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import os
import random
import gradio as gr
import pandas as pd
from findfile import find_files
from pyabsa import ATEPCCheckpointManager
from pyabsa.functional.dataset.dataset_manager import download_datasets_from_github, ABSADatasetList
download_datasets_from_github(os.getcwd())
def get_example(dataset):
filter_key_words = ['.py', '.md', 'readme', 'log', 'result', 'zip', '.state_dict', '.model', '.png', 'acc_', 'f1_', '.origin', '.adv', '.csv']
dataset_file = {'train': [], 'test': [], 'valid': []}
search_path = './'
task = 'apc_datasets'
dataset_file['test'] += find_files(search_path, [dataset, 'test', task, '.inference'], exclude_key=['.adv', '.org', '.defense', 'train.'] + filter_key_words)
for fname in dataset_file['test']:
lines = []
if isinstance(fname, str):
fname = [fname]
for f in fname:
print('loading: {}'.format(f))
fin = open(f, 'r', encoding='utf-8')
lines.extend(fin.readlines())
fin.close()
for i in range(len(lines)):
lines[i] = lines[i][:lines[i].find('!sent!')].replace('[ASP]', '')
return sorted(set(lines), key=lines.index)
dataset_dict = {dataset.name: get_example(dataset.name) for dataset in ABSADatasetList()}
aspect_extractor = ATEPCCheckpointManager.get_aspect_extractor(checkpoint='english')
def perform_inference(text, dataset):
if not text:
text = dataset_dict[dataset][random.randint(0, len(dataset_dict[dataset]))]
result = aspect_extractor.extract_aspect(inference_source=[text],
pred_sentiment=True)
result = pd.DataFrame({
'aspect': result[0]['aspect'],
'sentiment': result[0]['sentiment'],
'position': result[0]['position']
})
return result, text
demo = gr.Blocks()
with demo:
gr.Markdown("# Multilingual Aspect-based Sentiment Analysis!")
gr.Markdown("### Repo: [PyABSA](https://github.com/yangheng95/PyABSA)")
gr.Markdown("""### Author: [Heng Yang](https://github.com/yangheng95) (杨恒)
[](https://pepy.tech/project/pyabsa)
[](https://pepy.tech/project/pyabsa)
"""
)
gr.Markdown("Your input text should be no more than 80 words, that's the longest text we used in training. However, you can try longer text in self-training ")
output_dfs = []
with gr.Row():
with gr.Column():
input_sentence = gr.Textbox(placeholder='Leave blank to give you a random result...', label="Example:")
gr.Markdown("You can find the datasets at [github.com/yangheng95/ABSADatasets](https://github.com/yangheng95/ABSADatasets/tree/v1.2/datasets/text_classification)")
dataset_ids = gr.Radio(choices=[dataset.name for dataset in ABSADatasetList()[:-1]], value='Laptop14', label="Datasets")
inference_button = gr.Button("Let's go!")
gr.Markdown("This demo support many other language as well, you can try and explore the results of other languages by yourself.")
with gr.Column():
output_df = gr.DataFrame(label="Prediction Results:")
output_dfs.append(output_df)
inference_button.click(fn=perform_inference,
inputs=[input_sentence, dataset_ids],
outputs=[output_df, input_sentence])
gr.Markdown("")
demo.launch(share=True)
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