Update app.py

app.py

@@ -19,7 +19,6 @@ ALL_message = load_from_pkl(r'./label0601.pkl')
 Model_Paht = r'./model_epoch62.pth.tar'
 checkpoint = torch.load(Model_Paht,map_location='cpu')
 
-a = 5
 classnet = resnet50(
                 num_classes=1,
                 sample_size=128,
@@ -70,27 +69,7 @@ def inference():
         n = float(np.round(1-p, decimals=2))
         return {'急性期': n, '亚急性期': p}
 
-#
-#
-# def image_classifier(inp):
-#     #return {'cat': 0.3, 'dog': 0.7}
-#     return inp
-#
-# def image_read(inp):
-#     image = sitk.GetArrayFromImage(sitk.ReadImage(inp))
-#     ss = np.sum(image)
-#     return str(ss)
-#
-#
-# def upload_file(files):
-#     file_paths = [file.name for file in files]
-#     return file_paths
-#
-# with gr.Blocks() as demo:
-#     file_output = gr.File()
-#     upload_button = gr.UploadButton("Click to Upload a File",  file_types=["image", "video"], file_count="multiple")
-#     upload_button.upload(upload_file, upload_button, gr.Code(''))
-# demo.launch()
+
 
 import gradio as gr
 import numpy as np
@@ -229,6 +208,16 @@ class App:
                 btn2.click(get_medical_message, inputs=None, outputs=[out4,out6])
                 #demo = gr.Series(get_name, prepend_hello, append_nice)
 
+
+            gr.Markdown("##Examples")
+            gr.Examples(
+                #examples=r'F:\WorkSpacing\XS_data\FenQi\chuli_data\ALL\358small_exp4_cut_128_256_128\1093978_A_L_MRI.nii.gz',
+                examples=[os.path.join(os.path.dirname(__file__), "1093978_A_L_MRI.nii.gz")],
+                inputs=inp,
+                outputs=[out1, out2, out3,slider1,slider2,slider3],
+                fn=get_Image_reslice,
+                cache_examples=True,
+            )
         demo.launch()
 app = App()
     # with gr.Blocks() as demo: