Update app.py

app.py

@@ -172,6 +172,40 @@ with gr.Blocks(fill_width=True) as demo:
     
     T_data_ss_state = gr.State(value=pd.DataFrame())
     
+
+    with gr.Tab("Single Text Classification"):
+        gr.Markdown(
+        """
+        # Event Type Prediction Demo
+        In this section you test the relevance classifier with written texts.\n 
+        Usage:\n
+            - Type a tweet-like text in the textbox.\n
+            - Then press Enter.\n
+        """)
+        with gr.Group():
+            with gr.Row():
+                with gr.Column(scale=3):
+                    model_sing_classify = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
+                with gr.Column(scale=7):
+                    with gr.Accordion("Prediction threshold", open=False):        
+                        T_threshold = gr.Slider(0, 1, value=0, step=0.01, label="Prediction threshold", show_label=False, 
+                                          info="This value sets a threshold by which texts classified flood or fire are accepted, \
+                                              higher values makes the classifier stricter (CAUTION: A value of 1 will set all predictions as none)", interactive=True) 
+                
+        text_to_classify = gr.Textbox(label="Text", info="Enter tweet-like text", submit_btn=True)
+        text_to_classify_examples = gr.Examples([["The streets are flooded, I can't leave #BostonStorm"], 
+                                                 ["Controlado el incendio de Rodezno que ha obligado a desalojar a varias bodegas de la zona."], 
+                                                 ["Cambrils:estació Renfe inundada 19 persones dins d'un tren. FGC a Capellades, petit descarrilament 5 passatgers #Inuncat @emergenciescat"],
+                                                 ["Anscheinend steht die komplette Neckarwiese unter Wasser! #Hochwasser"]], text_to_classify)
+
+        with gr.Group():
+            with gr.Row():
+                with gr.Column():
+                    classification = gr.Textbox(label="Classification")
+                with gr.Column():
+                    classification_score = gr.Number(label="Classification Score")
+                    
+    
     with gr.Tab("Event Type Classification"):
         gr.Markdown(
         """
@@ -244,36 +278,6 @@ with gr.Blocks(fill_width=True) as demo:
         hsummary = gr.Textbox(label="Summary")
         
         qa_df = gr.DataFrame(visible=False)
-
-
-    with gr.Tab("Single Text Classification"):
-        gr.Markdown(
-        """
-        # Event Type Prediction Demo
-        In this section you test the relevance classifier with written texts.\n 
-        Usage:\n
-            - Type a tweet-like text in the textbox.\n
-            - Then press Enter.\n
-        """)
-        with gr.Row():
-            with gr.Column(scale=3):
-                model_sing_classify = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
-            with gr.Column(scale=7):
-                threshold_sing_classify = gr.Slider(0, 1, value=0, step=0.01, label="Prediction threshold", 
-                              info="This value sets a threshold by which texts classified flood or fire are accepted, \
-                                  higher values makes the classifier stricter (CAUTION: A value of 1 will set all predictions as none)", interactive=True)
-                
-        text_to_classify = gr.Textbox(label="Text", info="Enter tweet-like text", submit_btn=True)
-        text_to_classify_examples = gr.Examples([["The streets are flooded, I can't leave #BostonStorm"], 
-                                                 ["Controlado el incendio de Rodezno que ha obligado a desalojar a varias bodegas de la zona."], 
-                                                 ["Cambrils:estació Renfe inundada 19 persones dins d'un tren. FGC a Capellades, petit descarrilament 5 passatgers #Inuncat @emergenciescat"],
-                                                 ["Anscheinend steht die komplette Neckarwiese unter Wasser! #Hochwasser"]], text_to_classify)
-        
-        with gr.Row():
-            with gr.Column():
-                classification = gr.Textbox(label="Classification")
-            with gr.Column():
-                classification_score = gr.Number(label="Classification Score")
  
         
     # with gr.Tab("Event Type Classification Eval"):