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Upload app.py

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+ # -*- coding: utf-8 -*-
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+ """Job-coach-testing-site-zeroshot.ipynb
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+
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+ Automatically generated by Colaboratory.
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+
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+ Original file is located at
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+ https://colab.research.google.com/github/vanderbilt-data-science/job-coach-question-answering/blob/111-create-a-gradio-hf-space-to-test-whether-specific-information-is-in-a-coaching-document/job-coach-testing-zeroshot.ipynb
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+ """
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+
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+ import gradio as gr
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+
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+ import torch
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+ from transformers import AutoTokenizer, AutoModelForQuestionAnswering
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+ from transformers import pipeline
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+
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+ classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
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+ question_answerer = pipeline("question-answering", model = "bert-large-uncased-whole-word-masking-finetuned-squad")
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+ def zshot(context, queries):
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+ answered=[]
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+ res=[]
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+ notAnswered=[]
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+ queries=queries.split("?")
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+ queries.pop(-1)
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+ for query in queries:
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+ query.strip()
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+ result = question_answerer(question = query, context=context)
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+ answered.append([query,result['answer']])
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+
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+ for item in answered:
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+ result = ([text, classifier(text, item[0])])
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+ if result[1]['scores'][0] > 0.01:
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+ res.append(item[0] +"? Answer: " + item[1])
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+ else:
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+ notAnswered.append(item[0])
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+ result1=''',
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+ '''.join(res)
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+ result1='''Information Included in the Document:
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+ '''+ result1
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+ result2=''',
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+ '''.join(notAnswered)
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+ result2='''
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+ Information not included in the document, on no clearly stated:
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+ '''+ result2
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+
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+ return result1 + result2
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+
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+
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+ app = gr.Interface(
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+ zshot,
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+ inputs=[
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+ gr.Textbox(label="Context", value=""),
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+ gr.Textbox(label="Queries", value=""),
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+ ],
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+ outputs=["text"],
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+ )
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+ app.launch()
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+
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+ """#### Examples"""