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import os |
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import random |
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import gradio as gr |
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import pandas as pd |
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import requests |
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from pyabsa import ATEPCCheckpointManager |
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from pyabsa.functional.dataset.dataset_manager import download_datasets_from_github, ABSADatasetList, detect_infer_dataset |
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download_datasets_from_github(os.getcwd()) |
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dataset_items = {dataset.name: dataset for dataset in ABSADatasetList()} |
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def get_example(dataset): |
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task = 'apc' |
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dataset_file = detect_infer_dataset(dataset_items[dataset], task) |
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for fname in dataset_file: |
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lines = [] |
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if isinstance(fname, str): |
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fname = [fname] |
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for f in fname: |
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print('loading: {}'.format(f)) |
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fin = open(f, 'r', encoding='utf-8') |
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lines.extend(fin.readlines()) |
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fin.close() |
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for i in range(len(lines)): |
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lines[i] = lines[i][:lines[i].find('!sent!')].replace('[ASP]', '') |
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return sorted(set(lines), key=lines.index) |
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dataset_dict = {dataset.name: get_example(dataset.name) for dataset in ABSADatasetList()} |
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aspect_extractor = ATEPCCheckpointManager.get_aspect_extractor(checkpoint='multilingual-256-2') |
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def perform_inference(text, dataset): |
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if not text: |
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text = dataset_dict[dataset][random.randint(0, len(dataset_dict[dataset]) - 1)] |
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result = aspect_extractor.extract_aspect(inference_source=[text], |
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pred_sentiment=True) |
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result = pd.DataFrame({ |
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'aspect': result[0]['aspect'], |
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'sentiment': result[0]['sentiment'], |
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'confidence': [round(x, 4) for x in result[0]['confidence']], |
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'position': result[0]['position'] |
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}) |
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return result, '{}'.format(text) |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("# <p align='center'>Multilingual Aspect-based Sentiment Analysis !</p>") |
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gr.Markdown("""### Repo: [PyABSA](https://github.com/yangheng95/PyABSA) |
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### Author: [Heng Yang](https://github.com/yangheng95) (ζ¨ζ) |
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## This demo is based on v.1.16.27, while the latest release is [v2.X](https://github.com/yangheng95/PyABSA) |
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[](https://pepy.tech/project/pyabsa) |
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[](https://pepy.tech/project/pyabsa) |
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""" |
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) |
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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 ") |
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gr.Markdown("**You don't need to split each Chinese (Korean, etc.) token as the provided, just input the natural language text.**") |
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output_dfs = [] |
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with gr.Row(): |
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with gr.Column(): |
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input_sentence = gr.Textbox(placeholder='Leave this box blank and choose a dataset will give you a random example...', label="Example:") |
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gr.Markdown("You can find the datasets at [github.com/yangheng95/ABSADatasets](https://github.com/yangheng95/ABSADatasets/tree/v1.2)") |
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dataset_ids = gr.Radio(choices=[dataset.name for dataset in ABSADatasetList()[:-1]], value='Laptop14', label="Datasets") |
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inference_button = gr.Button("Let's go!") |
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gr.Markdown("There is a [demo](https://huggingface.co/spaces/yangheng/PyABSA-ATEPC-Chinese) specialized for the Chinese langauge") |
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gr.Markdown("This demo support many other language as well, you can try and explore the results of other languages by yourself.") |
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with gr.Column(): |
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output_text = gr.TextArea(label="Example:") |
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output_df = gr.DataFrame(label="Prediction Results:") |
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output_dfs.append(output_df) |
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inference_button.click(fn=perform_inference, |
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inputs=[input_sentence, dataset_ids], |
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outputs=[output_df, output_text], |
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api_name='inference') |
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gr.Markdown("") |
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demo.launch() |
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