Update app.py

app.py

@@ -36,10 +36,10 @@ def check_file(file, suffix=''):
             ).st_size > 0, f'File download failed: {url}'  # check
         return file
 
-def read_pretrain(path):
-    return torch.hub.load('ultralytics/yolov5', 'custom', path=path)
+# def read_pretrain(path):
+#     return torch.hub.load('ultralytics/yolov5', 'custom', path=path)
     
-default_pretrained = '2022.11.04-YOLOv5x6_1280-Hololive_Waifu_Classification.pt'
+# default_pretrained = '2022.11.04-YOLOv5x6_1280-Hololive_Waifu_Classification.pt'
     
 st.title("Hololive Waifu Classification")
 
@@ -64,18 +64,18 @@ agnostic = st.selectbox('Class-agnostic', (False, True))
 amp = st.selectbox('Automatic Mixed Precision inference', (False, True))
 max_det  = st.number_input(label='Maximum number of detections per image', min_value=None, max_value=None, value=1000, step=1)
 clicked = st.button('Excute')
-with st.spinner('Loading the model...'):
-    model = read_pretrain(default_pretrained)
+# with st.spinner('Loading the model...'):
+#     model = read_pretrain(default_pretrained)
     
 if clicked:
     with st.spinner('Loading the image...'):
         image_path = check_file(image)
         input_image = Image.open(image_path)
-    if default_pretrained != pretrained:
-        with st.spinner('Loading the model...'):
+    # if default_pretrained != pretrained:
+    with st.spinner('Loading the model...'):
             # model = torch.hub.load('ultralytics/yolov5', 'custom', path=os.path.join('pretrained', pretrained))
             # model = torch.hub.load('ultralytics/yolov5', 'custom', path=pretrained)
-            model = read_pretrain(pretrained)
+        model = read_pretrain(pretrained)
     with st.spinner('Updating configuration...'):
         model.conf = float(conf)
         model.max_det = int(max_det)