updated app.py

app.py

@@ -39,15 +39,24 @@ def detect_faces_roboflow(image_path):
     return CLIENT_FACE.infer(image_path, model_id="face-detector-v4liw/2").get("predictions", [])
 
 def detect_eyes_roboflow(image_path, raw_image):
-    resp = CLIENT_EYES.infer(image_path, model_id="eye-detection-kso3d/3")
-    crops = []
-    for p in resp.get("predictions", []):
-        x1, y1 = int(p['x'] - p['width'] / 2), int(p['y'] - p['height'] / 2)
-        x2, y2 = int(p['x'] + p['width'] / 2), int(p['y'] + p['height'] / 2)
-        crop = raw_image[y1:y2, x1:x2]
-        if crop.size > 0:
-            crops.append(crop)
-    return crops
+    """Calls Roboflow to find eyes and returns cropped images of them."""
+    try:
+        resp = CLIENT_EYES.infer(image_path, model_id="eye-detection-kso3d/3")
+        crops = []
+        for p in resp.get("predictions", []):
+            x1 = int(p['x'] - p['width'] / 2)
+            y1 = int(p['y'] - p['height'] / 2)
+            x2 = int(p['x'] + p['width'] / 2)
+            y2 = int(p['y'] + p['height'] / 2)
+            crop = raw_image[y1:y2, x1:x2]
+            if crop.size > 0:
+                crops.append(crop)
+        # On success, return the crops and None for the error message
+        return crops, None
+    except Exception as e:
+        # If Roboflow fails, return an empty list and the error message
+        print(f"Error in Roboflow eye detection: {e}")
+        return [], str(e)
 
 def get_largest_iris_prediction(eye_crop):
     "Calls Roboflow to find the largest iris using a temporary file for reliability."
@@ -96,9 +105,9 @@ 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."})
 
-        eye_crops = detect_eyes_roboflow(temp_image_path, raw_image)
-        if len(eye_crops) != 2:
-            return JSONResponse(status_code=200, content={"warnings": ["Exactly two eyes not detected."]})
+        eye_crops, error_msg = detect_eyes_roboflow(temp_image_path, raw_image)
+        if error_msg or len(eye_crops) != 2:
+        return JSONResponse(status_code=200, content={"warnings": ["Exactly two eyes not detected."]})
 
         eye_crops.sort(key=lambda c: cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY))[0])