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# -*- coding: utf-8 -*-
# file: deploy_demo.py
# time: 2021/10/10
# author: yangheng <hy345@exeter.ac.uk>
# github: https://github.com/yangheng95
# Copyright (C) 2021. All Rights Reserved.
import gradio as gr
import pandas as pd
from pyabsa import APCCheckpointManager
sentiment_classifier = APCCheckpointManager.get_sentiment_classifier(checkpoint='multilingual',
auto_device=True # False means load model on CPU
)
def inference(text):
result = sentiment_classifier.infer(text=text,
print_result=True,
ignore_error=False,
clear_input_samples=True)
result = pd.DataFrame({
'aspect': result['aspect'],
'sentiment': result['sentiment'],
'confidence': [round(c, 3) for c in result['confidence']],
'ref_sentiment': ['' if ref == '-999' else ref for ref in result['ref_sentiment']],
'is_correct': result['ref_check'],
})
return result
if __name__ == '__main__':
iface = gr.Interface(
fn=inference,
inputs=["text"],
examples=[
['Strong build though which really adds to its [ASP]durability[ASP] .'], # !sent! Positive
['Strong [ASP]build[ASP] though which really adds to its durability . !sent! Positive'],
['The [ASP]battery life[ASP] is excellent - 6-7 hours without charging . !sent! Positive'],
['I have had my [ASP]computer[ASP] for 2 weeks already and it [ASP]works[ASP] perfectly . !sent! Positive, Positive'],
['And I may be the only one but I am really liking [ASP]Windows 8[ASP] . !sent! Positive'],
['This demo is trained on the laptop and restaurant and other review datasets from [ASP]ABSADatasets[ASP] (https://github.com/yangheng95/ABSADatasets)'],
['To fit on your data, please train the model on your own data, see the [ASP]PyABSA[ASP] (https://github.com/yangheng95/PyABSA)'],
],
outputs="dataframe",
title='Multilingual Aspect Sentiment Classification for Short Texts (powered by PyABSA)'
)
iface.launch(share=True)
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