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app.py

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+import wikipedia
+import gradio as gr
+from gradio.mix import Parallel
+import requests
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.tokenize import sent_tokenize
+import re
+
+
+def opendomain(text):
+  question_words = ["what", "why", "when", "where","name", "is", "how", "do", "does",
+                    "which", "are", "could", "would","should", "has", "have", "whom",
+                    "whose", "don't", "a", "an","?",".","the","i","you","he","she","it",
+                    "that","this",",","am",","]
+  lower_text = text.lower()
+  lower_text = word_tokenize(lower_text)
+  new_text = [i for i in lower_text if i not in question_words]
+  new_txt = "".join(new_text)
+
+  r = requests.post(
+      url="https://jaimin-new-content.hf.space/run/predict",
+      json={"data": [new_txt, "en"]},
+    )
+  response = r.json()
+  text1 = response["data"]
+  final_out = text1[0]
+  final_out=re.sub(r'\=.+\=', '', final_out)
+
+  result = list(filter(lambda x: x != '', final_out.split('\n\n')))
+
+  answer = []
+  for i in range(6):
+      if len(result[i]) > 500:
+          summary_point=result[i].split(".")[0]
+          answer.append(summary_point)
+  l = []
+  for i in range(len(answer)):
+      l.append("".join(answer[i]))
+  gen_output = []
+  for i in range(len(l)):
+      gen_output.append("* " + l[i] + ".")
+  paraphrase = "\n".join(gen_output)
+  final_answer = paraphrase.replace(" ", " ")
+  return final_answer
+
+
+iface = gr.Interface(fn=opendomain, inputs=[gr.inputs.Textbox(lines=5)], outputs="text")
+iface.launch()
+

requirements.txt

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+wikipedia
+requests
+gradio
+nltk