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| import gradio as gr | |
| import pandas as pd | |
| from joblib import load | |
| def predict_bodymass(FlipperLength): | |
| model = load("penguin_predictor.jb") | |
| # Create DataFrame from input | |
| data = { | |
| "FlipperLength": [FlipperLength] | |
| } | |
| xin = pd.DataFrame(data) | |
| bodymass = model.predict(xin) | |
| return bodymass[0] | |
| peso = gr.Textbox(label="Peso estimado (en gramos)") | |
| iface = gr.Interface( | |
| fn=predict_bodymass, | |
| inputs=[ | |
| gr.inputs.Textbox(placeholder="Introduce la longitud de la aleta en mil铆metros",numeric=True,label="Longitud de la aleta (mm)") | |
| ], | |
| title="Modelo de regresi贸n para la predicci贸n del peso de ping眉inos", | |
| outputs=[peso], | |
| examples=[[180], [200], [220]] | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |