Upload app.py
Browse files
app.py
ADDED
|
@@ -0,0 +1,58 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from transformers import pipeline
|
| 2 |
+
import wikipedia
|
| 3 |
+
import random
|
| 4 |
+
import gradio as gr
|
| 5 |
+
model_name = "deepset/electra-base-squad2"
|
| 6 |
+
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
|
| 7 |
+
|
| 8 |
+
def get_wiki_article(topic):
|
| 9 |
+
topic=topic
|
| 10 |
+
try:
|
| 11 |
+
search = wikipedia.search(topic, results = 1)[0]
|
| 12 |
+
except wikipedia.DisambiguationError as e:
|
| 13 |
+
choices = [x for x in e.options if ('disambiguation' not in x) and ('All pages' not in x) and (x!=topic)]
|
| 14 |
+
search = random.choice(choices)
|
| 15 |
+
try:
|
| 16 |
+
p = wikipedia.page(search)
|
| 17 |
+
except wikipedia.exceptions.DisambiguationError as e:
|
| 18 |
+
choices = [x for x in e.options if ('disambiguation' not in x) and ('All pages' not in x) and (x!=topic)]
|
| 19 |
+
s = random.choice(choices)
|
| 20 |
+
p = wikipedia.page(s)
|
| 21 |
+
return p.content, p.url
|
| 22 |
+
|
| 23 |
+
def get_answer(topic, question):
|
| 24 |
+
w_art, w_url=get_wiki_article(topic)
|
| 25 |
+
qa = {'question': question, 'context': w_art}
|
| 26 |
+
res = nlp(qa)
|
| 27 |
+
return res['answer'], w_url, {'confidence':res['score']}
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
inputs = [
|
| 31 |
+
gr.inputs.Textbox(lines=2, label="Topic"),
|
| 32 |
+
gr.inputs.Textbox(lines=2, label="Question")
|
| 33 |
+
]
|
| 34 |
+
outputs = [
|
| 35 |
+
gr.outputs.Textbox(type='str',label="Answer"),
|
| 36 |
+
gr.outputs.Textbox(type='str',label="Wikipedia Reference Article"),
|
| 37 |
+
gr.outputs.Label(type="confidences",label="Confidence in answer (assuming the correct wikipedia article)"),
|
| 38 |
+
]
|
| 39 |
+
|
| 40 |
+
title = "AI Wikipedia Search"
|
| 41 |
+
description = 'Contextual Question and Answer'
|
| 42 |
+
article = ''
|
| 43 |
+
examples = [
|
| 44 |
+
['Quantum', 'What is quanta in physics?'],
|
| 45 |
+
['Cicero', 'What quotes did Marcus Tullius Cicero make?'],
|
| 46 |
+
['Alzheimers', 'What causes alzheimers?'],
|
| 47 |
+
['Neuropathy', 'With neuropathy and neuro-muskoskeletal issues, and what are the treatments available?'],
|
| 48 |
+
['Chemotherapy', 'What are possible care options for patients in chemotherapy?'],
|
| 49 |
+
['Health', 'What is mindfulness and how does it affect health?'],
|
| 50 |
+
['Medicine', 'In medicine what is the Hippocratic Oath?'],
|
| 51 |
+
['Insurance', 'What is Medicare?'],
|
| 52 |
+
['Financial Services', 'Does Medicaid offer financial assistance?'],
|
| 53 |
+
['Ontology', 'Why is an anthology different than ontology?'],
|
| 54 |
+
['Taxonomy', 'What is a biology taxonomy?'],
|
| 55 |
+
['Pharmacy', 'What does a pharmacist do?']
|
| 56 |
+
]
|
| 57 |
+
|
| 58 |
+
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)
|