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| import pickle | |
| import pandas as pd | |
| import gradio as gr | |
| import sklearn | |
| # Cargar el modelo | |
| with open('modelo_search_2.pkl', 'rb') as f: | |
| model = pickle.load(f) | |
| def predict(mainline_moves, Opening, WhiteFideId, BlackFideId, WhiteElo, BlackElo): | |
| df_model = pd.DataFrame({"mainline_moves":mainline_moves, | |
| "Opening":Opening, | |
| "WhiteFideId":WhiteFideId, | |
| "BlackFideId":BlackFideId, | |
| "WhiteElo": WhiteElo, | |
| "BlackElo": BlackElo}, | |
| index= [0]) | |
| label_pred = model.predict(df_model)[0].item() | |
| if label_pred == 0: | |
| return "Gan贸 Blancas" | |
| elif label_pred == 1: | |
| return "Gan贸 Negras" | |
| else: | |
| return "Tablas" | |
| # Definir los widgets de entrada | |
| mainline_moves = gr.inputs.Number(label="Mainline Moves") | |
| Opening = gr.inputs.Textbox(label="Opening") | |
| WhiteFideId = gr.inputs.Textbox(label="White Fide ID") | |
| BlackFideId = gr.inputs.Textbox(label="Black Fide ID") | |
| WhiteElo = gr.inputs.Slider(label="White Elo", | |
| minimum=0, maximum=3000, step=10, | |
| default=2400) | |
| BlackElo = gr.inputs.Slider(label="Black Elo", | |
| minimum=0, maximum=3000, step=10, | |
| default=2400) | |
| # Definir la salida | |
| output_label = gr.outputs.Label(num_top_classes=1, label="Resultado de la partida") | |
| # Definir la interfaz | |
| iface = gr.Interface(fn=predict, | |
| inputs=[mainline_moves, Opening, WhiteFideId, BlackFideId, WhiteElo, BlackElo], | |
| outputs=output_label, | |
| title="Predicci贸n de partidas de Ajedrez", | |
| theme=gr.themes.Soft(), | |
| layout="horizontal", | |
| allow_flagging=False, | |
| description="Coloca los datos solicitados y descubre el resultado de la partida!") | |
| # Ejecutar la interfaz | |
| iface.launch() |