Spaces:
Sleeping
Sleeping
Sean MacAvaney
commited on
Commit
•
7e8e133
1
Parent(s):
4692c69
update
Browse files
app.py
CHANGED
@@ -5,6 +5,7 @@ pt.init()
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from pyterrier_gradio import Demo, MarkdownFile, interface, df2code, code2md, EX_Q
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retr = pt.TerrierRetrieve.from_dataset('vaswani', 'terrier_stemmed')
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COLAB_NAME = 'pyterrier_retrieve.ipynb'
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COLAB_INSTALL = '''
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@@ -33,15 +34,29 @@ retr({df2code(input)})'''
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res['score'] = res['score'].map(lambda x: round(x, 2))
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return (res, code2md(code, COLAB_INSTALL, COLAB_NAME))
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interface(
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MarkdownFile('README.md'),
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Demo(
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predict,
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['1', 'measurement of dielectric constant of liquids by the use of microwave techniques'],
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['2', 'mathematical analysis and design details of waveguide fed microwave radiations'],
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['3', 'use of digital computers in the design of band pass filters having given phase and attenuation characteristics'],
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], columns=['qid', 'query']),
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[
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gr.Dropdown(
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choices=['vaswani stemmed'],
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@@ -64,5 +79,12 @@ interface(
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)],
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scale=2/3
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),
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MarkdownFile('wrapup.md'),
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).launch(share=False)
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from pyterrier_gradio import Demo, MarkdownFile, interface, df2code, code2md, EX_Q
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retr = pt.TerrierRetrieve.from_dataset('vaswani', 'terrier_stemmed')
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sdm = pt.rewrite.SDM()
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COLAB_NAME = 'pyterrier_retrieve.ipynb'
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COLAB_INSTALL = '''
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res['score'] = res['score'].map(lambda x: round(x, 2))
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return (res, code2md(code, COLAB_INSTALL, COLAB_NAME))
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def predict_sdm(input):
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code = f'''import pandas as pd
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import pyterrier as pt ; pt.init()
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sdm = pt.rewrite.SDM()
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sdm({df2code(input)})
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'''
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res = sdm(input)
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return (res, code2md(code, COLAB_INSTALL, COLAB_NAME))
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Q = pd.DataFrame([
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['1', 'measurement of dielectric constant of liquids by the use of microwave techniques'],
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['2', 'mathematical analysis and design details of waveguide fed microwave radiations'],
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['3', 'use of digital computers in the design of band pass filters having given phase and attenuation characteristics'],
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], columns=['qid', 'query'])
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interface(
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MarkdownFile('README.md'),
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Demo(
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predict,
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Q,
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[
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gr.Dropdown(
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choices=['vaswani stemmed'],
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)],
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scale=2/3
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),
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MarkdownFile('sdm.md'),
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Demo(
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predict_sdm,
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Q,
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[],
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scale=2/3
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),
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MarkdownFile('wrapup.md'),
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).launch(share=False)
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