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Update app.py
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app.py
CHANGED
@@ -2,24 +2,22 @@ import gradio as gr
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import pandas as pd
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from joblib import load
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def cardio(age,is_male,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active,height,weight,BMI):
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model = load('cardiosight.joblib')
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df = pd.DataFrame.from_dict(
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{
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"age": [age*365],
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"gender":[0 if
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"ap_hi": [ap_hi],
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"ap_lo": [ap_lo],
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"cholesterol": [cholesterol + 1],
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"gluc": [gluc + 1],
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"smoke":[1 if smoke else 0],
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"alco": [1 if alco else 0],
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"active": [1 if active else 0],
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"newvalues_height": [height],
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"newvalues_weight": [weight],
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"New_values_BMI":
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}
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)
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@@ -29,37 +27,35 @@ def cardio(age,is_male,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active,height,wei
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predicted="Tiene un riesgo alto de sufrir problemas cardiovasculares"
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else:
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predicted="Su riesgo de sufrir problemas cardiovasculares es muy bajo. Siga así."
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return predicted
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iface = gr.Interface(
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cardio,
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[
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gr.
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gr.
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gr.
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gr.
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gr.
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gr.
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gr.
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gr.inputs.Slider(1,50,label="BMI"),
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],
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"text",
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examples=[
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[
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[
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[
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],
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interpretation="default",
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title = 'Calculadora de Riesgo Cardiovascular mediante Inteligencia Artificial',
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description = '
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theme = 'grass'
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)
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iface.launch()
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import pandas as pd
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from joblib import load
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def cardio(age,gender,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active,height,weight):
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model = load('cardiosight.joblib')
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df = pd.DataFrame.from_dict(
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{
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"age": [age*365],
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"gender":[0 if gender=='Male' else 1],
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"ap_hi": [ap_hi],
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"ap_lo": [ap_lo],
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"cholesterol": [cholesterol + 1],
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"gluc": [gluc + 1],
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"smoke":[1 if smoke=='Yes' else 0],
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"alco": [1 if alco=='Yes' else 0],
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"active": [1 if active=='Yes' else 0],
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"newvalues_height": [height],
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"newvalues_weight": [weight],
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"New_values_BMI": weight/((height/100)**2),
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}
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)
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predicted="Tiene un riesgo alto de sufrir problemas cardiovasculares"
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else:
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predicted="Su riesgo de sufrir problemas cardiovasculares es muy bajo. Siga así."
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return "Su IMC es de "+str(round(df['New_values_BMI'][0], 2))+'. '+predicted
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iface = gr.Interface(
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cardio,
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[
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gr.Slider(1,99,label="Age"),
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gr.Dropdown(choices=['Male', 'Female'], label='Gender', value='Female'),
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gr.Slider(10,250,label="Diastolic Preassure"),
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gr.Slider(10,250,label="Sistolic Preassure"),
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gr.Radio(["Normal","High","Very High"],type="index",label="Cholesterol"),
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gr.Radio(["Normal","High","Very High"],type="index",label="Glucosa Level"),
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gr.Dropdown(choices=['Yes', 'No'], label='Smoke', value='No'),
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gr.Dropdown(choices=['Yes', 'No'], label='Alcohol', value='No'),
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gr.Dropdown(choices=['Yes', 'No'], label='Active', value='Yes'),
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gr.Slider(30,220,label="Height in cm"),
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gr.Slider(10,300,label="Weight in Kg"),
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],
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"text",
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examples=[
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[20,'Male',110,60,"Normal","Normal",'No','No','Yes',168,60],
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[30,'Female',120,70,"High","High",'No','Yes','Yes',143,70],
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[40,'Male',130,80,"Very High","Very High",'Yes','Yes','No',185,80],
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[50,'Female',140,90,"Normal","High",'Yes','No','No',165,90],
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[60,'Male',150,100,"High","Very High",'No','No','Yes',175,100],
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[70,'Female',160,90,"Very High","Normal",'Yes','Yes','No',185,110],
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],
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title = 'Calculadora de Riesgo Cardiovascular mediante Inteligencia Artificial',
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description = 'Duplicación del proyecto de CARDIOSIGHT. He cambiado los botones tipo check por dropdown y calculado el IMC a partir de la altura y el peso. Más información: https://saturdays.ai/2022/03/16/cardiosight-machine-learning-para-calcular-riesgo-cardiovascular/'
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)
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iface.launch()
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