Update app.py

app.py

@@ -39,6 +39,7 @@ def single_classification(text, event_model, threshold):
     return res["event"], res["score"]
     
 def load_and_classify_csv(file, text_field, event_model, threshold):
+    text_field = text_field.strip()
     filepath = file.name
     if ".csv" in filepath:
         df = pd.read_csv(filepath)
@@ -66,7 +67,7 @@ def load_and_classify_csv(file, text_field, event_model, threshold):
     return flood_related, fire_related, not_related, model_confidence, len(df[text_field].to_list()), df, gr.update(interactive=True), gr.update(interactive=True)
 
 def load_and_classify_csv_dataframe(file, text_field, event_model, threshold): 
-    
+    text_field = text_field.strip()
     filepath = file.name
     if ".csv" in filepath:
         df = pd.read_csv(filepath)
@@ -98,6 +99,7 @@ def load_and_classify_csv_dataframe(file, text_field, event_model, threshold):
 
 
 def calculate_accuracy(flood_selections, fire_selections, none_selections, num_posts, text_field, data_df):
+    text_field = text_field.strip()
     posts = data_df[text_field].to_list()
     selections = flood_selections + fire_selections + none_selections
     eval = []
@@ -284,7 +286,12 @@ with gr.Blocks(fill_width=True) as demo:
         with gr.Group():
             with gr.Row():
                 with gr.Column(scale=7):
-                    qa_df = gr.DataFrame()
+                    qa_df = gr.DataFrame(wrap=True,
+                                        show_fullscreen_button=True,
+                                        show_copy_button=True,
+                                        show_search="filter",
+                                        max_height=1000,
+                                        column_widths=["10%","70%","20%"])
                 with gr.Column(scale=3):
                     qa_tweet_embed = gr.HTML("""<div id="tweet-container2"></div>""")