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(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("#

Multilingual Aspect-based Sentiment Analysis !

") gr.Markdown("""### Repo: [PyABSA V2](https://github.com/yangheng95/PyABSA) ### Author: [Heng Yang](https://github.com/yangheng95) (杨恒) [![Downloads](https://pepy.tech/badge/pyabsa)](https://pepy.tech/project/pyabsa) [![Downloads](https://pepy.tech/badge/pyabsa/month)](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()