File size: 3,819 Bytes
0181e0b 9cd92fc 0181e0b 9cd92fc f1fde39 0181e0b f1fde39 0181e0b f1fde39 0181e0b f1fde39 0181e0b 167c46c 9cd92fc 0181e0b 13b5184 9cd92fc 4a829e5 9cd92fc 995e059 0181e0b 13b5184 0181e0b 167c46c 0181e0b e098ae7 0181e0b e098ae7 0181e0b |
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 |
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, detect_infer_dataset
download_datasets_from_github(os.getcwd())
dataset_items = {dataset.name: dataset for dataset in ABSADatasetList()}
def get_example(dataset):
task = 'apc'
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('!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='multilingual')
def perform_inference(text, dataset):
if not text:
text = dataset_dict[dataset][random.randint(0, len(dataset_dict[dataset])-1)]
result = aspect_extractor.extract_aspect(inference_source=[text],
pred_sentiment=True)
result = pd.DataFrame({
'aspect': result[0]['aspect'],
'sentiment': result[0]['sentiment'],
# 'probability': result[0]['probs'],
'confidence': [round(x, 4) for x in result[0]['confidence']],
'position': result[0]['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](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("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])
gr.Markdown("")
demo.launch(share=True)
|