Update app.py

app.py

@@ -6,7 +6,6 @@ nltk.download('punkt')
 from transformers import pipeline
 import gradio as gr
 
-
 USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
 
 config = Config()
@@ -45,9 +44,10 @@ io2 = gr.Interface.load("huggingface/facebook/bart-large-cnn")
 io3 = gr.Interface.load("huggingface/google/pegasus-xsum")  
 io4 = gr.Interface.load("huggingface/sshleifer/distilbart-cnn-6-6")                   
 
-iface = Parallel(io1, io2, io3, io4,
-                 theme='huggingface', 
-                 inputs = gr.inputs.Textbox(lines=10, label="Text"))
+# Use Parallel and Series directly from gradio
+iface = gr.Parallel(io1, io2, io3, io4,
+                    theme='huggingface', 
+                    inputs=gr.inputs.Textbox(lines=10, label="Text"))
 
 iface.launch()
 
@@ -70,21 +70,19 @@ sample_url = [['https://www.technologyreview.com/2021/07/22/1029973/deepmind-alp
               ['https://www.technologyreview.com/2021/07/21/1029860/disability-rights-employment-discrimination-ai-hiring/'],
               ['https://www.technologyreview.com/2021/07/09/1028140/ai-voice-actors-sound-human/']]
 
-desc =  '''
+desc = '''
         Let Hugging Face models summarize articles for you. 
         Note: Shorter articles generate faster summaries.
         This summarizer uses bart-large-cnn model by Facebook
         '''
 
-iface = Series(extractor, summarizer, 
-  inputs=gr.inputs.Textbox(
-      lines=2,
-      label='URL'
-  ),
-  outputs='text',
-  title='News Summarizer',
-  theme='huggingface',
-  description=desc,
-  examples=sample_url)
+# Use Parallel and Series directly from gradio
+iface = gr.Series(extractor, summarizer, 
+                   inputs=gr.inputs.Textbox(lines=2, label='URL'),
+                   outputs='text',
+                   title='News Summarizer',
+                   theme='huggingface',
+                   description=desc,
+                   examples=sample_url)
 
 iface.launch()