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script.py
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script.py
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import torch
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline,TFAutoModelForQuestionAnswering
import tensorflow
#summary
tokenizers = AutoTokenizer.from_pretrained('t5-base')
models = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)
#qa
modelq = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad",return_dict=False)
tokenizerq = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
nlp = pipeline("question-answering", model=modelq, tokenizer=tokenizerq)
############
sequence=input("\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPaste T&C:\n")
res=0
while res!=3:
print("\n\n\nMenu\n")
print("1)Sumerise T&C\n")
print("2)Ask Questions\n")
print("3)Exit\n\n")
res=int(input("Response:"))
if res==1:
inputs = tokenizers.encode("summarize: " + sequence,
return_tensors='pt',
max_length=512,
truncation=True)
summary_ids = models.generate(inputs, max_length=200, min_length=1, length_penalty=5., num_beams=2)
summary = tokenizers.decode(summary_ids[0])
print("\n\n\n",summary,"\n\n\n")
elif res==2:
context= sequence
question = input("Question:")
result = nlp(question = question, context=context)
#print (f"QUESTION: {question}")
print("\n\n\n")
print(f"ANSWER: {result['answer']}")
print("\n\n\n")
elif res!=3:
print("Invalid option\n\n\n")