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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
from gensim.parsing.preprocessing import STOPWORDS
import wikipedia
import gradio as gr
import nltk
from nltk.tokenize import word_tokenize
import re
nltk.download('punkt')
#model_name = "deepset/roberta-base-squad2"
model_name="jaimin/Bullet_Point"
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def bullete(text, wikipedia_language="en"):
try:
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)
if wikipedia_language:
wikipedia.set_lang(wikipedia_language)
et_page = wikipedia.page(new_txt.replace(" ", ""))
title = et_page.title
content = et_page.content
page_url = et_page.url
linked_pages = et_page.links
text1 = content
except:
return "Please write correct question"
final_out = re.sub(r'\=.+\=', '', text1)
result = list(filter(lambda x: x != '', final_out.split('\n\n')))
answer = []
try:
for i in range(len(result[0].split('.'))):
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': text,
'context': result[0].split('.')[i]
}
res = nlp(QA_input)
print(QA_input)
values = list(res.values())[3]
answer.append(values)
except:
gen_output = []
for i in range(len(answer)):
gen_output.append("* " + answer[i] + ".")
paraphrase = "\n".join(gen_output)
final_answer = paraphrase.replace(" ", " ")
return final_answer
interface = gr.Interface(fn=bullete,
inputs="text",
outputs="text",
title='Bullet Point')
interface.launch(inline=False)
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