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Cardiosight
File size: 2,478 Bytes 
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import gradio as gr
import pandas as pd
from joblib import load



def cardio(age,is_male,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active,height,weight,BMI):
    model = load('cardiosight.joblib')
    df = pd.DataFrame.from_dict(
        {
            "age": [age*365],
            "gender":[0 if is_male else 1],
            "ap_hi": [ap_hi],
            "ap_lo": [ap_lo],
            "cholesterol": [cholesterol + 1],
            "gluc": [gluc + 1],
            "smoke":[1 if smoke else 0],
            "alco": [1 if alco else 0],
            "active": [1 if active else 0],
            "newvalues_height": [height],
            "newvalues_weight": [weight],
            "New_values_BMI": [BMI],
            
        }
    )
       
    pred = model.predict(df)[0]
    if pred==1:
      predicted="Tiene un riesgo alto de sufrir problemas cardiovasculares"
    else:
      predicted="Su riesgo de sufrir problemas cardiovasculares es muy bajo. Siga as铆."
    return predicted
    
iface = gr.Interface(
    cardio,
    [
        gr.inputs.Slider(1,99,label="Age"),
        "checkbox",
        gr.inputs.Slider(10,250,label="Diastolic Preassure"),
        gr.inputs.Slider(10,250,label="Sistolic Preassure"),
        gr.inputs.Radio(["Normal","High","Very High"],type="index",label="Cholesterol"),
        gr.inputs.Radio(["Normal","High","Very High"],type="index",label="Glucosa Level"),
        "checkbox",
        "checkbox",
        "checkbox",
        gr.inputs.Slider(30,220,label="Height in cm"),
        gr.inputs.Slider(10,300,label="Weight in Kg"),
        gr.inputs.Slider(1,50,label="BMI"),
    ],

    "text",
    examples=[
        [40,True,120,80,"High","Normal",0,0,1,168,62,21],
        [35,False,150,60,"Very High","Normal",0,0,1,143,52,31],
        [60,True,160,70,"High","High",1,1,0,185,90,23],
    ],
    interpretation="default",
    title = 'Calculadora de Riesgo Cardiovascular mediante Inteligencia Artificial',
    description = 'El proyecto de CARDIOSIGHT nace debido a la presente necesidad en nuestro pa铆s de crear m茅todos y herramientas de identificaci贸n temprana para los individuos con alto riesgo de sufrir enfermedades cardiovasculares. Con el fin de prevenir eventos card铆acos primarios y ayudar a disminuir la incidencia de nuevos casos, por medio de h谩bitos de prevenci贸n. Mas informaci贸n: https://saturdays.ai/2022/03/16/cardiosight-machine-learning-para-calcular-riesgo-cardiovascular/',
    theme = 'grass'
)


   
iface.launch()