fix inconsistent labeling

app.py

@@ -104,44 +104,49 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
         if not detect_faces_roboflow(temp_image_path):
             return JSONResponse(status_code=400, content={"error": "No face detected."})
 
-        # --- This is the corrected, robust logic ---
+        # --- This is the final corrected logic ---
         
-        # By default, assume we will use the original image
         image_to_process = raw_image
+        was_mirrored = False # --- NEW: Add a flag to track if we flipped the image
         
-        # First, try detection on the original image
         print("--- Attempting detection on original image... ---")
         eye_crops, error_msg = detect_eyes_roboflow(temp_image_path, image_to_process)
 
-        # If the first attempt fails, try the mirrored version
         if len(eye_crops) != 2:
             print("--- Original failed. Attempting detection on mirrored image... ---")
             mirrored_image = cv2.flip(raw_image, 1)
-            image_to_process = mirrored_image # Set the mirrored image as the one to use now
-
+            image_to_process = mirrored_image
+            was_mirrored = True # --- NEW: Set the flag to true
+            
             with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_mirrored:
                 cv2.imwrite(tmp_mirrored.name, mirrored_image)
                 temp_mirrored_image_path = tmp_mirrored.name
             try:
-                # Ensure we pass the mirrored image data for cropping
                 eye_crops, error_msg = detect_eyes_roboflow(temp_mirrored_image_path, image_to_process)
             finally:
                 os.remove(temp_mirrored_image_path)
 
-        # Final check after both attempts
         if error_msg or len(eye_crops) != 2:
             return JSONResponse(
                 status_code=400,
                 content={"error": "Could not detect exactly two eyes. Please try another photo."}
             )
 
+        # Sort the eyes from left to right based on their position in the image
         eye_crops.sort(key=lambda c: cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY))[0])
         
+        # --- NEW: If the image was mirrored, reverse the sorted list ---
+        # This ensures the person's right eye is always first.
+        if was_mirrored:
+            print("--- Image was mirrored, reversing eye order for correct labeling. ---")
+            eye_crops.reverse()
+
         flags = {}
         eye_images_b64 = {}
-
         for i, eye_crop in enumerate(eye_crops):
+            # Now, because of the reverse, i=0 is ALWAYS the person's right eye
             side = "right" if i == 0 else "left"
+            
             is_success, buffer = cv2.imencode(".jpg", eye_crop)
             if is_success:
                 eye_images_b64[side] = "data:image/jpeg;base64," + base64.b64encode(buffer).decode("utf-8")
@@ -156,7 +161,6 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
             else:
                 flags[side] = None
         
-        # Encode the image that was successfully analyzed (original or mirrored)
         is_success_main, buffer_main = cv2.imencode(".jpg", image_to_process)
         analyzed_image_b64 = ""
         if is_success_main: