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| import gradio as gr | |
| import gensim | |
| print(gensim.__version__) | |
| import transformers | |
| import sacremoses # for back translation tokenizer | |
| import nlpaug.augmenter.char as nac | |
| import nlpaug.augmenter.word as naw | |
| import nlpaug.augmenter.sentence as nas | |
| import nlpaug.flow as nafc | |
| from nlpaug.util import Action | |
| from nlpaug.util.file.download import DownloadUtil | |
| DownloadUtil.download_word2vec(dest_dir = '.') | |
| # Possible values are ‘wiki-news-300d-1M’, ‘wiki-news-300d-1M-subword’, ‘crawl-300d-2M’ and ‘crawl-300d-2M-subword’ | |
| DownloadUtil.download_fasttext(dest_dir = '.', model_name = 'crawl-300d-2M') | |
| # for synonym replacement | |
| DownloadUtil.download_glove(dest_dir = '.', model_name = 'glove.6B') | |
| # augmentations | |
| def augment_text(text, aug_type, model_type=None, model_path=None, aug_p=0.25, aug_max=3): | |
| if aug_type == 'Word Embedding Substitution': | |
| aug = naw.WordEmbsAug( | |
| model_type=model_type, | |
| model_path=model_path, | |
| action="substitute", | |
| aug_p=aug_p | |
| ) | |
| elif aug_type == 'Contextual Insertion': | |
| aug = naw.ContextualWordEmbsAug( | |
| model_path='bert-base-uncased', | |
| action="insert", | |
| aug_p=aug_p | |
| ) | |
| elif aug_type == 'Synonym Replacement': | |
| aug = naw.SynonymAug( | |
| aug_src="wordnet", | |
| aug_max=aug_max | |
| ) | |
| elif aug_type == 'Back Translation': | |
| aug = naw.BackTranslationAug( | |
| from_model_name='facebook/wmt19-en-de', | |
| to_model_name='facebook/wmt19-de-en' | |
| ) | |
| else: | |
| return text | |
| augmented_text = aug.augment(text) | |
| return augmented_text | |
| with gr.Blocks() as iface: | |
| text_input = gr.Textbox(label="Input Text") | |
| aug_type_input = gr.Radio( | |
| choices=['Word Embedding Substitution', 'Contextual Insertion', 'Synonym Replacement', 'Back Translation'], | |
| label="Augmentation Type", | |
| value='Word Embedding Substitution' | |
| ) | |
| model_type_input = gr.Dropdown( | |
| choices=['word2vec', 'fasttext', 'glove'], | |
| label="Model Type (for Word Embedding Substitution)", | |
| value='word2vec', | |
| visible=True | |
| ) | |
| model_path_input = gr.Textbox( | |
| label="Model Path (for Word Embedding Substitution)", | |
| value="GoogleNews-vectors-negative300.bin", | |
| visible=True | |
| ) | |
| aug_p_input = gr.Slider( | |
| minimum=0, maximum=1, step=0.05, value=0.25, | |
| label="Probability of Augmentation (for Embedding Substitution or Contextual Insertion)" | |
| ) | |
| aug_max_input = gr.Slider( | |
| minimum=1, maximum=10, step=1, value=3, | |
| label="Max Number of Words to Change (for Synonym Replacement)", | |
| visible=False | |
| ) | |
| augmented_output = gr.Textbox(label="Augmented Text") | |
| # update input block visibility based on aug type | |
| def update_inputs(aug_type): | |
| if aug_type == 'Word Embedding Substitution': | |
| return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False) | |
| elif aug_type == 'Contextual Insertion': | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) | |
| elif aug_type == 'Synonym Replacement': | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) | |
| elif aug_type == 'Back Translation': | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) | |
| # update inputs when aug type changes | |
| aug_type_input.change( | |
| update_inputs, | |
| inputs=[aug_type_input], | |
| outputs=[model_type_input, model_path_input, aug_max_input] | |
| ) | |
| apply_button = gr.Button("Apply Augmentation") | |
| apply_button.click( | |
| augment_text, | |
| inputs=[text_input, aug_type_input, model_type_input, model_path_input, aug_p_input, aug_max_input], | |
| outputs=[augmented_output] | |
| ) | |
| iface.launch() | |