added log messages to properly debug

app.py

@@ -89,8 +89,6 @@ def run_leukocoria_prediction(iris_crop):
 # --- 3. FastAPI Application ---
 app = FastAPI()
 
-# In app.py, replace the existing function with this one
-
 @app.post("/detect/")
 async def full_detection_pipeline(image: UploadFile = File(...)):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
@@ -105,26 +103,26 @@ 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 final corrected logic ---
         
         image_to_process = raw_image
-        was_mirrored = False # Add a flag to track if we flipped the image
+        was_mirrored = False
         
-        print("--- Attempting detection on original image... ---")
+        print("--- 1. Attempting detection on original image... ---")
         eye_crops, error_msg = detect_eyes_roboflow(temp_image_path, image_to_process)
+        print(f"--- 2. Found {len(eye_crops)} eyes in original image. ---")
 
         if len(eye_crops) != 2:
-            print("--- Original failed. Attempting detection on mirrored image... ---")
+            print("--- 3. Original failed. Attempting detection on mirrored image... ---")
             mirrored_image = cv2.flip(raw_image, 1)
             image_to_process = mirrored_image
-            was_mirrored = True # Set the flag to true
+            was_mirrored = 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:
                 eye_crops, error_msg = detect_eyes_roboflow(temp_mirrored_image_path, image_to_process)
+                print(f"--- 4. Found {len(eye_crops)} eyes in mirrored image. ---")
             finally:
                 os.remove(temp_mirrored_image_path)
 
@@ -134,26 +132,32 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
                 content={"error": "Could not detect exactly two eyes. Please try another photo."}
             )
 
+        # Get the bounding box coordinates before sorting
+        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])
         
-        # --- THE CRITICAL FIX ---
-        # If the image was mirrored, the eye on the left of the image is the person's
-        # left eye. We need to reverse the list so the person's right eye is first.
+        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} ---")
+        
         if was_mirrored:
-            print("--- Image was mirrored, reversing eye order for correct labeling. ---")
+            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 = {}
         for i, eye_crop in enumerate(eye_crops):
-            # Because of the sort and potential reverse, i=0 is ALWAYS the person's right eye
             side = "right" if i == 0 else "left"
+            print(f"--- 9. Processing loop index {i}, assigning to: {side} eye. ---")
             
+            # ... (rest of the processing loop remains the same) ...
             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")
-
             pred = get_largest_iris_prediction(eye_crop)
             if pred:
                 x1, y1 = int(pred['x'] - pred['width'] / 2), int(pred['y'] - pred['height'] / 2)
@@ -164,6 +168,8 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
             else:
                 flags[side] = None
         
+        print("--- 10. Final generated flags:", flags, "---")
+        
         is_success_main, buffer_main = cv2.imencode(".jpg", image_to_process)
         analyzed_image_b64 = ""
         if is_success_main: