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import gradio as gr
from transformers import BertForQuestionAnswering
from transformers import BertTokenizerFast
import torch
from nltk.tokenize import word_tokenize
import timm
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = timm.create_model('hf_hub:pseudolab/AI_Tutor_BERT', pretrained=True)
#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):
context, question = '', ''
act = False
tmp = ''
for t in text:
tmp += t
if len(tmp) == 4:
tmp = tmp[1:]
if tmp == '///':
act = True
if act == True:
question += t
if act == False:
context += t
return context[:-2], question[1:]
# 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 context
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |