update

app.py

@@ -14,6 +14,7 @@ newsapi = NewsApiClient(api_key=HF_TOKEN)
 classifier = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment") 
 today = str(date.today())
 
+print("******** Outside Inference function ********")
 print(f"HF_TOKEN is - {HF_TOKEN}")
 
 #times of india
@@ -64,11 +65,13 @@ def inference(newssource): #, date):
   url = [entry['url'] for entry in all_articles['articles']]
   urlToImage = [entry['urlToImage'] for entry in all_articles['articles']]
   
-  print(f"description length is {len(description)}")
-  print(f"content length is {len(content)}")
-  print(f"url length is {len(url)}")
-  print(f"urlToImage length is {len(urlToImage)}")
-  print(f"sentiment length is {len(sentiment)}")
+  print("********************************************************")
+  print(f"Newssource is - {newssource}")
+  print(f"description length is - {len(description)}")
+  print(f"content length is - {len(content)}")
+  print(f"url length is - {len(url)}")
+  print(f"urlToImage length is - {len(urlToImage)}")
+  print(f"sentiment length is - {len(sentiment)}")
   
   dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment,}
   #dictnews = { 'description' : [entry['description'] for entry in all_articles['articles']],
@@ -98,6 +101,6 @@ with gr.Blocks() as demo:
     out_dataframe = gr.Dataframe(wrap=True, datatype = ["str", "str", "markdown", "markdown", "str"])
     
   b1.click(fn=inference, inputs=in_newssource, outputs=[out_dataframe, out_news])
-  b2.click(fn=inference, inputs=in_newssource, outputs=out_dataframe)
+  b2.click(fn=inference, inputs=in_newssource, outputs=[out_dataframe, out_news])
 
 demo.launch(debug=True, show_error=True)