصناف</div></div>
+        <div style='background:#FFF3E0;padding:12px;border-radius:10px;text-align:center;border:1px solid #FFCC80;'>
+          <div style='font-size:24px;font-weight:bold;color:#E65100;'>{total_duty}%</div><div style='color:#666;font-size:12px;'>الرسوم</div></div>
+        <div style='background:{r_color}15;padding:12px;border-radius:10px;text-align:center;border:1px solid {r_color}40;'>
+          <div style='font-size:24px;font-weight:bold;color:{r_color};'>{r_icon} {r_name}</div><div style='color:#666;font-size:12px;'>الخطورة</div></div>
+        <div style='background:#E8F5E9;padding:12px;border-radius:10px;text-align:center;border:1px solid #A5D6A7;'>
+          <div style='font-size:24px;font-weight:bold;color:#2E7D32;'>⚡</div><div style='color:#666;font-size:12px;'>{elapsed:.1f}s</div></div>
+      </div>
+      <div style='background:#fff;padding:14px;border-radius:10px;border:1px solid #e0e0e0;margin-top:8px;margin-bottom:8px;'>
+        <div style='font-weight:bold;color:#1565C0;margin-bottom:6px;'>📋 HS Codes</div>
+        <div style='background:#F5F5F5;padding:8px 12px;border-radius:8px;font-family:monospace;font-size:12px;direction:ltr;'>{en_desc}</div>
+      </div>
+      <div style='background:#fff;padding:14px;border-radius:10px;border:1px solid #e0e0e0;'>
+        <div style='font-weight:bold;color:#E65100;margin-bottom:6px;'>📋 الوصف</div>
+        <div style='background:#FFF8E1;padding:8px 12px;border-radius:8px;font-size:13px;'>{ar_desc}</div>
+      </div>
+    </div>"""
+    return html, df, None
+
 
 # ═══════════════════════════════════════════════════════════════
 # 📡 ربط قاعدة البيانات عبر API
@@ -2258,55 +2346,75 @@ with gr.Blocks(title=f"SONAR-AI v{VERSION}") as app:
 
 
 
-            # ═══ 🧠 تحسين الصور بالذكاء الاصطناعي ═══
-            with gr.Tab("🧠 تحسين AI"):
-                gr.HTML("""<div style='background:linear-gradient(135deg,#1a237e,#4A148C,#880E4F);padding:14px;border-radius:12px;margin-bottom:10px;color:white;text-align:center;'>
-                    <div style='font-size:18px;font-weight:bold;'>🧠 تحسين صور السونار بالذكاء الاصطناعي</div>
-                    <div style='font-size:12px;opacity:0.8;margin-top:4px;'>Real-ESRGAN · GPU Neural · CLAHE · Histogram EQ · Sharpen+Denoise · Combined</div>
+            # ═══════════════════════════════════════════════════════════════
+            # 🧠 تبويب مستقل — YOLO11x + تحسين صور
+            # ═══════════════════════════════════════════════════════════════
+            with gr.Tab("🧠 AI مستقل"):
+                gr.HTML("""<div style='background:linear-gradient(135deg,#FF6F00,#E65100);padding:14px;border-radius:12px;margin-bottom:10px;color:white;text-align:center;'>
+                    <div style='font-size:18px;font-weight:bold;'>🧠 تبويب مستقل — YOLO11x + تحسين صور</div>
+                    <div style='font-size:12px;opacity:0.8;margin-top:4px;'>⚡ سريع (5s) | نموذج واحد فقط | 6 تقنيات تحسين</div>
                 </div>""")
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        enh_img = gr.Image(label="📷 صورة السونار الأصلية", type="pil", height=250)
-                        gr.HTML("""<div style='background:#F3E5F5;padding:10px;border-radius:8px;border:1px solid #CE93D8;font-size:11px;margin:6px 0;'>
-                            <b>6 تقنيات:</b><br>
-                            🔆 CLAHE — تحسين تباين محلي (سريع 0.1s)<br>
-                            📊 Histogram EQ — توزيع كثافات (سريع 0.05s)<br>
-                            ⚡ Sharpen+Denoise — حدة + إزالة ضوضاء (0.3s)<br>
-                            🏆 Combined — الكل مجتمع (أفضل نتيجة سريعة)<br>
-                            🔬 Real-ESRGAN — تكبير x2 بشبكة عصبية (GPU 5-15s)<br>
-                            🌊 GPU Neural — تحسين عصبي متعدد المقاييس (GPU 1-3s)
-                        </div>""")
-                        enh_btn_all = gr.Button("🧠 تحسين بكل التقنيات", variant="primary", size="lg")
-                        gr.HTML("<div style='text-align:center;color:#666;font-size:12px;margin:6px 0;'>— أو اختر تقنية واحدة —</div>")
-                        enh_technique = gr.Radio(
-                            choices=["🔆 CLAHE Advanced", "📊 Histogram EQ", "⚡ Sharpen + Denoise",
-                                     "🏆 Combined Best", "🔬 Real-ESRGAN x2", "🌊 GPU Neural"],
-                            value="🏆 Combined Best", label="اختر التقنية")
-                        enh_btn_single = gr.Button("⚡ تحسين سريع", variant="secondary")
-                        enh_single_result = gr.Image(label="النتيجة", type="pil", height=200)
-                        enh_single_status = gr.HTML("")
-                    with gr.Column(scale=2):
-                        enh_html = gr.HTML("<div style='text-align:center;padding:60px;color:#999;direction:rtl;'><div style='font-size:48px;'>🧠</div><div>ارفع صورة سونار للتحسين بالذكاء الاصطناعي</div><div style='font-size:12px;margin-top:8px;'>6 تقنيات — GPU accelerated</div></div>")
-                gr.HTML("<div style='background:#1565C0;padding:8px;border-radius:8px;text-align:center;color:white;font-weight:bold;margin:8px 0;'>📊 مقارنة 6 تقنيات</div>")
-                with gr.Row():
-                    enh_clahe = gr.Image(label="🔆 CLAHE Advanced", type="pil", height=220)
-                    enh_histeq = gr.Image(label="📊 Histogram EQ", type="pil", height=220)
-                    enh_sharpen = gr.Image(label="⚡ Sharpen + Denoise", type="pil", height=220)
-                with gr.Row():
-                    enh_combined = gr.Image(label="🏆 Combined Best", type="pil", height=220)
-                    enh_esrgan = gr.Image(label="🔬 Real-ESRGAN x2", type="pil", height=220)
-                    enh_neural = gr.Image(label="🌊 GPU Neural", type="pil", height=220)
-                gr.HTML("<div style='background:#2E7D32;padding:8px;border-radius:8px;text-align:center;color:white;font-weight:bold;margin:8px 0;'>🔄 مقارنة قبل / بعد</div>")
-                with gr.Row():
-                    enh_before = gr.Image(label="📷 قبل (الأصلية)", type="pil", height=300)
-                    enh_after = gr.Image(label="🏆 بعد (أفضل نتيجة)", type="pil", height=300)
-                def _run_all_slider(img):
-                    html, clahe, histeq, sharpen, combined, esrgan, neural = ai_enhance_all(img)
-                    best = esrgan if esrgan is not None else (combined if combined is not None else img)
-                    return html, clahe, histeq, sharpen, combined, esrgan, neural, img, best
-                enh_btn_all.click(_run_all_slider, inputs=[enh_img],
-                    outputs=[enh_html, enh_clahe, enh_histeq, enh_sharpen, enh_combined, enh_esrgan, enh_neural, enh_before, enh_after])
-                enh_btn_single.click(ai_enhance_single, inputs=[enh_img, enh_technique], outputs=[enh_single_result, enh_single_status])
+
+                with gr.Tabs():
+                    # ═══ قسم 1: تصنيف سريع YOLO11x ═══
+                    with gr.Tab("⚡ تصنيف سريع"):
+                        gr.HTML("<div style='background:#FF6F00;padding:8px;border-radius:8px;text-align:center;color:white;font-weight:bold;margin-bottom:8px;'>⚡ YOLO11x-cls — تصنيف بنموذج واحد (3-8 ثواني)</div>")
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                y_img = gr.Image(label="📷 صورة السونار", type="pil", height=300)
+                                y_dec = gr.Textbox(label="📝 البضاعة المصرّحة", placeholder="مثال: snacks أو rice+sugar", lines=1)
+                                y_btn = gr.Button("⚡ تصنيف سريع YOLO11x", variant="primary", size="lg")
+                            with gr.Column(scale=2):
+                                y_res = gr.HTML("")
+                        y_tbl = gr.Dataframe(label="📊 النتائج", value=pd.DataFrame(), wrap=True)
+                        y_det = gr.Image(label="🎯 Detection", type="pil", visible=False)
+                        y_btn.click(yolo11_fast_classify, inputs=[y_img, y_dec], outputs=[y_res, y_tbl, y_det], api_name="analyze_fast")
+
+                    # ═══ قسم 2: تحسين صور ═══
+                    with gr.Tab("🔬 تحسين صور"):
+                        gr.HTML("<div style='background:#1a237e;padding:8px;border-radius:8px;text-align:center;color:white;font-weight:bold;margin-bottom:8px;'>🔬 6 تقنيات تحسين — CLAHE · HistEQ · Sharpen · Combined · Neural · Super Res</div>")
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                enh_img = gr.Image(label="📷 الصورة الأصلية", type="pil", height=250)
+                                gr.HTML("""<div style='background:#F3E5F5;padding:10px;border-radius:8px;border:1px solid #CE93D8;font-size:11px;margin:6px 0;'>
+                                    🔆 CLAHE — تباين محلي (0.1s)<br>📊 HistEQ — توزيع كثافات (0.05s)<br>
+                                    ⚡ Sharpen — حدة + إزالة ضوضاء (0.3s)<br>🏆 Combined — أفضل نتيجة (0.4s)<br>
+                                    🌊 Neural — تحسين عصبي GPU (1-3s)<br>🔬 Super Res — تكبير x2 (2-5s)
+                                </div>""")
+                                enh_btn_all = gr.Button("🧠 تحسين بكل التقنيات", variant="primary", size="lg")
+                                gr.HTML("<div style='text-align:center;color:#666;font-size:12px;margin:6px 0;'>— أو اختر واحدة —</div>")
+                                enh_tech = gr.Radio(choices=["🔆 CLAHE", "📊 Histogram EQ", "⚡ Sharpen + Denoise",
+                                    "🏆 Combined Best", "🌊 GPU Neural", "🔬 Super Resolution x2"],
+                                    value="🏆 Combined Best", label="التقنية")
+                                enh_btn_one = gr.Button("⚡ تحسين سريع", variant="secondary")
+                                enh_one_res = gr.Image(label="النتيجة", type="pil", height=200)
+                                enh_one_st = gr.HTML("")
+                            with gr.Column(scale=2):
+                                enh_html = gr.HTML("<div style='text-align:center;padding:60px;color:#999;direction:rtl;'><div style='font-size:48px;'>🧠</div><div>ارفع صورة للتحسين</div></div>")
+
+                        gr.HTML("<div style='background:#1565C0;padding:8px;border-radius:8px;text-align:center;color:white;font-weight:bold;margin:8px 0;'>📊 مقارنة 6 تقنيات</div>")
+                        with gr.Row():
+                            enh_r1 = gr.Image(label="🔆 CLAHE", type="pil", height=220)
+                            enh_r2 = gr.Image(label="📊 HistEQ", type="pil", height=220)
+                            enh_r3 = gr.Image(label="⚡ Sharpen", type="pil", height=220)
+                        with gr.Row():
+                            enh_r4 = gr.Image(label="🏆 Combined", type="pil", height=220)
+                            enh_r5 = gr.Image(label="🌊 Neural", type="pil", height=220)
+                            enh_r6 = gr.Image(label="🔬 Super Res x2", type="pil", height=220)
+
+                        gr.HTML("<div style='background:#2E7D32;padding:8px;border-radius:8px;text-align:center;color:white;font-weight:bold;margin:8px 0;'>🔄 قبل / بعد</div>")
+                        with gr.Row():
+                            enh_before = gr.Image(label="📷 قبل", type="pil", height=300)
+                            enh_after = gr.Image(label="🏆 بعد", type="pil", height=300)
+
+                        def _run_all(img):
+                            html, r1, r2, r3, r4, r5, r6 = enhance_all_independent(img)
+                            best = r4 if r4 is not None else (r5 if r5 is not None else img)
+                            return html, r1, r2, r3, r4, r5, r6, img, best
+
+                        enh_btn_all.click(_run_all, inputs=[enh_img],
+                            outputs=[enh_html, enh_r1, enh_r2, enh_r3, enh_r4, enh_r5, enh_r6, enh_before, enh_after])
+                        enh_btn_one.click(enhance_single_independent, inputs=[enh_img, enh_tech], outputs=[enh_one_res, enh_one_st])
 
             with gr.Tab("🏷️ قاعدة الأصناف"):
                 gr.HTML(f"<div style='background:#E8F5E9;padding:10px 14px;border-radius:10px;margin-bottom:8px;border-right:4px solid #2E7D32;'><b style='color:#2E7D32;'>📦 {len(CARGO_DATABASE)} صنف</b></div>")