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# https://huggingface.co/transformers/main_classes/pipelines.html
# https://huggingface.co/models?filter=conversational
# Install Dependences
# Use my Conda qna environment, then you're all set
# !pip install transformers
# !pip install ipywidgets
# !pip install gradio # see setup for installing gradio
# Import Dependencies
from transformers import pipeline
import gradio as gr
# Create the Q&A pipeline
nlp = pipeline('question-answering', model='deepset/roberta-base-squad2', tokenizer='deepset/roberta-base-squad2')
#nlp = pipeline('question-answering', model='bert-large-uncased-whole-word-masking-finetuned-squad ', tokenizer='bert-large-uncased-whole-word-masking-finetuned-squad ')
#nlp = pipeline("question-answering", model='distilbert-base-cased-distilled-squad')
#nlp = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad')
def question_answer(context_filename, question):
"""Produce a NLP response based on the input text filename and question."""
with open(context_filename) as f:
context = f.read()
nlp_input = {'question': question, 'context': context}
result = nlp(nlp_input)
return result['answer']
demo = gr.Interface(
fn=question_answer,
#inputs=gr.inputs.Textbox(lines=2, placeholder='Enter your question'),
inputs=[
gr.Dropdown([
'spiderman.txt',
'world-john.txt',
'world-romans.txt',
'world-nt.txt',
'world-ot.txt']), # 'lotr01.txt'
"text"
],
outputs="textbox")
demo.launch(share=True)
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