Update app.py

app.py

@@ -1,16 +1,32 @@
 import gradio as gr
-import transformers
-from transformers import BartTokenizer, BartForConditionalGeneration
+from gradio.mix import Parallel, Series
+from transformers import pipeline
+import wikipedia
 
-model_name = 'facebook/bart-large-cnn'
-tokenizer = BartTokenizer.from_pretrained(model_name)
-model = BartForConditionalGeneration.from_pretrained(model_name)
+def extract_article_text(url):
+  data = wikipedia.page(pageid=url).content
+  return text
 
-def summarize(inp):
-    inp = inp.replace('\n','')
-    inp = tokenizer.encode(inp, return_tensors='pt', max_length=1024)
-    summary_ids = model.generate(inp, num_beams=4, min_length=200, max_length=300, early_stopping=True)
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    return summary
+extractor = gr.Interface(extract_article_text, 'text', 'text')
+summarizer = gr.Interface.load("huggingface/facebook/bart-large-cnn")
 
-gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=7, label="Input Text"), outputs="text").launch(inline=False)
+sample_url = [[1691376],[45199431]]
+           
+description =  '''
+        This application summarizes a news article based on the sentences used in the article. 
+        Enter a news article link and submit for a summary. 
+        A shorter news article produces a faster summary. A news article behind a paywall may produce an error.
+        '''
+  
+iface = Series(extractor, summarizer, 
+  inputs = gr.inputs.Textbox(
+      lines = 2,
+      label = 'URL'
+  ),
+  outputs = 'text',
+  title = 'Extractive News Summarizer BART',
+  theme = 'grass',
+  description = description,
+  examples=sample_url)
+  
+iface.launch()