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import os |
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import gradio as gr |
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from huggingface_hub import snapshot_download |
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from prettytable import PrettyTable |
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import pandas as pd |
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import torch |
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import traceback |
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config = { |
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"model_type": "roberta", |
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"model_name_or_path": "roberta-large", |
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"logic_lambda": 0.5, |
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"prior": "random", |
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"mask_rate": 0.0, |
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"cand_k": 1, |
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"max_seq1_length": 256, |
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"max_seq2_length": 128, |
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"max_num_questions": 8, |
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"do_lower_case": False, |
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"seed": 42, |
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"n_gpu": torch.cuda.device_count(), |
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} |
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os.system('git clone https://github.com/kkpathak91/project_metch/') |
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os.system('rm -r project_metch/data/') |
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os.system('rm -r project_metch/results/') |
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os.system('rm -r project_metch/models/') |
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os.system('mv project_metch/* ./') |
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model_dir = snapshot_download('kkpathak91/FVM') |
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config['fc_dir'] = os.path.join(model_dir, 'fact_checking/roberta-large/') |
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config['mrc_dir'] = os.path.join(model_dir, 'mrc_seq2seq/bart-base/') |
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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, 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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text = loren.fc_client.tokenizer.clean_up_tokenization(text) |
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return text.replace('<mask>', f'<i><b>{phrase}</b></i>') |
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def highlight_entity(text, entity): |
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return text.replace(entity, f'<i><b>{entity}</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': [highlight_entity(x, e) for x, e in zip(js['evidence'], js['entities'])]} |
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elif output_type == 'z': |
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p_sup, p_ref, p_nei = [], [], [] |
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for x in js['phrase_veracity']: |
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max_idx = torch.argmax(torch.tensor(x)).tolist() |
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x = ['%.4f' % xx for xx in x] |
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x[max_idx] = f'<i><b>{x[max_idx]}</b></i>' |
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p_sup.append(x[2]) |
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p_ref.append(x[0]) |
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p_nei.append(x[1]) |
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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': p_sup, |
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'p_REF': p_ref, |
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'p_NEI': p_nei, |
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} |
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else: |
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raise NotImplementedError |
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data = pd.DataFrame(data) |
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pt = PrettyTable(field_names=list(data.columns), |
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align='l', border=True, hrules=1, vrules=1) |
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for v in data.values: |
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pt.add_row(v) |
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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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def run(claim): |
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try: |
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js = loren.check(claim) |
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except Exception as error_msg: |
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exc = traceback.format_exc() |
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msg = f'[Error]: {error_msg}.\n[Traceback]: {exc}' |
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loren.logger.error(claim) |
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loren.logger.error(msg) |
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return 'Oops, something went wrong.', '', '' |
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label = js['claim_veracity'] |
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loren.logger.warning(label + str(js)) |
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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 label, z_html, ev_html |
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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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'text', |
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'html', |
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'html', |
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], |
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examples=['Kanpur is a city in Nepal', |
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'PV Sindhu is an Indian Badminton Player.'], |
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title="A Framework for Data-Driven Document Evaluation and Scoring", |
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layout='horizontal', |
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description="[Student Name: Karan Kumar Pathak] " " [Roll No.: 2020fc04334] ", |
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flagging_dir='results/flagged/', |
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allow_flagging=True, |
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flagging_options=['Interesting!', 'Error: Claim Phrase Parsing', 'Error: Local Premise', |
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'Error: Require Commonsense', 'Error: Evidence Retrieval'], |
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enable_queue=True |
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) |
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iface.launch() |
