Update app.py

app.py

@@ -165,17 +165,20 @@ def get_akc_breeds_link():
 #     except Exception as e:
 #         return f"An error occurred: {e}", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
+
 def predict(image):
     if image is None:
         return "Please upload an image to get started.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
     try:
+        # 檢查圖片是否是 numpy.ndarray，如果是則轉換為 PIL.Image
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+
         # 使用 YOLO 偵測狗
         results = model_yolo(image)
-        
-        # 檢查 YOLO 輸出
-        boxes = results[0].boxes  # 修改這裡，使用 results[0].boxes 來提取邊界框
-        
+        boxes = results[0].boxes  # 提取邊界框
+
         if len(boxes) == 0:
             return "No dog detected in the image.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
@@ -185,7 +188,9 @@ def predict(image):
         for i, box in enumerate(boxes):
             # 提取每隻狗的區域
             x1, y1, x2, y2 = map(int, box.xyxy[0])  # 使用 box.xyxy 來提取邊界框座標
-            cropped_image = image.crop((x1, y1, x2, y2))  # 裁剪狗區域
+
+            # 裁剪出狗區域，確保 image 是 PIL.Image 格式
+            cropped_image = image.crop((x1, y1, x2, y2))
             image_tensor = preprocess_image(cropped_image)
 
             with torch.no_grad():
@@ -226,6 +231,7 @@ def predict(image):
 
 
 
+
 def format_description(description, breed):
     if isinstance(description, dict):
         formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])