Update app.py

app.py

@@ -1,594 +1,596 @@
 # app.py - InterFuser Self-Driving API Server
 
+import uuid
+import base64
+import cv2
+# import torch
+# import numpy as np
+# from fastapi import FastAPI, HTTPException
+# from fastapi.responses import HTMLResponse
+# from pydantic import BaseModel
+# from torchvision import transforms
+# from typing import List, Dict, Any, Optional
+# import logging
+
+# # استيراد من ملفاتنا المحلية
+# from model_definition import InterfuserModel, load_and_prepare_model, create_model_config
+# from simulation_modules import (
+#     InterfuserController, ControllerConfig, Tracker, DisplayInterface, 
+#     render, render_waypoints, render_self_car, WAYPOINT_SCALE_FACTOR, 
+#     T1_FUTURE_TIME, T2_FUTURE_TIME
+# )
+
+# # إعداد التسجيل
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+
+# # ================== إعدادات عامة وتحميل النموذج ==================
+# app = FastAPI(
+#     title="Baseer Self-Driving API",
+#     description="API للقيادة الذاتية باستخدام نموذج InterFuser",
+#     version="1.0.0"
+# )
+
+# device = torch.device("cpu")
+# logger.info(f"Using device: {device}")
+
+# # تحميل النموذج باستخدام الدالة المحسنة
+# try:
+#     # إنشاء إعدادات النموذج باستخدام الإعدادات الصحيحة من التدريب
+#     model_config = create_model_config(
+#         model_path="model/best_model.pth"
+#         # الإعدادات الصحيحة من التدريب ستطبق تلقائياً:
+#         # embed_dim=256, rgb_backbone_name='r50', waypoints_pred_head='gru'
+#         # with_lidar=False, with_right_left_sensors=False, with_center_sensor=False
+#     )
+    
+#     # تحميل النموذج مع الأوزان
+#     model = load_and_prepare_model(model_config, device)
+#     logger.info("✅ تم تحميل النموذج بنجاح")
+    
+# except Exception as e:
+#     logger.error(f"❌ خطأ في تحميل النموذج: {e}")
+#     logger.info("🔄 محاولة إنشاء نموذج بأوزان عشوائية...")
+#     try:
+#         model = InterfuserModel()
+#         model.to(device)
+#         model.eval()
+#         logger.warning("⚠️ تم إنشاء النموذج بأوزان عشوائية")
+#     except Exception as e2:
+#         logger.error(f"❌ فشل في إنشاء النموذج: {e2}")
+#         model = None
+
+# # تهيئة واجهة العرض
+# display = DisplayInterface()
+
+# # قاموس لتخزين جلسات المستخدمين
+# SESSIONS: Dict[str, Dict] = {}
+
+# # ================== هياكل بيانات Pydantic ==================
+# class Measurements(BaseModel):
+#     pos: List[float] = [0.0, 0.0]  # [x, y] position
+#     theta: float = 0.0              # orientation angle
+#     speed: float = 0.0              # current speed
+#     steer: float = 0.0              # current steering
+#     throttle: float = 0.0           # current throttle
+#     brake: bool = False             # brake status
+#     command: int = 4                # driving command (4 = FollowLane)
+#     target_point: List[float] = [0.0, 0.0]  # target point [x, y]
+
+# class ModelOutputs(BaseModel):
+#     traffic: List[List[List[float]]]  # 20x20x7 grid
+#     waypoints: List[List[float]]      # Nx2 waypoints
+#     is_junction: float
+#     traffic_light_state: float
+#     stop_sign: float
+
+# class ControlCommands(BaseModel):
+#     steer: float
+#     throttle: float
+#     brake: bool
+
+# class RunStepInput(BaseModel):
+#     session_id: str
+#     image_b64: str
+#     measurements: Measurements
+
+# class RunStepOutput(BaseModel):
+#     model_outputs: ModelOutputs
+#     control_commands: ControlCommands
+#     dashboard_image_b64: str
+
+# class SessionResponse(BaseModel):
+#     session_id: str
+#     message: str
+
+# # ================== دوال المساعدة ==================
+# def get_image_transform():
+#     """إنشاء تحويلات الصورة كما في PDMDataset"""
+#     return transforms.Compose([
+#         transforms.ToTensor(),
+#         transforms.Resize((224, 224), antialias=True),
+#         transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+#     ])
+
+# # إنشاء كائن التحويل مرة واحدة
+# image_transform = get_image_transform()
+
+# def preprocess_input(frame_rgb: np.ndarray, measurements: Measurements, device: torch.device) -> Dict[str, torch.Tensor]:
+#     """
+#     تحاكي ما يفعله PDMDataset.__getitem__ لإنشاء دفعة (batch) واحدة.
+#     """
+#     # 1. معالجة الصورة الرئيسية
+#     from PIL import Image
+#     if isinstance(frame_rgb, np.ndarray):
+#         frame_rgb = Image.fromarray(frame_rgb)
+    
+#     image_tensor = image_transform(frame_rgb).unsqueeze(0).to(device)  # إضافة بُعد الدفعة
+
+#     # 2. إنشاء مدخلات الكاميرات الأخرى عن طريق الاستنساخ
+#     batch = {
+#         'rgb': image_tensor,
+#         'rgb_left': image_tensor.clone(),
+#         'rgb_right': image_tensor.clone(),
+#         'rgb_center': image_tensor.clone(),
+#     }
+
+#     # 3. إنشاء مدخل ليدار وهمي (أصفار)
+#     batch['lidar'] = torch.zeros(1, 3, 224, 224, dtype=torch.float32).to(device)
+
+#     # 4. تجميع القياسات بنفس ترتيب PDMDataset
+#     m = measurements
+#     measurements_tensor = torch.tensor([[
+#         m.pos[0], m.pos[1], m.theta,
+#         m.steer, m.throttle, float(m.brake),
+#         m.speed, float(m.command)
+#     ]], dtype=torch.float32).to(device)
+#     batch['measurements'] = measurements_tensor
+
+#     # 5. إنشاء نقطة هدف
+#     batch['target_point'] = torch.tensor([m.target_point], dtype=torch.float32).to(device)
+    
+#     # لا نحتاج إلى قيم ground truth (gt_*) أثناء التنبؤ
+#     return batch
+
+# def decode_base64_image(image_b64: str) -> np.ndarray:
+#     """
+#     فك تشفير صورة Base64
+#     """
+#     try:
+#         image_bytes = base64.b64decode(image_b64)
+#         nparr = np.frombuffer(image_bytes, np.uint8)
+#         image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+#         return image
+#     except Exception as e:
+#         raise HTTPException(status_code=400, detail=f"Invalid image format: {str(e)}")
+
+# def encode_image_to_base64(image: np.ndarray) -> str:
+#     """
+#     تشفير صورة إلى Base64
+#     """
+#     _, buffer = cv2.imencode('.jpg', image, [cv2.IMWRITE_JPEG_QUALITY, 85])
+#     return base64.b64encode(buffer).decode('utf-8')
+
+# # ================== نقاط نهاية الـ API ==================
+# @app.get("/", response_class=HTMLResponse)
+# async def root():
+#     """
+#     الصفحة الرئيسية للـ API
+#     """
+#     html_content = f"""
+#     <!DOCTYPE html>
+#     <html dir="rtl" lang="ar">
+#     <head>
+#         <meta charset="UTF-8">
+#         <meta name="viewport" content="width=device-width, initial-scale=1.0">
+#         <title>🚗 Baseer Self-Driving API</title>
+#         <style>
+#             * {{
+#                 margin: 0;
+#                 padding: 0;
+#                 box-sizing: border-box;
+#             }}
+#             body {{
+#                 font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+#                 background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+#                 min-height: 100vh;
+#                 display: flex;
+#                 align-items: center;
+#                 justify-content: center;
+#                 padding: 20px;
+#             }}
+#             .container {{
+#                 background: rgba(255, 255, 255, 0.95);
+#                 backdrop-filter: blur(10px);
+#                 border-radius: 20px;
+#                 padding: 40px;
+#                 box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
+#                 text-align: center;
+#                 max-width: 600px;
+#                 width: 100%;
+#             }}
+#             .logo {{
+#                 font-size: 4rem;
+#                 margin-bottom: 20px;
+#                 animation: bounce 2s infinite;
+#             }}
+#             @keyframes bounce {{
+#                 0%, 20%, 50%, 80%, 100% {{ transform: translateY(0); }}
+#                 40% {{ transform: translateY(-10px); }}
+#                 60% {{ transform: translateY(-5px); }}
+#             }}
+#             h1 {{
+#                 color: #333;
+#                 margin-bottom: 10px;
+#                 font-size: 2.5rem;
+#             }}
+#             .subtitle {{
+#                 color: #666;
+#                 margin-bottom: 30px;
+#                 font-size: 1.2rem;
+#             }}
+#             .status {{
+#                 display: inline-block;
+#                 background: #4CAF50;
+#                 color: white;
+#                 padding: 8px 16px;
+#                 border-radius: 20px;
+#                 margin: 10px 0;
+#                 font-weight: bold;
+#             }}
+#             .stats {{
+#                 display: grid;
+#                 grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+#                 gap: 20px;
+#                 margin: 30px 0;
+#             }}
+#             .stat-card {{
+#                 background: #f8f9fa;
+#                 padding: 20px;
+#                 border-radius: 15px;
+#                 border-left: 4px solid #667eea;
+#             }}
+#             .stat-number {{
+#                 font-size: 2rem;
+#                 font-weight: bold;
+#                 color: #667eea;
+#             }}
+#             .stat-label {{
+#                 color: #666;
+#                 margin-top: 5px;
+#             }}
+#             .buttons {{
+#                 display: flex;
+#                 gap: 15px;
+#                 justify-content: center;
+#                 flex-wrap: wrap;
+#                 margin-top: 30px;
+#             }}
+#             .btn {{
+#                 display: inline-block;
+#                 padding: 12px 24px;
+#                 border-radius: 25px;
+#                 text-decoration: none;
+#                 font-weight: bold;
+#                 transition: all 0.3s ease;
+#                 border: none;
+#                 cursor: pointer;
+#             }}
+#             .btn-primary {{
+#                 background: #667eea;
+#                 color: white;
+#             }}
+#             .btn-secondary {{
+#                 background: #6c757d;
+#                 color: white;
+#             }}
+#             .btn:hover {{
+#                 transform: translateY(-2px);
+#                 box-shadow: 0 5px 15px rgba(0, 0, 0, 0.2);
+#             }}
+#             .features {{
+#                 text-align: right;
+#                 margin-top: 30px;
+#                 padding: 20px;
+#                 background: #f8f9fa;
+#                 border-radius: 15px;
+#             }}
+#             .features h3 {{
+#                 color: #333;
+#                 margin-bottom: 15px;
+#             }}
+#             .features ul {{
+#                 list-style: none;
+#                 padding: 0;
+#             }}
+#             .features li {{
+#                 padding: 5px 0;
+#                 color: #666;
+#             }}
+#             .features li:before {{
+#                 content: "✅ ";
+#                 margin-left: 10px;
+#             }}
+#         </style>
+#     </head>
+#     <body>
+#         <div class="container">
+#             <div class="logo">🚗</div>
+#             <h1>Baseer Self-Driving API</h1>
+#             <p class="subtitle">نظام القيادة الذاتية المتقدم</p>
+            
+#             <div class="status">🟢 يعمل بنجاح</div>
+            
+#             <div class="stats">
+#                 <div class="stat-card">
+#                     <div class="stat-number">{len(SESSIONS)}</div>
+#                     <div class="stat-label">الجلسات النشطة</div>
+#                 </div>
+#                 <div class="stat-card">
+#                     <div class="stat-number">v1.0</div>
+#                     <div class="stat-label">الإصدار</div>
+#                 </div>
+#                 <div class="stat-card">
+#                     <div class="stat-number">FastAPI</div>
+#                     <div class="stat-label">التقنية</div>
+#                 </div>
+#             </div>
+            
+#             <div class="buttons">
+#                 <a href="/docs" class="btn btn-primary">📚 توثيق API</a>
+#                 <a href="/sessions" class="btn btn-secondary">📊 الجلسات</a>
+#             </div>
+            
+#             <div class="features">
+#                 <h3>🌟 الميزات الرئيسية</h3>
+#                 <ul>
+#                     <li>نموذج InterFuser للقيادة الذاتية</li>
+#                     <li>معالجة الصور في الوقت الفعلي</li>
+#                     <li>اكتشاف الكائنات المرورية</li>
+#                     <li>تحديد المسارات الذكية</li>
+#                     <li>واجهة RESTful سهلة الاستخدام</li>
+#                     <li>إدارة جلسات متعددة</li>
+#                 </ul>
+#             </div>
+#         </div>
+#     </body>
+#     </html>
+#     """
+#     return html_content
+
 import uuid
 import base64
 import cv2
 import torch
 import numpy as np
+import logging
 from fastapi import FastAPI, HTTPException
 from fastapi.responses import HTMLResponse
-from pydantic import BaseModel
-from torchvision import transforms
-from typing import List, Dict, Any, Optional
-import logging
+from pydantic import BaseModel, Field
+from typing import List, Dict, Tuple
 
-# استيراد من ملفاتنا المحلية
+# ==============================================================================
+# 1. استيراد كل مكونات المشروع التي قمنا بتطويرها
+#    (تأكد من أن هذه الملفات موجودة في نفس المجلد)
+# ==============================================================================
+# من ملف النموذج (يحتوي على كلاس Interfuser والدوال المساعدة)
 from model_definition import InterfuserModel, load_and_prepare_model, create_model_config
-from simulation_modules import (
-    InterfuserController, ControllerConfig, Tracker, DisplayInterface, 
-    render, render_waypoints, render_self_car, WAYPOINT_SCALE_FACTOR, 
-    T1_FUTURE_TIME, T2_FUTURE_TIME
-)
 
-# إعداد التسجيل
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+# من ملفات التحكم والعرض
+from simulation_modules import InterfuserController, Tracker
+from simulation_modules import DisplayInterface, render_bev, unnormalize_image, DisplayConfig
+# # استيراد من ملفاتنا المحلية
+# from model_definition import InterfuserModel, load_and_prepare_model, create_model_config
+# from simulation_modules import (
+#     InterfuserController, ControllerConfig, Tracker, DisplayInterface, 
+#     render, render_waypoints, render_self_car, WAYPOINT_SCALE_FACTOR, 
+#     T1_FUTURE_TIME, T2_FUTURE_TIME
+# )
+# ==============================================================================
+# 2. إعدادات عامة وتطبيق FastAPI
+# ==============================================================================
+# إعداد التسجيل (Logging)
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-# ================== إعدادات عامة وتحميل النموذج ==================
+# تهيئة تطبيق FastAPI
 app = FastAPI(
     title="Baseer Self-Driving API",
-    description="API للقيادة الذاتية باستخدام نموذج InterFuser",
-    version="1.0.0"
+    description="An advanced API for the InterFuser self-driving model, providing real-time control commands and scene analysis.",
+    version="1.1.0"
 )
 
-device = torch.device("cpu")
-logger.info(f"Using device: {device}")
-
-# تحميل النموذج باستخدام الدالة المحسنة
-try:
-    # إنشاء إعدادات النموذج باستخدام الإعدادات الصحيحة من التدريب
-    model_config = create_model_config(
-        model_path="model/best_model.pth"
-        # الإعدادات الصحيحة من التدريب ستطبق تلقائياً:
-        # embed_dim=256, rgb_backbone_name='r50', waypoints_pred_head='gru'
-        # with_lidar=False, with_right_left_sensors=False, with_center_sensor=False
-    )
-    
-    # تحميل النموذج مع الأوزان
-    model = load_and_prepare_model(model_config, device)
-    logger.info("✅ تم تحميل النموذج بنجاح")
-    
-except Exception as e:
-    logger.error(f"❌ خطأ في تحميل النموذج: {e}")
-    logger.info("🔄 محاولة إنشاء نموذج بأوزان عشوائية...")
-    try:
-        model = InterfuserModel()
-        model.to(device)
-        model.eval()
-        logger.warning("⚠️ تم إنشاء النموذج بأوزان عشوائية")
-    except Exception as e2:
-        logger.error(f"❌ فشل في إنشاء النموذج: {e2}")
-        model = None
-
-# تهيئة واجهة العرض
-display = DisplayInterface()
-
-# قاموس لتخزين جلسات المستخدمين
-SESSIONS: Dict[str, Dict] = {}
+# متغيرات عامة سيتم تهيئتها عند بدء التشغيل
+MODEL: Interfuser = None
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+SESSIONS: Dict[str, Dict] = {} # قاموس لتخزين حالة الجلسات النشطة
 
-# ================== هياكل بيانات Pydantic ==================
+# ==============================================================================
+# 3. تعريف نماذج البيانات (Pydantic Models) للـ API
+# ==============================================================================
 class Measurements(BaseModel):
-    pos: List[float] = [0.0, 0.0]  # [x, y] position
-    theta: float = 0.0              # orientation angle
-    speed: float = 0.0              # current speed
-    steer: float = 0.0              # current steering
-    throttle: float = 0.0           # current throttle
-    brake: bool = False             # brake status
-    command: int = 4                # driving command (4 = FollowLane)
-    target_point: List[float] = [0.0, 0.0]  # target point [x, y]
-
-class ModelOutputs(BaseModel):
-    traffic: List[List[List[float]]]  # 20x20x7 grid
-    waypoints: List[List[float]]      # Nx2 waypoints
-    is_junction: float
-    traffic_light_state: float
-    stop_sign: float
+    pos_global: Tuple[float, float] = Field(..., example=(0.0, 0.0), description="Global [X, Y] position of the vehicle.")
+    theta: float = Field(..., example=0.0, description="Global orientation angle of the vehicle in radians.")
+    speed: float = Field(..., example=0.0, description="Current speed in m/s.")
+    target_point: Tuple[float, float] = Field(..., example=(10.0, 0.0), description="Target point relative to the vehicle.")
+
+class RunStepRequest(BaseModel):
+    session_id: str
+    image_b64: str = Field(..., description="Base64 encoded string of the vehicle's front camera view (BGR format).")
+    measurements: Measurements
 
 class ControlCommands(BaseModel):
     steer: float
     throttle: float
     brake: bool
 
-class RunStepInput(BaseModel):
-    session_id: str
-    image_b64: str
-    measurements: Measurements
+class SceneAnalysis(BaseModel):
+    is_junction: float
+    traffic_light_state: float
+    stop_sign: float
 
-class RunStepOutput(BaseModel):
-    model_outputs: ModelOutputs
+class RunStepResponse(BaseModel):
     control_commands: ControlCommands
-    dashboard_image_b64: str
+    scene_analysis: SceneAnalysis
+    predicted_waypoints: List[Tuple[float, float]]
+    dashboard_b64: str = Field(..., description="Base64 encoded string of the comprehensive dashboard view.")
+    reason: str = Field(..., description="The reason for the current control action (e.g., 'Following ID 12', 'Red Light').")
 
-class SessionResponse(BaseModel):
-    session_id: str
-    message: str
+# ==============================================================================
+# 4. دوال مساعدة (Helpers)
+# ==============================================================================
+def b64_to_cv2(b64_string: str) -> np.ndarray:
+    try:
+        img_bytes = base64.b64decode(b64_string)
+        img_array = np.frombuffer(img_bytes, dtype=np.uint8)
+        return cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+    except Exception:
+        raise HTTPException(status_code=400, detail="Invalid Base64 image string.")
+
+def cv2_to_b64(img: np.ndarray) -> str:
+    _, buffer = cv2.imencode('.jpg', img)
+    return base64.b64encode(buffer).decode('utf-8')
 
-# ================== دوال المساعدة ==================
-def get_image_transform():
-    """إنشاء تحويلات الصورة كما في PDMDataset"""
-    return transforms.Compose([
+def prepare_model_input(image: np.ndarray, measurements: Measurements) -> Dict[str, torch.Tensor]:
+    """
+    إعداد دفعة (batch of 1) لتمريرها إلى النموذج.
+    """
+    transform = transforms.Compose([
         transforms.ToTensor(),
         transforms.Resize((224, 224), antialias=True),
         transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     ])
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image_tensor = transform(image_rgb).unsqueeze(0).to(DEVICE)
 
-# إنشاء كائن التحويل مرة واحدة
-image_transform = get_image_transform()
-
-def preprocess_input(frame_rgb: np.ndarray, measurements: Measurements, device: torch.device) -> Dict[str, torch.Tensor]:
-    """
-    تحاكي ما يفعله PDMDataset.__getitem__ لإنشاء دفعة (batch) واحدة.
-    """
-    # 1. معالجة الصورة الرئيسية
-    from PIL import Image
-    if isinstance(frame_rgb, np.ndarray):
-        frame_rgb = Image.fromarray(frame_rgb)
+    measurements_tensor = torch.tensor([[
+        measurements.pos_global[0], measurements.pos_global[1], measurements.theta,
+        0.0, 0.0, 0.0, # Steer, throttle, brake (not used by model)
+        measurements.speed, 4.0 # Command (default to FollowLane)
+    ]], dtype=torch.float32).to(DEVICE)
     
-    image_tensor = image_transform(frame_rgb).unsqueeze(0).to(device)  # إضافة بُعد الدفعة
+    target_point_tensor = torch.tensor([measurements.target_point], dtype=torch.float32).to(DEVICE)
 
-    # 2. إنشاء مدخلات الكاميرات الأخرى عن طريق الاستنساخ
-    batch = {
+    return {
         'rgb': image_tensor,
-        'rgb_left': image_tensor.clone(),
-        'rgb_right': image_tensor.clone(),
-        'rgb_center': image_tensor.clone(),
+        'rgb_left': image_tensor.clone(), 'rgb_right': image_tensor.clone(), 'rgb_center': image_tensor.clone(),
+        'measurements': measurements_tensor,
+        'target_point': target_point_tensor,
+        'lidar': torch.zeros_like(image_tensor)
     }
 
-    # 3. إنشاء مدخل ليدار وهمي (أصفار)
-    batch['lidar'] = torch.zeros(1, 3, 224, 224, dtype=torch.float32).to(device)
-
-    # 4. تجميع القياسات بنفس ترتيب PDMDataset
-    m = measurements
-    measurements_tensor = torch.tensor([[
-        m.pos[0], m.pos[1], m.theta,
-        m.steer, m.throttle, float(m.brake),
-        m.speed, float(m.command)
-    ]], dtype=torch.float32).to(device)
-    batch['measurements'] = measurements_tensor
-
-    # 5. إنشاء نقطة هدف
-    batch['target_point'] = torch.tensor([m.target_point], dtype=torch.float32).to(device)
-    
-    # لا نحتاج إلى قيم ground truth (gt_*) أثناء التنبؤ
-    return batch
-
-def decode_base64_image(image_b64: str) -> np.ndarray:
-    """
-    فك تشفير صورة Base64
-    """
-    try:
-        image_bytes = base64.b64decode(image_b64)
-        nparr = np.frombuffer(image_bytes, np.uint8)
-        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-        return image
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Invalid image format: {str(e)}")
-
-def encode_image_to_base64(image: np.ndarray) -> str:
-    """
-    تشفير صورة إلى Base64
-    """
-    _, buffer = cv2.imencode('.jpg', image, [cv2.IMWRITE_JPEG_QUALITY, 85])
-    return base64.b64encode(buffer).decode('utf-8')
+# ==============================================================================
+# 5. أحداث دورة حياة التطبيق (Startup/Shutdown)
+# ==============================================================================
+@app.on_event("startup")
+async def startup_event():
+    global MODEL
+    logging.info("🚗 Server starting up...")
+    logging.info(f"Using device: {DEVICE}")
+    MODEL = load_and_prepare_model(DEVICE)
+    if MODEL:
+        logging.info("✅ Model loaded successfully. Server is ready!")
+    else:
+        logging.error("❌ CRITICAL: Model could not be loaded. The API will not function correctly.")
 
-# ================== نقاط نهاية الـ API ==================
-@app.get("/", response_class=HTMLResponse)
+# ==============================================================================
+# 6. نقاط النهاية الرئيسية (API Endpoints)
+# ==============================================================================
+@app.get("/", response_class=HTMLResponse, include_in_schema=False)
 async def root():
-    """
-    الصفحة الرئيسية للـ API
-    """
-    html_content = f"""
-    <!DOCTYPE html>
-    <html dir="rtl" lang="ar">
-    <head>
-        <meta charset="UTF-8">
-        <meta name="viewport" content="width=device-width, initial-scale=1.0">
-        <title>🚗 Baseer Self-Driving API</title>
-        <style>
-            * {{
-                margin: 0;
-                padding: 0;
-                box-sizing: border-box;
-            }}
-            body {{
-                font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-                min-height: 100vh;
-                display: flex;
-                align-items: center;
-                justify-content: center;
-                padding: 20px;
-            }}
-            .container {{
-                background: rgba(255, 255, 255, 0.95);
-                backdrop-filter: blur(10px);
-                border-radius: 20px;
-                padding: 40px;
-                box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
-                text-align: center;
-                max-width: 600px;
-                width: 100%;
-            }}
-            .logo {{
-                font-size: 4rem;
-                margin-bottom: 20px;
-                animation: bounce 2s infinite;
-            }}
-            @keyframes bounce {{
-                0%, 20%, 50%, 80%, 100% {{ transform: translateY(0); }}
-                40% {{ transform: translateY(-10px); }}
-                60% {{ transform: translateY(-5px); }}
-            }}
-            h1 {{
-                color: #333;
-                margin-bottom: 10px;
-                font-size: 2.5rem;
-            }}
-            .subtitle {{
-                color: #666;
-                margin-bottom: 30px;
-                font-size: 1.2rem;
-            }}
-            .status {{
-                display: inline-block;
-                background: #4CAF50;
-                color: white;
-                padding: 8px 16px;
-                border-radius: 20px;
-                margin: 10px 0;
-                font-weight: bold;
-            }}
-            .stats {{
-                display: grid;
-                grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
-                gap: 20px;
-                margin: 30px 0;
-            }}
-            .stat-card {{
-                background: #f8f9fa;
-                padding: 20px;
-                border-radius: 15px;
-                border-left: 4px solid #667eea;
-            }}
-            .stat-number {{
-                font-size: 2rem;
-                font-weight: bold;
-                color: #667eea;
-            }}
-            .stat-label {{
-                color: #666;
-                margin-top: 5px;
-            }}
-            .buttons {{
-                display: flex;
-                gap: 15px;
-                justify-content: center;
-                flex-wrap: wrap;
-                margin-top: 30px;
-            }}
-            .btn {{
-                display: inline-block;
-                padding: 12px 24px;
-                border-radius: 25px;
-                text-decoration: none;
-                font-weight: bold;
-                transition: all 0.3s ease;
-                border: none;
-                cursor: pointer;
-            }}
-            .btn-primary {{
-                background: #667eea;
-                color: white;
-            }}
-            .btn-secondary {{
-                background: #6c757d;
-                color: white;
-            }}
-            .btn:hover {{
-                transform: translateY(-2px);
-                box-shadow: 0 5px 15px rgba(0, 0, 0, 0.2);
-            }}
-            .features {{
-                text-align: right;
-                margin-top: 30px;
-                padding: 20px;
-                background: #f8f9fa;
-                border-radius: 15px;
-            }}
-            .features h3 {{
-                color: #333;
-                margin-bottom: 15px;
-            }}
-            .features ul {{
-                list-style: none;
-                padding: 0;
-            }}
-            .features li {{
-                padding: 5px 0;
-                color: #666;
-            }}
-            .features li:before {{
-                content: "✅ ";
-                margin-left: 10px;
-            }}
-        </style>
-    </head>
-    <body>
-        <div class="container">
-            <div class="logo">🚗</div>
-            <h1>Baseer Self-Driving API</h1>
-            <p class="subtitle">نظام القيادة الذاتية المتقدم</p>
-            
-            <div class="status">🟢 يعمل بنجاح</div>
-            
-            <div class="stats">
-                <div class="stat-card">
-                    <div class="stat-number">{len(SESSIONS)}</div>
-                    <div class="stat-label">الجلسات النشطة</div>
-                </div>
-                <div class="stat-card">
-                    <div class="stat-number">v1.0</div>
-                    <div class="stat-label">الإصدار</div>
-                </div>
-                <div class="stat-card">
-                    <div class="stat-number">FastAPI</div>
-                    <div class="stat-label">التقنية</div>
-                </div>
-            </div>
-            
-            <div class="buttons">
-                <a href="/docs" class="btn btn-primary">📚 توثيق API</a>
-                <a href="/sessions" class="btn btn-secondary">📊 الجلسات</a>
-            </div>
-            
-            <div class="features">
-                <h3>🌟 الميزات الرئيسية</h3>
-                <ul>
-                    <li>نموذج InterFuser للقيادة الذاتية</li>
-                    <li>معالجة الصور في الوقت الفعلي</li>
-                    <li>اكتشاف الكائنات المرورية</li>
-                    <li>تحديد المسارات الذكية</li>
-                    <li>واجهة RESTful سهلة الاستخدام</li>
-                    <li>إدارة جلسات متعددة</li>
-                </ul>
-            </div>
-        </div>
-    </body>
+    # هذا يعرض صفحة رئيسية بسيطة وجميلة للمستخدمين
+    return """
+    <html>
+        <head><title>Baseer API</title></head>
+        <body style='font-family: sans-serif; text-align: center; padding-top: 50px;'>
+            <h1>🚗 Baseer Self-Driving API</h1>
+            <p>Welcome! The API is running.</p>
+            <p>Navigate to <a href="/docs">/docs</a> for the interactive API documentation.</p>
+        </body>
     </html>
     """
-    return html_content
 
-@app.post("/start_session", response_model=SessionResponse)
-async def start_session():
-    """
-    بدء جلسة جديدة للمحاكاة
-    """
+@app.post("/start_session", summary="Start a new driving session", tags=["Session Management"])
+def start_session():
     session_id = str(uuid.uuid4())
+    config = create_model_config()
+    controller_params = config.get('controller_params', {})
+    controller_params.update({'frequency': 10.0}) # Set default frequency
     
-    # إنشاء جلسة جديدة
     SESSIONS[session_id] = {
-        'tracker': Tracker(frequency=10),
-        'controller': InterfuserController(ControllerConfig()),
-        'frame_num': 0,
-        'created_at': np.datetime64('now'),
-        'last_activity': np.datetime64('now')
+        'tracker': Tracker(grid_conf=config['grid_conf']),
+        'controller': InterfuserController({'controller_params': controller_params, 'grid_conf': config['grid_conf']}),
+        'frame_num': 0
     }
-    
-    logger.info(f"New session created: {session_id}")
-    
-    return SessionResponse(
-        session_id=session_id,
-        message="Session started successfully"
-    )
+    logging.info(f"New session started: {session_id}")
+    return {"session_id": session_id}
 
-@app.post("/run_step", response_model=RunStepOutput)
-async def run_step(data: RunStepInput):
-    """
-    تنفيذ خطوة محاكاة كاملة
-    """
-    # التحقق من وجود الجلسة
-    if data.session_id not in SESSIONS:
-        raise HTTPException(status_code=404, detail="Session not found")
-    
-    session = SESSIONS[data.session_id]
-    tracker = session['tracker']
-    controller = session['controller']
-    
-    # تحديث وقت النشاط
-    session['last_activity'] = np.datetime64('now')
+@app.post("/run_step", response_model=RunStepResponse, summary="Process a single simulation step", tags=["Core"])
+@torch.no_grad()
+def run_step(request: RunStepRequest):
+    if MODEL is None:
+        raise HTTPException(status_code=503, detail="Model is not available.")
     
-    try:
-        # 1. فك تشفير الصورة
-        frame_bgr = decode_base64_image(data.image_b64)
-        frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
-        
-        # 2. معالجة المدخلات
-        inputs = preprocess_input(frame_rgb, data.measurements, device)
-        
-        # 3. تشغيل النموذج
-        if model is None:
-            raise HTTPException(status_code=500, detail="Model not loaded")
-        
-        with torch.no_grad():
-            traffic, waypoints, is_junction, traffic_light, stop_sign, _ = model(inputs)
-        
-        # 4. معالجة مخرجات النموذج
-        traffic_np = traffic.cpu().numpy()[0]  # أخذ أول عنصر من الـ batch
-        waypoints_np = waypoints.cpu().numpy()[0]
-        is_junction_prob = torch.sigmoid(is_junction)[0, 1].item()
-        traffic_light_prob = torch.sigmoid(traffic_light)[0, 0].item()
-        stop_sign_prob = torch.sigmoid(stop_sign)[0, 1].item()
-        
-        # 5. تحديث التتبع
-        # تحويل traffic grid إلى detections للتتبع
-        detections = []
-        h, w, c = traffic_np.shape
-        for y in range(h):
-            for x in range(w):
-                for ch in range(c):
-                    if traffic_np[y, x, ch] > 0.2:  # عتبة الكشف
-                        world_x = (x / w - 0.5) * 64  # تحويل إلى إحداثيات العالم
-                        world_y = (y / h - 0.5) * 64
-                        detections.append({
-                            'position': [world_x, world_y],
-                            'feature': traffic_np[y, x, ch]
-                        })
-        
-        updated_traffic = tracker.update_and_predict(detections, session['frame_num'])
-        
-        # 6. تشغيل المتحكم
-        steer, throttle, brake, metadata = controller.run_step(
-            current_speed=data.measurements.speed,
-            waypoints=waypoints_np,
-            junction=is_junction_prob,
-            traffic_light_state=traffic_light_prob,
-            stop_sign=stop_sign_prob,
-            meta_data={'frame': session['frame_num']}
-        )
-        
-        # 7. إنشاء خرائط العرض
-        surround_t0, counts_t0 = render(updated_traffic, t=0)
-        surround_t1, counts_t1 = render(updated_traffic, t=T1_FUTURE_TIME)
-        surround_t2, counts_t2 = render(updated_traffic, t=T2_FUTURE_TIME)
-        
-        # إضافة المسار المقترح
-        wp_map = render_waypoints(waypoints_np)
-        map_t0 = cv2.add(surround_t0, wp_map)
-        
-        # إضافة السيارة الذاتية
-        map_t0 = render_self_car(map_t0)
-        map_t1 = render_self_car(surround_t1)
-        map_t2 = render_self_car(surround_t2)
-        
-        # 8. إنشاء لوحة العرض النهائية
-        interface_data = {
-            'camera_view': frame_bgr,
-            'map_t0': map_t0,
-            'map_t1': map_t1,
-            'map_t2': map_t2,
-            'text_info': {
-                'Frame': f"Frame: {session['frame_num']}",
-                'Control': f"Steer: {steer:.2f}, Throttle: {throttle:.2f}, Brake: {brake}",
-                'Speed': f"Speed: {data.measurements.speed:.1f} km/h",
-                'Junction': f"Junction: {is_junction_prob:.2f}",
-                'Traffic Light': f"Red Light: {traffic_light_prob:.2f}",
-                'Stop Sign': f"Stop Sign: {stop_sign_prob:.2f}",
-                'Metadata': metadata
-            },
-            'object_counts': {
-                't0': counts_t0,
-                't1': counts_t1,
-                't2': counts_t2
-            }
-        }
-        
-        dashboard_image = display.run_interface(interface_data)
-        dashboard_b64 = encode_image_to_base64(dashboard_image)
-        
-        # 9. تجميع المخرجات النهائية
-        response = RunStepOutput(
-            model_outputs=ModelOutputs(
-                traffic=traffic_np.tolist(),
-                waypoints=waypoints_np.tolist(),
-                is_junction=is_junction_prob,
-                traffic_light_state=traffic_light_prob,
-                stop_sign=stop_sign_prob
-            ),
-            control_commands=ControlCommands(
-                steer=float(steer),
-                throttle=float(throttle),
-                brake=bool(brake)
-            ),
-            dashboard_image_b64=dashboard_b64
-        )
-        
-        # تحديث رقم الإطار
-        session['frame_num'] += 1
-        
-        logger.info(f"Step completed for session {data.session_id}, frame {session['frame_num']}")
-        
-        return response
-        
-    except Exception as e:
-        logger.error(f"Error in run_step: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Processing error: {str(e)}")
-
-@app.post("/end_session", response_model=SessionResponse)
-async def end_session(session_id: str):
-    """
-    إنهاء جلسة المحاكاة
-    """
-    if session_id not in SESSIONS:
-        raise HTTPException(status_code=404, detail="Session not found")
-    
-    # حذف الجلسة
-    del SESSIONS[session_id]
+    session = SESSIONS.get(request.session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail="Session ID not found.")
+
+    # --- 1. الإدراك (Perception) ---
+    image = b64_to_cv2(request.image_b64)
+    model_input = prepare_model_input(image, request.measurements)
+    traffic, waypoints, junc, light, stop, _ = MODEL(model_input)
+
+    # --- 2. معالجة مخرجات النموذج ---
+    traffic_processed = torch.cat([torch.sigmoid(traffic[0][:, 0:1]), traffic[0][:, 1:]], dim=1)
+    traffic_np = traffic_processed.cpu().numpy().reshape(20, 20, -1)
+    waypoints_np = waypoints[0].cpu().numpy()
+    junction_prob = torch.softmax(junc, dim=1)[0, 1].item()
+    light_prob = torch.softmax(light, dim=1)[0, 1].item()
+    stop_prob = torch.softmax(stop, dim=1)[0, 1].item()
     
-    logger.info(f"Session ended: {session_id}")
+    # --- 3. التتبع والتحكم ---
+    ego_pos = np.array(request.measurements.pos_global)
+    ego_theta = request.measurements.theta
+    frame_num = session['frame_num']
     
-    return SessionResponse(
-        session_id=session_id,
-        message="Session ended successfully"
+    active_tracks = session['tracker'].process_frame(traffic_np, ego_pos, ego_theta, frame_num)
+    steer, throttle, brake, ctrl_info = session['controller'].run_step(
+        speed=request.measurements.speed, waypoints=torch.from_numpy(waypoints_np),
+        junction=junction_prob, traffic_light=light_prob, stop_sign=stop_prob,
+        bev_map=traffic_np, ego_pos=ego_pos, ego_theta=ego_theta, frame_num=frame_num
     )
 
-@app.get("/sessions")
-async def list_sessions():
-    """
-    عرض قائمة الجلسات النشطة
-    """
-    active_sessions = []
-    current_time = np.datetime64('now')
-    
-    for session_id, session_data in SESSIONS.items():
-        time_diff = current_time - session_data['last_activity']
-        active_sessions.append({
-            'session_id': session_id,
-            'frame_count': session_data['frame_num'],
-            'created_at': str(session_data['created_at']),
-            'last_activity': str(session_data['last_activity']),
-            'inactive_minutes': float(time_diff / np.timedelta64(1, 'm'))
-        })
-    
-    return {
-        'total_sessions': len(active_sessions),
-        'sessions': active_sessions
+    # --- 4. إنشاء الواجهة المرئية ---
+    display_iface = DisplayInterface(DisplayConfig(width=1280, height=720))
+    bev_maps = render_bev(active_tracks, waypoints_np, ego_pos, ego_theta)
+    display_data = {
+        'camera_view': image, 'map_t0': bev_maps['t0'], 'map_t1': bev_maps['t1'], 'map_t2': bev_maps['t2'],
+        'frame_num': frame_num, 'speed': request.measurements.speed * 3.6,
+        'target_speed': ctrl_info.get('target_speed', 0) * 3.6,
+        'steer': steer, 'throttle': throttle, 'brake': brake,
+        'light_prob': light_prob, 'stop_prob': stop_prob,
+        'object_counts': {'car': len(active_tracks)}
     }
+    dashboard = display_iface.run_interface(display_data)
 
-@app.delete("/sessions/cleanup")
-async def cleanup_inactive_sessions(max_inactive_minutes: int = 30):
-    """
-    تنظيف الجلسات غير النشطة
-    """
-    current_time = np.datetime64('now')
-    cleaned_sessions = []
-    
-    for session_id in list(SESSIONS.keys()):
-        session = SESSIONS[session_id]
-        time_diff = current_time - session['last_activity']
-        inactive_minutes = float(time_diff / np.timedelta64(1, 'm'))
-        
-        if inactive_minutes > max_inactive_minutes:
-            del SESSIONS[session_id]
-            cleaned_sessions.append(session_id)
+    # --- 5. تحديث الجلسة وإرجاع الرد ---
+    session['frame_num'] += 1
     
-    logger.info(f"Cleaned up {len(cleaned_sessions)} inactive sessions")
-    
-    return {
-        'message': f"Cleaned up {len(cleaned_sessions)} inactive sessions",
-        'cleaned_sessions': cleaned_sessions,
-        'remaining_sessions': len(SESSIONS)
-    }
-
-# ================== معالج الأخطاء ==================
-@app.exception_handler(Exception)
-async def global_exception_handler(request, exc):
-    logger.error(f"Global exception: {str(exc)}")
-    return {
-        "error": "Internal server error",
-        "detail": str(exc)
-    }
+    return RunStepResponse(
+        control_commands=ControlCommands(steer=steer, throttle=throttle, brake=brake),
+        scene_analysis=SceneAnalysis(is_junction=junction_prob, traffic_light_state=light_prob, stop_sign=stop_prob),
+        predicted_waypoints=[tuple(wp) for wp in waypoints_np.tolist()],
+        dashboard_b64=cv2_to_b64(dashboard),
+        reason=ctrl_info.get('brake_reason', "Cruising")
+    )
 
+@app.post("/end_session", summary="End and clean up a session", tags=["Session Management"])
+def end_session(session_id: str):
+    if session_id in SESSIONS:
+        del SESSIONS[session_id]
+        logging.info(f"Session ended: {session_id}")
+        return {"message": f"Session {session_id} ended."}
+    raise HTTPException(status_code=404, detail="Session not found.")
 # ================== تشغيل الخادم ==================
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+# if __name__ == "__main__":
+#     import uvicorn
+#     uvicorn.run(app, host="0.0.0.0", port=7860)