Update app.py

app.py

@@ -15,7 +15,7 @@ import traceback
 # -----------------------------
 HAND_MODEL_PATH = "hand_landmarker.task"
 HAND_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task"
-YOLO_MODEL_PATH = "yolov11n_finetuned_ASL.pt"
+YOLO_MODEL_PATH = "yolov11n_finetuned_ASL.pt"  # Push this small model to HF repo
 
 # -----------------------------
 # 2. Download MediaPipe model if missing
@@ -30,22 +30,20 @@ if not os.path.exists(HAND_MODEL_PATH):
 # -----------------------------
 # 3. Load models
 # -----------------------------
-# YOLO ASL classifier
 yolo_model = YOLO(YOLO_MODEL_PATH)
 yolo_model.eval()
 
-# MediaPipe hand landmark detector
 base_options = python.BaseOptions(model_asset_path=HAND_MODEL_PATH)
 hand_options = vision.HandLandmarkerOptions(base_options=base_options, num_hands=1)
 detector = vision.HandLandmarker.create_from_options(hand_options)
 
 # -----------------------------
-# 4. Inference function with robust error handling
+# 4. Inference function
 # -----------------------------
 def predict_asl(image):
     try:
         if image is None:
-            raise ValueError("No image provided")
+            raise ValueError("No image uploaded")
         
         img = image.copy()
         h, w, _ = img.shape
@@ -60,7 +58,7 @@ def predict_asl(image):
                     x, y = int(landmark.x * w), int(landmark.y * h)
                     cv2.circle(img, (x, y), 3, (0, 255, 0), -1)
 
-        # --- YOLO prediction ---
+        # --- YOLO prediction directly on NumPy array ---
         results = yolo_model.predict(img, imgsz=300, verbose=False)[0]
         pred_idx = results.probs.top1
         pred_label = results.names[pred_idx]
@@ -83,7 +81,6 @@ def predict_asl(image):
     except Exception as e:
         print("❌ Error in predict_asl:", e)
         traceback.print_exc()
-        # Return original image and error placeholders
         return image, "Error", 0.0
 
 # -----------------------------