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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()
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