Spaces:

sbudni
/

sk

Runtime error

App Files Files Community

sk / app.py

sbudni

Updated fixed context

9072259 over 1 year ago

raw history blame contribute delete

No virus

2.9 kB


	import gradio as gr
	import numpy as np
	import torch
	from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

	from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering

	# Load model & tokenizer
	# model_name = "deepset/roberta-base-squad2"
	# model_name = "AmazonScience/qanlu"
	# model_name = 'distilbert-base-cased-distilled-squad'
	model_name = "bert-large-uncased-whole-word-masking-finetuned-squad"
	# tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
	tokenizer = AutoTokenizer.from_pretrained(model_name)

	# tokenizer = DistilBertTokenizer.from_pretrained(model_name)
	# model = TFDistilBertForQuestionAnswering.from_pretrained(model_name)

	# model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
	model = AutoModelForQuestionAnswering.from_pretrained(model_name)

	def QA_function(context, question):
	inputs = tokenizer(question, context, add_special_tokens=True, return_tensors="pt") # pt for PyTorch tf for tensorflow
	input_ids = inputs["input_ids"].tolist()[0]

	outputs = model(**inputs)
	answer_start_scores = outputs.start_logits
	answer_end_scores = outputs.end_logits

	## following for torch
	# Get the most likely beginning of answer with the argmax of the score
	answer_start_index = torch.argmax(answer_start_scores)
	# Get the most likely end of answer with the argmax of the score
	answer_end_index = torch.argmax(answer_end_scores) + 1

	answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start_index:answer_end_index]))
	return answer

	# gradio_ui = gr.Interface(QA_function, [gr.inputs.Textbox(lines=7, label="Context"), gr.inputs.Textbox(label="Question")], gr.outputs.Textbox(label="Answer"))



	# a) Get predictions
	nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
	fixed_context = """Ishaan is 6 year old kid. he is very good in football. He is very good sports person.
	he is smart kid. He can run very fast. as fast as 10 meters in 1 minute.
	He goes to Vidyani ketan school. He goes to school from 8 am to 3:30 pm.
	Ishaan has many friends. Vineet is Ishaan's brother. """

	# def get_answer(fixed_context,question):
	# QA_input = {
	# 'question': question,
	# 'context': fixed_context
	# }
	# res = nlp(QA_input)
	# return res['answer']

	def get_answer(question):
	QA_input = {
	'question': question,
	'context': fixed_context
	}
	res = nlp(QA_input)
	return res['answer']

	# gradio_ui = gr.Interface(get_answer, [gr.inputs.Textbox(lines=7, label="Context"), gr.inputs.Textbox(label="Question")], gr.outputs.Textbox(label="Answer"))
	gradio_ui = gr.Interface(get_answer, [gr.inputs.Textbox(label="Question")], gr.outputs.Textbox(label="Answer"))

	gradio_ui.launch()