Hugging Face's logo

Spaces:
kkpathak91
/
Bits-tech
Runtime error

App Files Community
Bits-tech
File size: 4,135 Bytes 
eb56186
affea88
091be56
eb56186
38d8e22
eb56186
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5a4452e
 
 
 
 
eb56186
8ea1bf3
eb56186
 
 
 
b74a24a
51ec97a
eb56186
b74a24a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4e5079a
 
96cacaf
b74a24a
96cacaf
b74a24a
e8bdaf4
b74a24a
e8bdaf4
b74a24a
e8bdaf4
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
 
"""
Name: karan kumar Pathak  
Email: 2020fc04335@wilp.bits-pilani.com 
"""

import os
import gradio as gr
from huggingface_hub import snapshot_download
from prettytable import PrettyTable
import pandas as pd
import torch
import traceback

config = {
    "model_type": "roberta",
    "model_name_or_path": "roberta-large",
    "logic_lambda": 0.5,
    "prior": "random",
    "mask_rate": 0.0,
    "cand_k": 1,
    "max_seq1_length": 256,
    "max_seq2_length": 128,
    "max_num_questions": 8,
    "do_lower_case": False,
    "seed": 42,
    "n_gpu": torch.cuda.device_count(),
}

os.system('git clone https://github.com/kkpathak91/project_metch/')
os.system('rm -r project_metch/data/')
os.system('rm -r project_metch/results/')
os.system('rm -r project_metch/models/')
os.system('mv project_metch/* ./')

model_dir = snapshot_download('kkpathak91/FVM')
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/')


from src.loren import Loren


loren = Loren(config, verbose=False)
try:
    js = loren.check('Donald Trump won the 2020 U.S. presidential election.')
except Exception as e:
    raise ValueError(e)


def highlight_phrase(text, phrase):
    text = loren.fc_client.tokenizer.clean_up_tokenization(text)
    return text.replace('<mask>', f'<i><b>{phrase}</b></i>')


def highlight_entity(text, entity):
    return text.replace(entity, f'<i><b>{entity}</b></i>')


def gradio_formatter(js, output_type):
    zebra_css = '''
    tr:nth-child(even) {
        background: #f1f1f1;
    }
    thead{
        background: #f1f1f1;
    }'''
    if output_type == 'e':
        data = {'Evidence': [highlight_entity(x, e) for x, e in zip(js['evidence'], js['entities'])]}
    elif output_type == 'z':
        p_sup, p_ref, p_nei = [], [], []
        for x in js['phrase_veracity']:
            max_idx = torch.argmax(torch.tensor(x)).tolist()
            x = ['%.4f' % xx for xx in x]
            x[max_idx] = f'<i><b>{x[max_idx]}</b></i>'
            p_sup.append(x[2])
            p_ref.append(x[0])
            p_nei.append(x[1])

        data = {
            'Claim Phrase': js['claim_phrases'],
            'Local Premise': [highlight_phrase(q, x[0]) for q, x in zip(js['cloze_qs'], js['evidential'])],
            'p_SUP': p_sup,
            'p_REF': p_ref,
            'p_NEI': p_nei,
        }
    else:
        raise NotImplementedError
    data = pd.DataFrame(data)
    pt = PrettyTable(field_names=list(data.columns), 
        align='l', border=True, hrules=1, vrules=1)
    for v in data.values:
        pt.add_row(v)
    html = pt.get_html_string(attributes={
        'style': 'border-width: 2px; bordercolor: black'
    }, format=True)
    html = f'<head> <style type="text/css"> {zebra_css} </style> </head>\n' + html
    html = html.replace('&lt;', '<').replace('&gt;', '>')
    return html


def run(claim):
    try:
        js = loren.check(claim)
    except Exception as error_msg:
        exc = traceback.format_exc()
        msg = f'[Error]: {error_msg}.\n[Traceback]: {exc}'
        loren.logger.error(claim)
        loren.logger.error(msg)
        return 'Oops, something went wrong.', '', ''
    label = js['claim_veracity']
    loren.logger.warning(label + str(js))
    ev_html = gradio_formatter(js, 'e')
    z_html = gradio_formatter(js, 'z')
    return label, z_html, ev_html


iface = gr.Interface(
    fn=run,
    inputs="text",
    outputs=[
        'text',
        'html',
        'html',
    ],
    examples=['Kanpur is a city in Nepal',
              'PV Sindhu is an Indian Badminton Player.'],
    title="A Framework for Data-Driven Document Evaluation and Scoring",
    layout='horizontal',
    description="[Student Name: Karan Kumar Pathak] " " [Roll No.: 2020fc04334] ",
    flagging_dir='results/flagged/',
    allow_flagging='auto',
    flagging_options=['Interesting!', 'Error: Claim Phrase Parsing', 'Error: Local Premise',
                      'Error: Require Commonsense', 'Error: Evidence Retrieval']
)
iface.launch(enable_queue=True)