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| import gensim.downloader | |
| import gradio as gr | |
| import pandas as pd | |
| model = gensim.downloader.load("glove-wiki-gigaword-50") | |
| description = """ | |
| ### Word Embedding Demo App | |
| Universidade Federal de São Paulo - Escola Paulista de Medicina | |
| The output is Word3 + (Word2 - Word1) | |
| Credits: | |
| * Gensim | |
| * Glove | |
| """ | |
| Word1 = gr.Textbox() | |
| Word2 = gr.Textbox() | |
| Word3 = gr.Textbox() | |
| label = gr.Label(show_label=True, label="Word4") | |
| bp = gr.BarPlot(x="x", y="y") | |
| def inference(word1, word2, word3): | |
| output = model.similar_by_vector(model[word3] + model[word2] - model[word1]) | |
| #out_str = [x + ": " + str(y) for x,y in [item for item in output]] | |
| #return """\n""".join(out_str) | |
| df = pd.DataFrame({"x": [x for x,y in [item for item in output]], | |
| "y": [str(y) for x,y in [item for item in output]] | |
| }) | |
| return df | |
| examples = [ | |
| ["woman", "man", "aunt"], | |
| ["woman", "man", "girl"], | |
| ["woman", "man", "granddaughter"], | |
| ] | |
| iface = gr.Interface( | |
| fn=inference, | |
| inputs=[Word1, Word2, Word3], | |
| outputs=bp, | |
| description=description, | |
| examples=examples | |
| ) | |
| iface.launch() |