Update app.py

app.py

@@ -29,19 +29,19 @@ with gr.Blocks() as demo:
             #run_btn.click(run_inference, [model_name, pitch, input_path, f0_method, save_as, index_rate, volume_normalization, consonant_protection], output_message)
 
         with gr.Tab("Training"):
-            model_name = gr.Textbox(label="Model Name (No spaces or symbols)")
-            dataset_folder = gr.Textbox(label="Dataset Folder", value="/content/dataset")
-            f0method = gr.Dropdown(["pm", "harvest", "rmvpe", "rmvpe_gpu"], label="F0 Method", value="rmvpe_gpu")
-             
-            preprocess_btn = gr.Button("Start Preprocessing")
-            f0_btn = gr.Button("Extract F0 Feature")
-            train_btn = gr.Button("Train Index")
-            preprocess_output = gr.Textbox(label="Preprocessing Log")
-            f0_output = gr.Textbox(label="F0 Feature Extraction Log")
-            train_output = gr.Textbox(label="Training Log")
+            with gr.TabItem("Create Index and stuff"):
+                model_name = gr.Textbox(label="Model Name (No spaces or symbols)")
+                dataset_folder = gr.Textbox(label="Dataset Folder", value="/content/dataset")
+                f0method = gr.Dropdown(["pm", "harvest", "rmvpe", "rmvpe_gpu"], label="F0 Method", value="rmvpe_gpu")
+                preprocess_btn = gr.Button("Start Preprocessing")
+                f0_btn = gr.Button("Extract F0 Feature")
+                train_btn = gr.Button("Train Index")
+                preprocess_output = gr.Textbox(label="Preprocessing Log")
+                f0_output = gr.Textbox(label="F0 Feature Extraction Log")
+                train_output = gr.Textbox(label="Training Log")
 
-            #preprocess_btn.click(preprocess_data, inputs=[model_name, dataset_folder], outputs=preprocess_output)
-            #f0_btn.click(extract_f0_feature, inputs=[model_name, f0method], outputs=f0_output)
-            #train_btn.click(train_index, inputs=[model_name, "v2"], outputs=train_output)
+                #preprocess_btn.click(preprocess_data, inputs=[model_name, dataset_folder], outputs=preprocess_output)
+                #f0_btn.click(extract_f0_feature, inputs=[model_name, f0method], outputs=f0_output)
+                #train_btn.click(train_index, inputs=[model_name, "v2"], outputs=train_output)
     
 demo.launch()