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import gradio as gr |
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import gensim |
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model_g = gensim.models.KeyedVectors.load_word2vec_format('v_glove_1024d_2.0' , binary=True) |
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def generate(text): |
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result= model_g.most_similar('together',topn=10) |
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return result |
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examples = [ |
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["sad"], |
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["together"], |
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["lake"] |
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] |
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title = "Visually Grounded embeddings" |
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description = 'Get the top 10 nearest neighbors from a visually grounded word embedding model described in [this paper](https://arxiv.org/abs/2206.08823).<br>' |
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txt = gr.Textbox(lines=1, label="Query word", placeholder="muffin") |
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out = gr.Textbox(lines=4, label="top 10 nearest neighbors") |
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demo = gr.Interface( |
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fn =generate, |
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inputs=txt, |
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outputs=out, |
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examples=examples, |
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title=title, |
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description=description, |
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theme="default", |
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cache_examples="never" |
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) |
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demo.launch(enable_queue=True, debug=True) |
