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