import gradio as gr
from transformers import BertForQuestionAnswering
from transformers import BertTokenizerFast
import torch
from nltk.tokenize import word_tokenize

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 question_answer(context, question):
  prediction = get_prediction(context,question)
  return prediction

def split(text):
    # words = word_tokenize(text)
    # context, question = '', ''
    # act = False

    # for w in words:
    #     if w == '///':
    #         act = True
            
    #     if act == False:
    #         context += w + ' '
    #     else:
    #         if w == '///':
    #             w = ''
    #         question += w + ' '

    # context = context[:-1]
    # question = question[1:-1]
    return text, text
    
# def greet(texts):
#     context, question = split(texts)
#     answer = question_answer(context, question)
#     return answer
def greet(text):
    context, question = split(text)
    # answer = question_answer(context, question)
    return word_tokenize(text)

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