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import gradio as gr |
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from gliner import GLiNER |
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model = GLiNER.from_pretrained("chris32/gliner_multi_pii_real_state-v2") |
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model.eval() |
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def generate_answer(text): |
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labels = [ |
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'SUPERFICIE_JARDIN', |
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'NOMBRE_CLUB_GOLF', |
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'SUPERFICIE_TERRENO', |
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'SUPERFICIE_HABITABLE', |
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'SUPERFICIE_TERRAZA', |
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'NOMBRE_COMPLETO_ARQUITECTO', |
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'SUPERFICIE_BALCON', |
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'NOMBRE_DESARROLLO', |
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'NOMBRE_TORRE', |
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'NOMBRE_CONDOMINIO', |
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'AÑO_REMODELACIÓN' |
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] |
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entities = model.predict_entities(text, labels, threshold=0.4) |
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result_dict = entities |
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return result_dict |
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text_input = gr.inputs.Textbox(lines=15, label="Input Text") |
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iface = gr.Interface( |
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fn=generate_answer, |
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inputs=text_input, |
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outputs="text", |
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title="Text Intelligence for Real State", |
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description="Input text describing the property." |
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) |
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iface.launch() |
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