Update main.py

main.py

@@ -1,7 +1,5 @@
 import io
 from typing import List, Dict
-
-import torch
 import uvicorn
 import numpy as np
 import uuid
@@ -18,18 +16,9 @@ from src.comparison import DamageComparator
 from src.visualization import DamageVisualizer
 from pathlib import Path
 from concurrent.futures import ThreadPoolExecutor, as_completed
+import torch
 import gc
 
-
-_initialized = False
-def init_globals_once(select_models=2, prefer_onnx=True):
-    global detector, comparator, visualizer, _initialized
-    if _initialized:
-        return
-    detector = load_detector(select_models, prefer_onnx)
-    comparator = DamageComparator(config_path=CONFIG_PATHS[select_models])
-    visualizer = DamageVisualizer(config_path=CONFIG_PATHS[select_models])
-    _initialized = True
 app = FastAPI(
     title="Car Damage Detection API",
     description="YOLOv11-based car damage detection with DINOv2 ReID (Memory Optimized)",
@@ -105,6 +94,7 @@ def get_optimal_model_index(select_models: int, prefer_onnx: bool = True) -> int
         if not onnx_path.exists():
             raise FileNotFoundError(
                 f"Requested ONNX model index {select_models} not found at {MODEL_PATHS.get(select_models)}")
+        print(f"🚀 Selected ONNX model with MAXIMUM optimizations: {MODEL_PATHS[select_models]}")
         return select_models
 
     # Normalize to valid PT indices
@@ -114,6 +104,7 @@ def get_optimal_model_index(select_models: int, prefer_onnx: bool = True) -> int
     # PT preferred for 0..4
     pt_path = Path(MODEL_PATHS.get(select_models, ""))
     if pt_path.exists():
+        print(f"📦 Selected PyTorch model: {MODEL_PATHS[select_models]}")
         return select_models
 
     # If PT not found and prefer_onnx: fallback to ONNX with optimizations
@@ -121,6 +112,7 @@ def get_optimal_model_index(select_models: int, prefer_onnx: bool = True) -> int
     if prefer_onnx and onnx_index is not None:
         onnx_path = Path(MODEL_PATHS.get(onnx_index, ""))
         if onnx_path.exists():
+            print(f"PT not found at {pt_path}, falling back to optimized ONNX {MODEL_PATHS[onnx_index]}")
             return onnx_index
 
     # No suitable file found
@@ -180,12 +172,15 @@ def load_detector(select_models: int = 2, prefer_onnx: bool = True):
     else:
         raise ValueError(f"select_models={select_models} must be 0-11")
 
-    optimization_status = "MAXIMUM OPTIMIZATIONS" if model_type == "ONNX" else "Standard PyTorch"
+    optimization_status = "🚀 MAXIMUM OPTIMIZATIONS" if model_type == "ONNX" else "📦 Standard PyTorch"
+    print(f"Loaded {model_size} model in {model_type} format - {optimization_status}")
+    print(f"✅ DINOv2 ReID enabled for damage comparison")
 
     return detector
 
 
 # Initialize default detector with medium model (preferring ONNX for performance)
+print("🚀 Initializing API with optimized ONNX Runtime and DINOv2 ReID support...")
 detector = load_detector(2, prefer_onnx=True)
 comparator = DamageComparator(config_path=CONFIG_PATHS[2])
 visualizer = DamageVisualizer(config_path=CONFIG_PATHS[2])
@@ -530,14 +525,11 @@ async def compare_vehicle_damages(
         before_2: UploadFile = File(...),
         before_3: UploadFile = File(...),
         before_4: UploadFile = File(...),
-
         # After delivery images (6 positions)
         after_1: UploadFile = File(...),
         after_2: UploadFile = File(...),
         after_3: UploadFile = File(...),
         after_4: UploadFile = File(...),
-
-        # Model selection
         select_models: int = Form(2),
         prefer_onnx: bool = Form(True)
 ):
@@ -578,7 +570,7 @@ async def compare_vehicle_damages(
         all_after_detections = []
 
         # Use ThreadPoolExecutor to share memory (avoid OOM)
-        print(f"Processing {len(before_images)} image pairs...")
+        print(f"🔄 Processing {len(before_images)} image pairs using ThreadPoolExecutor...")
 
         with ThreadPoolExecutor(max_workers=2) as executor:  # Limit workers to avoid memory issues
             futures = [
@@ -619,6 +611,7 @@ async def compare_vehicle_damages(
         position_results.sort(key=lambda x: int(list(x.keys())[0].split('_')[1]))
 
         # Deduplicate BEFORE damages across all 6 views using DINOv2
+        print("🔍 Deduplicating damages across views using DINOv2...")
         unique_before = comparator.deduplicate_detections_across_views(
             all_before_detections, all_before_images
         )
@@ -629,8 +622,8 @@ async def compare_vehicle_damages(
         )
 
         print(
-            f"Before: {sum(len(d['boxes']) for d in all_before_detections)} detections → {len(unique_before)} unique")
-        print(f"After: {sum(len(d['boxes']) for d in all_after_detections)} detections → {len(unique_after)} unique")
+            f"✅ Before: {sum(len(d['boxes']) for d in all_before_detections)} detections → {len(unique_before)} unique")
+        print(f"✅ After: {sum(len(d['boxes']) for d in all_after_detections)} detections → {len(unique_after)} unique")
 
         # Determine overall case with deduplication
         actual_new_damages = max(0, len(unique_after) - len(unique_before))
@@ -717,12 +710,11 @@ async def compare_vehicle_damages(
 
 
 if __name__ == "__main__":
-     import os
-     uvicorn.run(
+    import os
+    uvicorn.run(
         "main:app",
         host="0.0.0.0",
-         port=int(os.environ.get("PORT", 7860)),
-         workers=2,
-        reload=True,
+        port=int(os.environ.get("PORT", 7860)),  
+        reload=False,  
         log_level="info"
-    )
+    )