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| from gensim.models import KeyedVectors | |
| import gradio as gr | |
| word_vectors_path = "classical_bo.wordvectors" | |
| wv = KeyedVectors.load(str(word_vectors_path), mmap='r') | |
| def format_to_html(sim_output): | |
| table = """ | |
| <table> | |
| <tr> | |
| <th>Word</th> | |
| <th>Similarity (0-1)</th> | |
| </tr> | |
| {} | |
| </table> | |
| """ | |
| rows = "" | |
| for word, sim in sim_output: | |
| rows += f"<tr><td>{word}</td><td style='text-align: right'>{round(sim, 4)}</td></tr>" | |
| return table.format(rows) | |
| def find_most_similar(word): | |
| sim_output = wv.most_similar(word) | |
| return format_to_html(sim_output) | |
| def find_similarity(wordA, wordB): | |
| return max([wv.similarity(wordA, wordB), 0]) | |
| title = "Classical Bo Word2Vec word vectors" | |
| examples = ["སེམས་", "བླ་མ་", "སྟོབས་"] | |
| print(find_similarity(examples[0], examples[1])) | |
| demo = gr.Interface( | |
| fn=find_similarity, | |
| inputs=[gr.inputs.Textbox("Word A"), gr.inputs.Textbox("Word B")], | |
| outputs="number", | |
| title=title, | |
| # examples=examples | |
| ).launch() | |
| # demo = gr.Blocks() | |
| # with demo: | |
| # # Find top 10 most similar words | |
| # print(gr.__version__) | |
| # word = gr.inputs.Textbox("Word") | |
| # top_10_similar_words = gr.inputs.HTML() | |
| # b1 = gr.Button("Find similar words") | |
| # b1.click(find_most_similar, inputs=word, outputs=top_10_similar_words) | |
| # wordA = gr.inputs.Textbox("Word A") | |
| # wordB = gr.inputs.Textbox("Word B") | |
| # similarity = gr.Number() | |
| # b2 = gr.Button("Find similarity") | |
| # b2.click(similarity, inputs=[wordA, wordB], outputs=similarity) | |
| # demo.launch() |