question_answering.py

import torch
from transformers import BertTokenizer, BertForQuestionAnswering

# Load the pre-trained model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")

def answer_query(question, context):
  # Preprocess the question and context using the tokenizer
  inputs = tokenizer(question, context, return_tensors="pt")

  # Use the model for question answering
  with torch.no_grad():
    outputs = model(**inputs)

  # Get start and end logits directly from model outputs
  start_logits = outputs.start_logits
  end_logits = outputs.end_logits

  # Find the most likely answer span
  answer_start = torch.argmax(start_logits)
  answer_end = torch.argmax(end_logits) + 1

  # Extract the answer from the context
  answer = tokenizer.convert_tokens_to_string(context)[answer_start:answer_end]

  return answer