Update app.py

app.py

@@ -1,12 +1,12 @@
 import torch
-from monai.networks.nets import DenseNet121
+from torchvision import transforms, models, datasets, utils
 
 import gradio as gr
 
 #from PIL import Image
 
-model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=6)
-model.load_state_dict(torch.load('weights/mednist_model.pth', map_location=torch.device('cpu')))
+model = models.densenet121()
+model.load_state_dict(torch.load('derma_diseases_detection_best.pt', map_location=torch.device('cpu')))
 
 from monai.transforms import (
     EnsureChannelFirst,
@@ -55,12 +55,4 @@ with gr.Blocks(title="Medical Image Classification with MONAI - ClassCat",
     send_btn = gr.Button("Infer")
     send_btn.click(fn=classify_image, inputs=input_image, outputs=output_label)
 
-    with gr.Row():
-        gr.Examples(['./samples/mednist_AbdomenCT00.png'], label='Sample images : AbdomenCT', inputs=input_image)
-        gr.Examples(['./samples/mednist_CXR02.png'], label='CXR', inputs=input_image)
-        gr.Examples(['./samples/mednist_ChestCT08.png'], label='ChestCT', inputs=input_image)
-        gr.Examples(['./samples/mednist_Hand01.png'], label='Hand', inputs=input_image)
-        gr.Examples(['./samples/mednist_HeadCT07.png'], label='HeadCT', inputs=input_image)
-
-#demo.queue(concurrency_count=3)
 demo.launch(debug=True)