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import os
import random
import gradio as gr
import pandas as pd
import requests
from pyabsa import download_all_available_datasets, AspectTermExtraction as ATEPC, TaskCodeOption
from pyabsa.utils.data_utils.dataset_manager import detect_infer_dataset
download_all_available_datasets()
dataset_items = {dataset.name: dataset for dataset in ATEPC.ATEPCDatasetList()}
def get_example(dataset):
task = TaskCodeOption.Aspect_Polarity_Classification
dataset_file = detect_infer_dataset(dataset_items[dataset], task)
for fname in dataset_file:
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('$LABEL$')].replace('[B-ASP]', '').replace('[E-ASP]', '').strip()
return sorted(set(lines), key=lines.index)
dataset_dict = {dataset.name: get_example(dataset.name) for dataset in ATEPC.ATEPCDatasetList()}
aspect_extractor = ATEPC.AspectExtractor(checkpoint='multilingual')
def perform_inference(text, dataset):
if not text:
text = dataset_dict[dataset][random.randint(0, len(dataset_dict[dataset]) - 1)]
result = aspect_extractor.predict(example=text,
pred_sentiment=True)
result = pd.DataFrame({
'aspect': result['aspect'],
'sentiment': result['sentiment'],
# 'probability': result[0]['probs'],
'confidence': [round(x, 4) for x in result['confidence']],
'position': result['position']
})
return result, '{}'.format(text)
demo = gr.Blocks()
with demo:
gr.Markdown("# <p align='center'>Multilingual Aspect-based Sentiment Analysis !</p>")
gr.Markdown("""### Repo: [PyABSA V2](https://github.com/yangheng95/PyABSA)
### 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 trainer. However, you can try longer text in self-trainer ")
gr.Markdown("**You don't need to split each Chinese (Korean, etc.) token as the provided, just input the natural language text.**")
output_dfs = []
with gr.Row():
with gr.Column():
input_sentence = gr.Textbox(placeholder='Leave this box blank and choose a dataset will give you a random example...', 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 ATEPC.ATEPCDatasetList()[:-1]], value='Laptop14', label="Datasets")
inference_button = gr.Button("Let's go!")
gr.Markdown("There is a [demo](https://huggingface.co/spaces/yangheng/PyABSA-ATEPC-Chinese) specialized for the Chinese langauge")
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_text = gr.TextArea(label="Example:")
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, output_text])
demo.launch()
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