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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) (ζ¨ζ) | |
| [](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() | |