Create app.py

app.py

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+#importing the necessary library
+import re
+import nltk
+import torch
+from nltk.tokenize import sent_tokenize
+nltk.download('punkt')
+from IPython.display import HTML, display
+import gradio as gr
+from gradio.mix import Parallel
+from transformers import pipeline
+import numpy as np
+
+# Defining a function to read in the text file
+def read_in_text(url):
+  with open(url, 'r') as file:
+    article = file.read()
+    return article
+    
+#initailizing the model pipeline   
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
+tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
+
+#Defining a function to get the summary of the article
+def final_summary(file):
+  #reading in the text and tokenizing it into sentence
+  text = read_in_text(file.name)
+  chunks = sent_tokenize(text)
+  output = []
+
+  #looping through the sentences in a batch of 10 and summarizing them
+  for i in range(0,len(chunks), 20):
+    sentence = ' '.join(chunks[i:i+20])
+    inputs = tokenizer(sentence, max_length=1024, return_tensors="pt")
+    summary_ids = model.generate(inputs["input_ids"])
+    summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]  
+    output.append(summary)
+  
+  #joining all the summary output together
+  summary = ' '.join(output)
+  lines1 = sent_tokenize(summary)
+  for i in range(len(lines1)):
+          lines1[i] = "* " + lines1[i].strip().replace(' .', '.')
+
+  summ_bullet1 = "\n".join(lines1)
+  return summ_bullet1  
+  
+  #creating an interface for the headline generator using gradio
+demo = gr.Interface(final_summary, inputs=[gr.inputs.File(label="Drop your .txt file here", optional=False)],
+                                          title = "ARTICLE SUMMARIZER",
+                                          outputs=[gr.outputs.Textbox(label="Summary")],
+                                          theme= "darkhuggingface")
+                                          
+#launching the app
+if __name__ == "__main__":
+    demo.launch(debug=True)