Update app.py

app.py

@@ -654,7 +654,7 @@ def preprocess_image(image):
     return transform(image).unsqueeze(0)
     
 @spaces.GPU
-async def predict_single_dog(image):
+def predict_single_dog(image):
     """
     Predicts the dog breed using only the classifier.
     Args:
@@ -687,7 +687,7 @@ async def predict_single_dog(image):
         return probabilities[0], breeds[:3], relative_probs
         
 @spaces.GPU
-async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55):
+def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55):
     
     results = model_manager.yolo_model(image, conf=conf_threshold, 
                                      iou=iou_threshold)[0]
@@ -772,7 +772,7 @@ def create_breed_comparison(breed1: str, breed2: str) -> dict:
     return comparison_data
 
     
-async def predict(image):
+def predict(image):
     """
     Main prediction function that handles both single and multiple dog detection.
 
@@ -791,7 +791,7 @@ async def predict(image):
             image = Image.fromarray(image)
 
         # Detect dogs in the image
-        dogs = await detect_multiple_dogs(image)
+        dogs = detect_multiple_dogs(image)
         color_scheme = get_color_scheme(len(dogs) == 1)
 
         # Prepare for annotation
@@ -828,7 +828,7 @@ async def predict(image):
             draw.text((label_x, label_y), label, fill=color, font=font)
 
             # Predict breed
-            top1_prob, topk_breeds, relative_probs = await predict_single_dog(cropped_image)
+            top1_prob, topk_breeds, relative_probs = predict_single_dog(cropped_image)
             combined_confidence = detection_confidence * top1_prob
 
             # Format results based on confidence with error handling