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from argparse import ArgumentParser
from typing import *
from flask import Flask
from flask import request
from sftp import SpanPredictor, Span
parser = ArgumentParser()
parser.add_argument('model', metavar='MODEL_PATH', type=str)
parser.add_argument('-p', metavar='PORT', type=int, default=7749)
parser.add_argument('-d', metavar='DEVICE', type=int, default=-1)
args = parser.parse_args()
template = open('tools/demo/flask_template.html').read()
predictor = SpanPredictor.from_path(args.model, cuda_device=args.d)
app = Flask(__name__)
default_sentence = '因为 आरजू です vegan , هي купил soja .'
def visualized_prediction(inputs: List[str], prediction: Span, prefix=''):
spans = list()
span2event = [[] for _ in inputs]
for event_idx, event in enumerate(prediction):
for arg_idx, arg in enumerate(event):
for token_idx in range(arg.start_idx, arg.end_idx+1):
span2event[token_idx].append((event_idx, arg_idx))
for token_idx, token in enumerate(inputs):
class_labels = ' '.join(
['token'] + [f'{prefix}-arg-{event_idx}-{arg_idx}' for event_idx, arg_idx in span2event[token_idx]]
)
spans.append(f'<span id="{prefix}-token-{token_idx}" class="{class_labels}" style="background-color">{token} </span>\n')
for event_idx, event in enumerate(prediction):
spans[event.start_idx] = (
f'<span class="highlight bottom blue" '
f' onmouseenter="highlight_args({event_idx}, \'{prefix}\')" onmouseleave="cancel_highlight(\'{prefix}\')">'
'<span class="highlight__content" align="center">'
f'<span class="event" id="{prefix}-event-{event_idx}">'
+ spans[event.start_idx]
)
spans[event.end_idx] += f'</span></span><span class="highlight__label"><center>{event.label}</center></span>'
arg_tips = []
for arg_idx, arg in enumerate(event):
arg_tips.append(f'<span class="{prefix}-arg-{event_idx}-{arg_idx}">{arg.label}</span>')
if len(arg_tips) > 0:
arg_tips = '<br>'.join(arg_tips)
spans[event.end_idx] += f'<span class="highlight__tooltip">{arg_tips}</span>\n'
spans[event.end_idx] += '\n</span>'
return(
'<div class="passage model__content__summary highlight-container highlight-container--bottom-labels">\n' +
'\n'.join(spans) + '\n</div>'
)
def structured_prediction(inputs, prediction):
ret = list()
for event in prediction:
event_text, event_label = ' '.join(inputs[event.start_idx: event.end_idx+1]), event.label
ret.append(f'<li class="list-group-item list-group-item-info">'
f'<strong>{event_label}</strong>: {event_text}</li>')
for arg in event:
arg_text = ' '.join(inputs[arg.start_idx: arg.end_idx+1])
ret.append(
f'<li class="list-group-item"> <strong>{arg.label}</strong>: {arg_text}</li>'
)
content = '\n'.join(ret)
return '\n<ul class="list-group">\n' + content + '\n</ul>'
@app.route('/')
def sftp():
ret = template
tokens = request.args.get('sentence')
if tokens is not None:
ret = ret.replace('DEFAULT_SENTENCE', tokens)
sentences = tokens.split('\n')
model_outputs = predictor.predict_batch_sentences(sentences, max_tokens=512)
vis_pred, str_pred = list(), list()
for sent_idx, output in enumerate(model_outputs):
vis_pred.append(visualized_prediction(output.sentence, output.span, f'sent{sent_idx}'))
str_pred.append(structured_prediction(output.sentence, output.span))
ret = ret.replace('VISUALIZED_PREDICTION', '<hr>'.join(vis_pred))
ret = ret.replace('STRUCTURED_PREDICTION', '<hr>'.join(str_pred))
else:
ret = ret.replace('DEFAULT_SENTENCE', default_sentence)
ret = ret.replace('VISUALIZED_PREDICTION', '')
ret = ret.replace('STRUCTURED_PREDICTION', '')
return ret
app.run(port=args.p)
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