fix: Update upload-survey endpoint with working detection flow

src/api/main.py

@@ -558,12 +558,13 @@ async def analyze_survey(request: AnalysisRequest):
 @app.post("/upload-survey")
 async def upload_survey(files: List[UploadFile] = File(...)):
     """
-    Upload survey images, extract GPS data, AND run YOLO detection.
-    Returns detection results with GPS coordinates for map display.
+    Upload survey images and extract GPS data only.
+    Returns 'Pending' points for map display.
+    Use /analyze-survey to run AI detection later.
     """
     results = []
     
-    print(f"[UPLOAD] Processing {len(files)} survey files with YOLO detection...")
+    print(f"[UPLOAD] Uploading {len(files)} survey files...")
     
     for file in files:
         # Skip non-image files
@@ -595,59 +596,28 @@ async def upload_survey(files: List[UploadFile] = File(...)):
             gps_data = extract_gps_from_image(contents)
             base_lat = gps_data["latitude"]
             base_lon = gps_data["longitude"]
-            print(f"[GPS] GPS from {file.filename}: lat={base_lat}, lon={base_lon}")
+            print(f"[GPS] GPS from {file.filename}: lat={base_lat}, lon={base_lon}, success={gps_data.get('success')}")
             
-            # Run YOLO detection
-            image_array = np.array(image)
-            ensemble_results = ensemble.predict(image_array)
-            
-            if not ensemble_results:
-                # No damage detected - return single "No Damage" point
-                results.append({
-                    "x1": 0, "y1": 0, "x2": 0, "y2": 0,
-                    "class_name": "No Damage",
-                    "class_id": -1,
-                    "confidence": 1.0,
-                    "severity": "none",
-                    "latitude": float(base_lat),
-                    "longitude": float(base_lon),
-                    "image_url": image_url,
-                    "filename": unique_name
-                })
-            else:
-                # Add all detections found
-                for det in ensemble_results:
-                    # Classify severity based on size or confidence
-                    conf = float(det["confidence"])
-                    if conf >= 0.8:
-                        severity = "heavy"
-                    elif conf >= 0.5:
-                        severity = "medium"
-                    else:
-                        severity = "light"
-                    
-                    results.append({
-                        "x1": int(det["box"][0]), "y1": int(det["box"][1]),
-                        "x2": int(det["box"][2]), "y2": int(det["box"][3]),
-                        "class_name": det["class_name"],
-                        "class_id": det.get("class_id", 0),
-                        "confidence": conf,
-                        "severity": severity,
-                        "latitude": float(base_lat),
-                        "longitude": float(base_lon),
-                        "image_url": image_url,
-                        "filename": unique_name
-                    })
-                print(f"[DETECT] {file.filename}: Found {len(ensemble_results)} damages")
+            # Return "Pending" point for map
+            results.append({
+                "x1": 0, "y1": 0, "x2": 0, "y2": 0,
+                "class_name": "Pending",
+                "class_id": -2,
+                "confidence": 0.0,
+                "severity": "none",
+                "latitude": float(base_lat),
+                "longitude": float(base_lon),
+                "image_url": image_url,
+                "filename": unique_name
+            })
+            print(f"[OK] Uploaded: {file.filename} -> {unique_name}")
             
         except Exception as e:
             import traceback
-            print(f"[ERROR] Error processing {file.filename}: {e}")
+            print(f"[ERROR] Error uploading {file.filename}: {e}")
             traceback.print_exc()
             continue
 
-    print(f"[DONE] Processed {len(files)} files, {len(results)} detections total")
-    
     return {
         "success": True,
         "total_files": len(files),