app/pyfiles/qa_gen.py

import os
from pathlib import Path
import numpy as np
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer 

current_path = os.path.dirname(os.path.abspath(__file__))
tokenizer_path = os.path.join(current_path, "gpt_tokenizer")
model_path = os.path.join(current_path, "gpt2_qa_model")
tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path) # also try gpt2-medium
model = GPT2LMHeadModel.from_pretrained(model_path)
def generate_text(sequence, max_new_tokens):
    ids = tokenizer.encode(f'{sequence}', return_tensors='pt')
    input_length = ids.size(1)
    max_length = input_length + max_new_tokens
    final_outputs = model.generate(
        ids,
        do_sample=True,
        max_length=max_length,
        pad_token_id=model.config.eos_token_id
    )
    return tokenizer.decode(final_outputs[0], skip_special_tokens=True)


def question_awnser(prompt: str):
    result = generate_text("Question: " + prompt + "Answer: ", 35).split('Answer: ')[1]
    try:
        result = result.split('.')[0] + '.'
    except Exception as e:
        print(e)
    return result