app.py

import gradio as gr
from transformers import BertForQuestionAnswering
from transformers import BertTokenizerFast

tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def get_prediction(context, question):
  inputs = tokenizer.encode_plus(question, context, return_tensors='pt').to(device)
  outputs = model(**inputs)
  
  answer_start = torch.argmax(outputs[0])  
  answer_end = torch.argmax(outputs[1]) + 1 
  
  answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end]))
  
  return answer

def normalize_text(s):
  """Removing articles and punctuation, and standardizing whitespace are all typical text processing steps."""
  import string, re
  def remove_articles(text):
    regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
    return re.sub(regex, " ", text)
  def white_space_fix(text):
    return " ".join(text.split())
  def remove_punc(text):
    exclude = set(string.punctuation)
    return "".join(ch for ch in text if ch not in exclude)
  def lower(text):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(s))))

def exact_match(prediction, truth):
    return bool(normalize_text(prediction) == normalize_text(truth))

def compute_f1(prediction, truth):
  pred_tokens = normalize_text(prediction).split()
  truth_tokens = normalize_text(truth).split()
  
  # if either the prediction or the truth is no-answer then f1 = 1 if they agree, 0 otherwise
  if len(pred_tokens) == 0 or len(truth_tokens) == 0:
    return int(pred_tokens == truth_tokens)
  
  common_tokens = set(pred_tokens) & set(truth_tokens)
  
  # if there are no common tokens then f1 = 0
  if len(common_tokens) == 0:
    return 0
  
  prec = len(common_tokens) / len(pred_tokens)
  rec = len(common_tokens) / len(truth_tokens)
  
  return round(2 * (prec * rec) / (prec + rec), 2)
  
def question_answer(context, question):
  prediction = get_prediction(context,question)
  return prediction
    
def greet(texts):
    question = texts[:len(texts)]
    answer = texts[len(texts):]
    for question, answer in texts:
        question_answer(context, question)
    return texts

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()