app.py

import gradio as gr
import joblib
import numpy as np
import pandas as pd
from huggingface_hub import hf_hub_download

# --- Configuración ---
MODEL_REPO = "gusdelact/mouse-viral-svm"

# Descargar modelo y preprocessor desde HF Hub
model_path = hf_hub_download(MODEL_REPO, "model.joblib")
prep_path = hf_hub_download(MODEL_REPO, "preprocessor.joblib")

model = joblib.load(model_path)
preprocessor = joblib.load(prep_path)


def predict(med_1_ml, med_2_ml):
    """Predice si un ratón tiene virus basándose en mediciones de medicamentos."""
    data = pd.DataFrame({"Med_1_mL": [med_1_ml], "Med_2_mL": [med_2_ml]})
    X = preprocessor.transform(data)
    prediction = model.predict(X)[0]
    probability = model.predict_proba(X)[0]

    result = "🦠 Virus PRESENTE" if prediction == 1 else "✅ Sin virus"
    confidence = probability[int(prediction)]

    return (
        result,
        f"Confianza: {confidence:.2%}",
        f"P(No virus): {probability[0]:.4f}",
        f"P(Virus): {probability[1]:.4f}",
    )


demo = gr.Interface(
    fn=predict,
    inputs=[
        gr.Slider(1.0, 10.0, value=5.0, step=0.1, label="Med_1_mL",
                  info="Dosis del medicamento 1 (mL)"),
        gr.Slider(1.0, 10.0, value=5.0, step=0.1, label="Med_2_mL",
                  info="Dosis del medicamento 2 (mL)"),
    ],
    outputs=[
        gr.Textbox(label="Predicción"),
        gr.Textbox(label="Confianza"),
        gr.Textbox(label="Probabilidad No Virus"),
        gr.Textbox(label="Probabilidad Virus"),
    ],
    title="🐭 Mouse Viral Study — Clasificador SVM",
    description="""
    Clasificador SVM (Support Vector Machine) entrenado para detectar
    presencia de virus en ratones basándose en mediciones de dos medicamentos.

    **Modelo**: SVC con kernel RBF (C=1, gamma=0.01)
    **Métricas**: Accuracy=1.0, F1=1.0, ROC-AUC=1.0
    """,
    examples=[
        [2.0, 3.0],   # Virus presente
        [7.0, 8.0],   # Sin virus
        [5.0, 5.5],   # Zona frontera
    ],
)

if __name__ == "__main__":
    demo.launch()