Upload app.py
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app.py
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from transformers import pipeline
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import wikipedia
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import random
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import gradio as gr
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model_name = "deepset/electra-base-squad2"
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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def get_wiki_article(topic):
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topic=topic
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try:
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search = wikipedia.search(topic, results = 1)[0]
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except wikipedia.DisambiguationError as e:
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choices = [x for x in e.options if ('disambiguation' not in x) and ('All pages' not in x) and (x!=topic)]
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search = random.choice(choices)
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try:
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p = wikipedia.page(search)
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except wikipedia.exceptions.DisambiguationError as e:
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choices = [x for x in e.options if ('disambiguation' not in x) and ('All pages' not in x) and (x!=topic)]
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s = random.choice(choices)
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p = wikipedia.page(s)
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return p.content, p.url
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def get_answer(topic, question):
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w_art, w_url=get_wiki_article(topic)
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qa = {'question': question, 'context': w_art}
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res = nlp(qa)
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return res['answer'], w_url, {'confidence':res['score']}
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inputs = [
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gr.inputs.Textbox(lines=2, label="Topic"),
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gr.inputs.Textbox(lines=2, label="Question")
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]
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outputs = [
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gr.outputs.Textbox(type='str',label="Answer"),
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gr.outputs.Textbox(type='str',label="Wikipedia Reference Article"),
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gr.outputs.Label(type="confidences",label="Confidence in answer (assuming the correct wikipedia article)"),
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]
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title = "AI Wikipedia Search"
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description = 'Contextual Question and Answer'
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article = ''
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examples = [
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['Quantum', 'What is quanta in physics?'],
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['Cicero', 'What quotes did Marcus Tullius Cicero make?'],
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['Alzheimers', 'What causes alzheimers?'],
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['Neuropathy', 'With neuropathy and neuro-muskoskeletal issues, and what are the treatments available?'],
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['Chemotherapy', 'What are possible care options for patients in chemotherapy?'],
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['Health', 'What is mindfulness and how does it affect health?'],
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['Medicine', 'In medicine what is the Hippocratic Oath?'],
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['Insurance', 'What is Medicare?'],
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['Financial Services', 'Does Medicaid offer financial assistance?'],
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['Ontology', 'Why is an anthology different than ontology?'],
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['Taxonomy', 'What is a biology taxonomy?'],
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['Pharmacy', 'What does a pharmacist do?']
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]
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gr.Interface(get_answer, inputs, outputs, title=title, description=description, article=article, examples=examples, flagging_options=["strongly related","related", "neutral", "unrelated", "strongly unrelated"]).launch(share=False,enable_queue=False)
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