Spaces:
Sleeping
Sleeping
File size: 2,832 Bytes
c48b9e5 cab83d4 c48b9e5 cab83d4 c48b9e5 cab83d4 c48b9e5 c28c5f4 c48b9e5 cab83d4 c28c5f4 cab83d4 c28c5f4 c48b9e5 cab83d4 c28c5f4 cab83d4 c48b9e5 cab83d4 c28c5f4 cab83d4 c48b9e5 cab83d4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
import gradio as gr
import numpy as np
from joblib import load
rf = load("model.pkl")
columns = [
"BMI",
"Smoking",
"AlcoholDrinking",
"Stroke",
"PhysicalHealth",
"MentalHealth",
"DiffWalking",
"Sex",
"AgeEstimate",
"Race",
"Diabetic",
"PhysicalActivity",
"GenHealth",
"SleepTime",
"Asthma",
"KidneyDisease",
"SkinCancer"
]
def convert_to_int(s):
return int(s[:2])
def predict(
BMI,
Smoking,
AlcoholDrinking,
Stroke,
PhysicalHealth,
MentalHealth,
DiffWalking,
Sex,
AgeEstimate,
Race,
Diabetic,
PhysicalActivity,
GenHealth,
SleepTime,
Asthma,
KidneyDisease,
SkinCancer
):
data = np.array(
[
[
BMI,
Smoking,
AlcoholDrinking,
Stroke,
PhysicalHealth,
MentalHealth,
DiffWalking,
Sex,
convert_to_int(AgeEstimate),
Race+1,
Diabetic,
PhysicalActivity,
GenHealth+1,
SleepTime,
Asthma,
KidneyDisease,
SkinCancer
]
]
)
pred = rf.predict(data)[0]
if pred == 1:
return "Has heart disease"
else:
return "Does not have any heart disease"
inputs = [
gr.Slider(minimum=0, maximum=150, label="BMI"),
gr.Checkbox(label="Smoking"),
gr.Checkbox(label="Alcohol Drinking"),
gr.Checkbox(label="Stroke"),
gr.Number(label="Physical Health"),
gr.Number(label="MentalHealth"),
gr.Checkbox(label="Diff Walking"),
gr.Dropdown(["Female", "Male"], type="index", label="Sex"),
gr.Dropdown(['18-24',
'25-29',
'30-34',
'35-39',
'40-44',
'45-49',
'50-54',
'55-59',
'60-64',
'65-69',
'70-74',
'75-79',
'80 or older'], label="Age Category"),
gr.Dropdown(["White", "Black", "Asian", "American Indian/Alaskan Native", "Hispanic", "Other"], type="index", label="Race"),
gr.Dropdown(["No", "Yes", "Yes(during pregnancy)", "No, borderline diabetes"], type="index", label="Diebetic"),
gr.Checkbox(label="Physical Activity"),
gr.Dropdown(["Poor", "Fair", "Good", "Very good", "Excellent"], type="index", label="General Health"),
gr.Number(label="Sleep time"),
gr.Checkbox(label="Asthma"),
gr.Checkbox(label="Kidney Disease"),
gr.Checkbox(label="Skin Cancer"),
]
iface = gr.Interface(
fn=predict,
inputs=inputs,
outputs="text",
description="The purpose of this model is to predict whether or not a person has any heart diseases or not.",
)
iface.launch() |