Update app.py

app.py

@@ -170,7 +170,7 @@ def find_the_most_recent_predict_path(path):
   path = sorted(path, key=lambda x: x[-1])
   return '/'.join(path[-1])
     
-def Aspergillus_Detect():
+def Aspergillus_Detect(threshold):
     # get_all_files(working_directory)
     
     model = YOLO(weight_path)  # pretrained YOLOv8n model
@@ -254,14 +254,14 @@ def Aspergillus_Detect():
 
 """# classify images: genus and Aspergillus"""
 
-def classify_images(files):
+def classify_images(files,threshold):
     print(f'threshold = {threshold}')
     predict, result1 = genus_classify_images(files)
     # # 列出工作目录及其子目录下的所有文件
     # all_files = get_all_files(working_directory)
             
     if predict == 'Aspergillus':
-      result2 = Aspergillus_Detect()
+      result2 = Aspergillus_Detect(threshold)
       return f'{result1}\n\n{result2}'
     return result1
 
@@ -302,13 +302,13 @@ def delete_folder(directory_path):
 
 """# main of gradio"""
 
-threshold = 0.6
+# threshold = 0.6
 with gr.Blocks() as fungus_classification:
     threshold = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.6, label="Threshold", info="Choose between 0.1 and 1")
     image_output = gr.Gallery(label = 'Images of Molds')
     predict_outputs = gr.Textbox(label = 'Prediction Result')
     upload_button = gr.UploadButton("Click to Upload Files", file_types=["image", "video"], file_count="multiple")
     upload_button.upload(upload_file, upload_button, image_output)
-    upload_button.upload(classify_images, upload_button, predict_outputs)
+    upload_button.upload(classify_images, [upload_button,threshold], predict_outputs)
 # fungus_classification.launch(share=True, debug=True, enable_queue=True)
 fungus_classification.launch(share=True, debug=True)