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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 | |
| } | |
| 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() | |