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1717bf9
1
Parent(s):
5c533e1
improved layout
Browse files
app.py
CHANGED
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@@ -44,20 +44,31 @@ config['er_dir'] = os.path.join(model_dir, 'evidence_retrieval/')
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from src.loren import Loren
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loren = Loren(config)
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try:
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js = loren.check('Donald Trump won the 2020 U.S. presidential election.')
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except Exception as e:
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raise ValueError(e)
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def gradio_formatter(js, output_type):
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if output_type == 'e':
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data = {'Evidence': [f'* {x}' for x in js['evidence']]}
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elif output_type == 'z':
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data = {
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'Claim Phrase':
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'Local Premise': [
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'p_SUP': ['%.4f' % x[2] for x in js['phrase_veracity']],
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'p_REF': ['%.4f' % x[0] for x in js['phrase_veracity']],
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'p_NEI': ['%.4f' % x[1] for x in js['phrase_veracity']],
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@@ -72,6 +83,8 @@ def gradio_formatter(js, output_type):
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html = pt.get_html_string(attributes={
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'style': 'border-width: 2px; bordercolor: black'
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}, format=True)
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return html
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@@ -88,16 +101,16 @@ def run(claim):
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js = loren.check(claim)
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ev_html = gradio_formatter(js, 'e')
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z_html = gradio_formatter(js, 'z')
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return
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iface = gr.Interface(
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fn=run,
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inputs="text",
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outputs=[
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'html',
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'html',
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'label',
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'json'
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],
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examples=['Donald Trump won the U.S. 2020 presidential election.',
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@@ -112,8 +125,8 @@ iface = gr.Interface(
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"*Note that the demo system directly retrieves evidence from an up-to-date Wikipedia, which is different from the evidence used in the paper.",
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flagging_dir='results/flagged/',
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allow_flagging=True,
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flagging_options=['Good Case!', 'Error:
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'Error: Commonsense', 'Error: Evidence', 'Error: Other'],
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enable_queue=True
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)
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iface.launch()
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from src.loren import Loren
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loren = Loren(config, verbose=False)
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try:
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js = loren.check('Donald Trump won the 2020 U.S. presidential election.')
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except Exception as e:
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raise ValueError(e)
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def highlight_phrase(text, phrase):
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return text.replace('<mask>', f'<i><b>{phrase}</b></i>')
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def gradio_formatter(js, output_type):
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zebra_css = '''
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tr:nth-child(even) {
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background: #f1f1f1;
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}
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thead{
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background: #f1f1f1;
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}'''
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if output_type == 'e':
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data = {'Evidence': [f'* {x}' for x in js['evidence']]}
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elif output_type == 'z':
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data = {
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'Claim Phrase': js['claim_phrases'],
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'Local Premise': [highlight_phrase(q, x[0]) for q, x in zip(js['cloze_qs'], js['evidential'])],
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'p_SUP': ['%.4f' % x[2] for x in js['phrase_veracity']],
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'p_REF': ['%.4f' % x[0] for x in js['phrase_veracity']],
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'p_NEI': ['%.4f' % x[1] for x in js['phrase_veracity']],
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html = pt.get_html_string(attributes={
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'style': 'border-width: 2px; bordercolor: black'
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}, format=True)
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html = f'<head> <style type="text/css"> {zebra_css} </style> </head>\n' + html
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html = html.replace('<', '<').replace('>', '>')
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return html
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js = loren.check(claim)
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ev_html = gradio_formatter(js, 'e')
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z_html = gradio_formatter(js, 'z')
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return js['claim_veracity'], z_html, ev_html, js
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iface = gr.Interface(
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fn=run,
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inputs="text",
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outputs=[
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'label',
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'html',
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'html',
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'json'
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],
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examples=['Donald Trump won the U.S. 2020 presidential election.',
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"*Note that the demo system directly retrieves evidence from an up-to-date Wikipedia, which is different from the evidence used in the paper.",
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flagging_dir='results/flagged/',
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allow_flagging=True,
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flagging_options=['Good Case!', 'Error: Local Premise', 'Error: Claim Phrase Parsing',
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'Error: Commonsense Claim', 'Error: Inconsistent Phrase and Claim Veracity', 'Error: Evidence Retrieval', 'Error: Other'],
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enable_queue=True
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)
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iface.launch()
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