Update app.py

app.py

@@ -74,7 +74,7 @@ def predict_model1(image):
     return formatted_output
 
 # Set your OpenAI API key
-openai.api_key = "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"
+openai.api_key = "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"
 
 # Function to encode the image as base64
 def encode_image(image_path):
@@ -204,9 +204,9 @@ def toggle_model(selected_models,flag):
 
 def toggle_mode(mode):
         if mode == "파일 업로드":
-            return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+            return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) , gr.update(visible=False)
         else:
-            return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
 
 def display_image(image_file):
     image=Image.open(image_file)
@@ -223,7 +223,7 @@ def display_folder_images(image_file_path_list):
     image_files_cnt=len(image_files)
     current_image_index = 0
     if image_files:
-        return Image.open(image_files[current_image_index]), os.path.basename(image_files[current_image_index]), gr.update(interactive=False), gr.update(interactive=True)
+        return Image.open(image_files[current_image_index]), os.path.basename(image_files[current_image_index]), gr.update(interactive=False), gr.update(interactive=True), gr.update(visible = True), gr.update(visible = False)
     return None, "No images found"
     
 
@@ -245,6 +245,9 @@ def prev_image():
         return Image.open(image_files[current_image_index]), os.path.basename(image_files[current_image_index]), gr.update(interactive=not prev_disabled), gr.update(interactive= not next_disabled)
     return None, "No images found"
 
+def folder_reupload():
+    return gr.update(visible=False), gr.update(visible=True)
+
 css = """
 .dataframe-class {
     overflow-y: auto !important; /* 스크롤을 가능하게 */
@@ -261,6 +264,7 @@ with gr.Blocks(css=css) as iface:
             mode_selector = gr.Radio(["파일 업로드", "폴더 업로드"], label="Upload Mode", value="파일 업로드")
             image_uploader = gr.File(file_count="single", file_types=["image"], visible=True)
             folder_uploader = gr.File(file_count="directory", file_types=["image"], visible=False, height=50)
+            folder_reupload_button = gr.Button("폴더 업로드", visible=False)
             model_type=gr.Dropdown(["VAIV_DePlot","gpt-4o-mini","all"],value="VAIV_DePlot",label="model",multiselect=True)
             image_displayer = gr.Image(visible=True)
             image_name = gr.Text("", visible=True)
@@ -287,13 +291,14 @@ with gr.Blocks(css=css) as iface:
     mode_selector.change(
         toggle_mode,
         inputs=[mode_selector],
-        outputs=[image_uploader, folder_uploader, prev_button, next_button]
+        outputs=[image_uploader, folder_uploader, prev_button, next_button, folder_reupload_button]
     )
 
     image_uploader.upload(display_image,inputs=[image_uploader],outputs=[image_displayer,image_name])
-    folder_uploader.upload(display_folder_images, inputs=[folder_uploader], outputs=[image_displayer, image_name, prev_button, next_button])
+    folder_uploader.upload(display_folder_images, inputs=[folder_uploader], outputs=[image_displayer, image_name, prev_button, next_button, folder_reupload_button, folder_uploader])
     prev_button.click(prev_image, outputs=[image_displayer, image_name, prev_button, next_button])
     next_button.click(next_image, outputs=[image_displayer, image_name, prev_button, next_button])
+    folder_reupload_button.click(folder_reupload, outputs =[folder_reupload_button, folder_uploader])
     inference_button.click(inference,inputs=[image_uploader,mode_selector],outputs=[ko_deplot_generated_df, gpt_generated_df, ko_deplot_generated_txt, gpt_generated_txt])
 
     if __name__ == "__main__":