Update app.py

app.py

@@ -9,7 +9,7 @@ netflix = load_dataset('hugginglearners/netflix-shows',use_auth_token=True)
 
 #Filter for relevant columns and convert to pandas
 netflix_df = netflix['train'].to_pandas()
-netflix_df = netflix_df[['type','title','country','cast','release_year','rating','duration','listed_in','description']]
+netflix_df = netflix_df[['type','title','country','description','release_year','rating','duration','listed_in','cast']]
 passages = netflix_df['description'].tolist()
 
 #load mpnet model
@@ -99,13 +99,13 @@ with demo:
             
     with gr.Row():
         
-        bi_output = gr.DataFrame(headers=['Similarity Score','Type','Title','Country','Cast','Release Year','Rating','Duration','Category Listing','Description'],
-        label=f'Top-{top_k} Bi-Encoder Retrieval hits')
+        bi_output = gr.DataFrame(headers=['Similarity Score','Type','Title','Country','Description','Release Year','Rating','Duration','Category Listing','Cast'],
+        label=f'Top-{top_k} Bi-Encoder Retrieval hits', wrap=True)
         
     with gr.Row():
         
-        cross_output = gr.DataFrame(headers=['Similarity Score','Type','Title','Country','Cast','Release Year','Rating','Duration','Category Listing','Description'],
-        label=f'Top-{top_k} Cross-Encoder Re-ranker hits')
+        cross_output = gr.DataFrame(headers=['Similarity Score','Type','Title','Country','Description','Release Year','Rating','Duration','Category Listing','Cast'],
+        label=f'Top-{top_k} Cross-Encoder Re-ranker hits', wrap=True)
         
     with gr.Row():
         examples = gr.Examples(examples=example_queries,inputs=[query])