Create app.py

app.py

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+from newspaper import Article
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
+model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws")
+
+import nltk
+nltk.download('punkt')
+from nltk.tokenize import sent_tokenize
+
+def my_paraphrase(sentence):
+
+  sentence = "paraphrase: " + sentence + " </s>"
+  encoding = tokenizer.encode_plus(sentence,padding=True, return_tensors="pt")
+  input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
+  
+  outputs = model.generate(
+    input_ids=input_ids, attention_mask=attention_masks,
+    max_length=256,
+    do_sample=True,
+    top_k=120,
+    top_p=0.95,
+    early_stopping=True,
+    num_return_sequences=1)
+  output = tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
+
+  return(output)
+
+def text(url):
+  article = Article(url)
+  article.download()
+  article.parse()
+
+  input_text = article.text
+  output = " ".join([my_paraphrase(sent) for sent in sent_tokenize(input_text)])
+
+  return output
+
+import gradio as gr
+def summarize(URL):
+
+  outputtext = text(URL)
+  return outputtext
+gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=7, placeholder="Enter text here"), outputs=[gr.outputs.Textbox(label="Paraphrased Text")],examples=[["developed by python team"
+]]).launch(inline=False)