No meed to return state on display_eval

app.py

@@ -243,27 +243,30 @@ def display_eval(selected_models, dataset, task_type, downstream, fusion_type, s
 
     try:
         if not selected_models:
-            return "Please select at least one enabled model.", state
+            return "Please select at least one enabled model."
 
         if len(selected_models) > 1:
             if task_type == "Classification":
                 if downstream_model == "Default Settings":
                     downstream_model = "DefaultClassifier"
                     params = None
-                result, state["roc_auc"], state["fpr"], state["tpr"], state["x_batch"], state["y_batch"] = fm4m.multi_modal(model_list=selected_models,
-                                                                                               downstream_model=downstream_model,
-                                                                                               params = params,
-                                                                                               dataset=dataset)
+
+                result, state["roc_auc"], state["fpr"], state["tpr"], state["x_batch"], state["y_batch"] = \
+                    fm4m.multi_modal(model_list=selected_models,
+                                        downstream_model=downstream_model,
+                                        params = params,
+                                        dataset=dataset)
 
             elif task_type == "Regression":
                 if downstream_model == "Default Settings":
                     downstream_model = "DefaultRegressor"
                     params = None
 
-                result, state["RMSE"], state["y_batch_test"], state["y_prob"], state["x_batch"], state["y_batch"] = fm4m.multi_modal(model_list=selected_models,
-                                                                      downstream_model=downstream_model,
-                                                                      params=params,
-                                                                      dataset=dataset)
+                result, state["RMSE"], state["y_batch_test"], state["y_prob"], state["x_batch"], state["y_batch"] = \
+                    fm4m.multi_modal(model_list=selected_models,
+                                        downstream_model=downstream_model,
+                                        params=params,
+                                        dataset=dataset)
 
         else:
             if task_type == "Classification":
@@ -271,26 +274,28 @@ def display_eval(selected_models, dataset, task_type, downstream, fusion_type, s
                     downstream_model = "DefaultClassifier"
                     params = None
 
-                result, state["roc_auc"], state["fpr"], state["tpr"], state["x_batch"], state["y_batch"] = fm4m.single_modal(model=selected_models[0],
-                                                                                downstream_model=downstream_model,
-                                                                                params=params,
-                                                                                dataset=dataset)
+                result, state["roc_auc"], state["fpr"], state["tpr"], state["x_batch"], state["y_batch"] = \
+                    fm4m.single_modal(model=selected_models[0],
+                                        downstream_model=downstream_model,
+                                        params=params,
+                                        dataset=dataset)
 
             elif task_type == "Regression":
                 if downstream_model == "Default Settings":
                     downstream_model = "DefaultRegressor"
                     params = None
 
-                result, state["RMSE"], state["y_batch_test"], state["y_prob"], state["x_batch"], state["y_batch"] = fm4m.single_modal(model=selected_models[0],
-                                                                       downstream_model=downstream_model,
-                                                                       params=params,
-                                                                       dataset=dataset)
+                result, state["RMSE"], state["y_batch_test"], state["y_prob"], state["x_batch"], state["y_batch"] = \
+                    fm4m.single_modal(model=selected_models[0],
+                                        downstream_model=downstream_model,
+                                        params=params,
+                                        dataset=dataset)
 
         if result == None:
             result = "Data & Model Setting is incorrect"
     except Exception as e:
-        return f"An error occurred: {e}", state
-    return f"{result}", state
+        return f"An error occurred: {e}"
+    return f"{result}"
 
 
 # Function to handle plot display
@@ -604,7 +609,7 @@ with gr.Blocks() as demo:
 
             eval_button.click(display_eval,
                               inputs=[model_checkbox, selected_columns_message, task_radiobutton, output, fusion_radiobutton, state],
-                              outputs=[eval_output, state])
+                              outputs=eval_output)
 
             plot_radio.change(display_plot, inputs=[plot_radio, state], outputs=plot_output)