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import tensorflow as tf
#!pip install transformers
from transformers import pipeline
# importing necessary libraries
from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad",return_dict=False)
nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
#!pip install gradio
import gradio as gr
# creating the function
def func(context, question):
result = nlp(question = question, context=context)
return result['answer']
example_1 = "(1) My name is Ajulor Christian, I am a data scientist and machine learning engineer"
qst_1 = "what is christian's profession?"
example_2 = "(2) Natural Language Processing (NLP) allows machines to break down and interpret human language. It's at the core of tools we use every day β from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools."
qst_2 = "What is NLP used for?"
# creating the interface
app = gr.Interface(fn=func, inputs = ['textbox', 'text'], outputs = 'textbox',
title = 'Question Answering bot', theme = 'dark-grass',
description = 'Input context and question, then get answers!',
examples = [[example_1, qst_1],
[example_2, qst_2]]
)
# launching the app
app.launch(inline=False) |