devssaturdays jsr90 commited on
Commit
72e446c
1 Parent(s): 982a2eb

Update app.py (#2)

Browse files

- Update app.py (b066f66edf36de43aaec7ed09190bdf6889e4cea)


Co-authored-by: JESUS SANCHEZ <jsr90@users.noreply.huggingface.co>

Files changed (1) hide show
  1. app.py +25 -29
app.py CHANGED
@@ -2,24 +2,22 @@ import gradio as gr
2
  import pandas as pd
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  from joblib import load
4
 
5
-
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-
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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 is_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 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": [BMI],
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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.inputs.Slider(1,99,label="Age"),
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- "checkbox",
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- gr.inputs.Slider(10,250,label="Diastolic Preassure"),
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- gr.inputs.Slider(10,250,label="Sistolic Preassure"),
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- gr.inputs.Radio(["Normal","High","Very High"],type="index",label="Cholesterol"),
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- gr.inputs.Radio(["Normal","High","Very High"],type="index",label="Glucosa Level"),
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- "checkbox",
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- "checkbox",
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- "checkbox",
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- gr.inputs.Slider(30,220,label="Height in cm"),
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- gr.inputs.Slider(10,300,label="Weight in Kg"),
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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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- [40,True,120,80,"High","Normal",0,0,1,168,62,21],
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- [35,False,150,60,"Very High","Normal",0,0,1,143,52,31],
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- [60,True,160,70,"High","High",1,1,0,185,90,23],
 
 
 
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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 = 'El proyecto de CARDIOSIGHT nace debido a la presente necesidad en nuestro país de crear métodos y herramientas de identificación temprana para los individuos con alto riesgo de sufrir enfermedades cardiovasculares. Con el fin de prevenir eventos cardíacos primarios y ayudar a disminuir la incidencia de nuevos casos, por medio de hábitos de prevención. Mas información: https://saturdays.ai/2022/03/16/cardiosight-machine-learning-para-calcular-riesgo-cardiovascular/',
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- theme = 'grass'
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  )
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-
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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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  )
 
27
  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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32
  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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48
  "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/'
 
59
  )
60
 
 
 
61
  iface.launch()