Create app.py

app.py

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+from gensim.parsing.preprocessing import STOPWORDS
+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
+nltk.download('punkt')
+
+
+def opendomain(text):
+  question_words = STOPWORDS.union(set(['likes','play','.',',','like',"don't",'?','use','choose','important','better','?']))
+  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] + ".")
+
+  listToStr = ' '.join([str(elem) for elem in gen_output])
+  listToStr = listToStr.replace("\n", "")
+  return listToStr
+  #return final_answer
+
+
+iface = gr.Interface(fn=opendomain, inputs=[gr.inputs.Textbox(lines=5)], outputs="text")
+iface.launch()