Update app.py

app.py

@@ -212,10 +212,11 @@ with gr.Blocks(fill_width=True) as demo:
         3. Then, choose a BERT classifier model from the drop down.\n
         4. Finally, click the 'start prediction' buttton.\n
         """)
-        with gr.Row():
-            with gr.Column(scale=4):
-                with gr.Group():
+        with gr.Group():
+            with gr.Row(equal_height=True):
+                with gr.Column():
                     T_file_input = gr.File(label="Upload CSV or TSV File", file_types=['.tsv', '.csv']) 
+                with gr.Column():
                     T_text_field = gr.Textbox(label="Text field name", value="tweet_text")
                     T_event_model = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
                     with gr.Accordion("Prediction threshold", open=False):        
@@ -223,13 +224,13 @@ with gr.Blocks(fill_width=True) as demo:
                                           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)                
                     T_predict_button = gr.Button("Start Prediction")
-                
+        with gr.Row():
+            with gr.Column(scale=4):
                 T_data_filter = gr.Dropdown(visible=False)
                 T_tweet_embed = gr.HTML("<h1>Select a Tweet ID to view Tweet</h1>", container=True, every=1.0)
                 
             with gr.Column(scale=6):
                 T_data = gr.DataFrame(headers=["Texts", "event_label", "model_score", "IDs"], 
-                                      row_count=(10, 'dynamic'),
                                       wrap=True,
                                       show_fullscreen_button=True, 
                                       show_copy_button=True,