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# -*- coding: utf-8 -*-
"""
@Author : Jiangjie Chen
@Time : 2021/12/13 17:17
@Contact : jjchen19@fudan.edu.cn
@Description:
"""
import os
import gradio as gr
from src.loren import Loren
from huggingface_hub import snapshot_download
from prettytable import PrettyTable
import pandas as pd
config = {
"input": "demo",
"model_type": "roberta",
"model_name_or_path": "roberta-large",
"logic_lambda": 0.5,
"prior": "random",
"mask_rate": 0.0,
"cand_k": 3,
"max_seq2_length": 256,
"max_seq1_length": 128,
"max_num_questions": 8,
"do_lower_case": False
}
model_dir = snapshot_download('Jiangjie/loren')
config['fc_dir'] = os.path.join(model_dir, 'fact_checking/roberta-large/')
config['mrc_dir'] = os.path.join(model_dir, 'mrc_seq2seq/bart-base/')
config['er_dir'] = os.path.join(model_dir, 'evidence_retrieval/')
loren = Loren(config)
try:
# js = {
# 'id': 0,
# 'evidence': ['EVIDENCE1', 'EVIDENCE2'],
# 'question': ['QUESTION1', 'QUESTION2'],
# 'claim_phrase': ['CLAIMPHRASE1', 'CLAIMPHRASE2'],
# 'local_premise': [['E1 ' * 100, 'E1' * 100, 'E1' * 10], ['E2', 'E2', 'E2']],
# 'phrase_veracity': [[0.1, 0.5, 0.4], [0.1, 0.7, 0.2]],
# 'claim_veracity': 'SUPPORT'
# }
js = loren.check('Donald Trump won the 2020 U.S. presidential election.')
except Exception as e:
raise ValueError(e)
def gradio_formatter(js, output_type):
if output_type == 'e':
data = {'Evidence': js['evidence']}
elif output_type == 'z':
data = {
'Claim Phrase': js['claim_phrase'],
'Local Premise': [x[0] for x in js['local_premise']],
'p_SUP': [round(x[2], 4) for x in js['phrase_veracity']],
'p_REF': [round(x[0], 4) for x in js['phrase_veracity']],
'p_NEI': [round(x[1], 4) for x in js['phrase_veracity']],
}
else:
raise NotImplementedError
data = pd.DataFrame(data)
pt = PrettyTable(field_names=list(data.columns))
for v in data.values:
pt.add_row(v)
html = pt.get_html_string(attributes={
'style': 'border-width: 1px; border-collapse: collapse',
}, format=True)
return html
def run(claim):
js = loren.check(claim)
ev_html = gradio_formatter(js, 'e')
z_html = gradio_formatter(js, 'z')
return ev_html, z_html, js['claim_veracity'], js
iface = gr.Interface(
fn=run,
inputs="text",
outputs=[
'html',
'html',
'label',
'json'
],
examples=['Donald Trump won the U.S. 2020 presidential election.',
'The first inauguration of Bill Clinton was in the United States.'],
title="LOREN",
layout='vertical',
description="LOREN is an interpretable Fact Verification model against Wikipedia. "
"This is a demo system for \"LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification\". "
"See the paper for technical details. You can add FLAG on the bottom to record interesting or bad cases!",
flagging_dir='results/flagged/',
allow_flagging=True,
flagging_options=['Good Case!', 'Error: MRC', 'Error: Parsing',
'Error: Commonsense', 'Error: Evidence', 'Error: Other'],
enable_queue=True
)
iface.launch()
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