mao nani

app.py

@@ -96,8 +96,6 @@ def run_leukocoria_prediction(iris_crop):
 # --- 3. FastAPI Application ---
 app = FastAPI()
 
-# In app.py, replace the existing function with this one for testing
-
 @app.post("/detect/")
 async def full_detection_pipeline(image: UploadFile = File(...)):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
@@ -113,8 +111,6 @@ 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 test logic with eye_crops.reverse() removed ---
-        
         image_to_process = raw_image
         was_mirrored = False
         
@@ -146,16 +142,16 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
         initial_boxes = [cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY)) for c in eye_crops]
         print(f"--- 5. Initial eye coordinates (x,y,w,h): {initial_boxes} ---")
 
-        # 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])
         
         sorted_boxes = [cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY)) for c in eye_crops]
         print(f"--- 6. Sorted eye coordinates (x,y,w,h): {sorted_boxes} ---")
         
-        # --- THE CHANGE ---
-        # The eye_crops.reverse() line has been removed for this test.
         if was_mirrored:
-            print("--- 7. Image was mirrored. (Reverse logic is currently disabled for testing) ---")
+            print("--- 7. Image was mirrored, reversing eye order for correct labeling. ---")
+            eye_crops.reverse()
+            reversed_boxes = [cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY)) for c in eye_crops]
+            print(f"--- 8. Reversed eye coordinates (x,y,w,h): {reversed_boxes} ---")
 
         flags = {}
         eye_images_b64 = {}
@@ -177,8 +173,10 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
             else:
                 flags[side] = None
         
+        # --- THIS BLOCK IS NOW CORRECTLY UN-INDENTED ---
+        # It runs AFTER the 'for' loop is complete.
         print("--- 10. Final generated flags:", flags, "---")
-
+        
         is_success_main, buffer_main = cv2.imencode(".jpg", image_to_process)
         analyzed_image_b64 = ""
         if is_success_main: