Spaces:
Build error
Build error
File size: 3,710 Bytes
79eea61 6b57d24 f9a1aee 1030a98 f9a1aee 79eea61 4f98842 79eea61 4f98842 79eea61 4f98842 79eea61 998be39 79eea61 6451fb4 79eea61 6451fb4 79eea61 6451fb4 79eea61 6451fb4 79eea61 6451fb4 79eea61 4f98842 79eea61 4f98842 79eea61 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
import gradio as gr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("diabetes.csv")
df.info()
fig, axs = plt.subplots(4, 2, figsize=(15, 12))
axs = axs.flatten()
sns.distplot(df["Pregnancies"], rug=True, color="#38b000", ax=axs[0])
sns.distplot(df["Glucose"], rug=True, color="#FF9933", ax=axs[1])
sns.distplot(df["BloodPressure"], rug=True, color="#522500", ax=axs[2])
sns.distplot(df["SkinThickness"], rug=True, color="#66b3ff", ax=axs[3])
sns.distplot(df["Insulin"], rug=True, color="#FF6699", ax=axs[4])
sns.distplot(df["BMI"], color="#e76f51", rug=True, ax=axs[5])
sns.distplot(df["DiabetesPedigreeFunction"], color="#03045e", rug=True, ax=axs[6])
sns.distplot(df["Age"], rug=True, color="#333533", ax=axs[7])
plt.show()
bmi_outliers = df[df["BMI"] > 40]
bmi_outliers["BMI"].shape
df["Diabetes"] = df["Outcome"].apply(lambda x: "Diabetic" if x == 1 else "Not_Diabetic")
df["BMI"] = df["BMI"].apply(lambda x: df.BMI.mean() if x > 40 else x)
from pycaret.classification import *
print("Hello 1")
#Diab = setup(data = df,target = 'Diabetes',normalize=True, session_id=1,fix_imbalance = True)
print("2")
Diab = setup(data=df,target="Diabetes",normalize=True,session_id=1,fix_imbalance=True,numeric_features=["Pregnancies"],log_experiment=True,experiment_name="diabetes",ignore_features=["Outcome"],silent=True)
best = compare_models(n_select=15)
compare_model_results = pull()
rf = create_model("rf", fold=10)
tuned_rf = tune_model(rf)
# creating a predict function to be passed into gradio UI
def predict(
model,
Pregnancies,
Glucose,
BloodPressure,
BMI,
SkinThickness,
Insulin,
Age,
DiabetesPedigreeFunction
):
df = pd.DataFrame.from_dict(
{
"Pregnancies": [Pregnancies],
"Glucose": [Glucose],
"BloodPressure": [BloodPressure],
"BMI": [BMI],
"SkinThickness": [SkinThickness],
"Insulin": [Insulin],
"Age": [Age],
"DiabetesPedigreeFunction": [DiabetesPedigreeFunction]
}
)
model_index = list(compare_model_results["Model"]).index(model)
model = best[model_index]
pred = predict_model(model, df, raw_score=True)
return {
"Not Diabetic": pred["Score_Not_Diabetic"][0].astype("float64"),
"Diabetic": pred["Score_Diabetic"][0].astype("float64")
}
import gradio as gr
model = gr.inputs.Dropdown(list(compare_model_results["Model"]), label="Models")
Pregnancies = gr.inputs.Slider(
minimum=1, maximum=20,step=1, default=df["Pregnancies"].mean(), label="Pregnancies"
)
Glucose = gr.inputs.Slider(
minimum=0, maximum=200,step=1 ,default=df["Glucose"].mean(), label="Glucose"
)
BloodPressure = gr.inputs.Slider(
minimum=0, maximum=140,step=1 ,default=df["BloodPressure"].mean(), label="BloodPressure"
)
BMI = gr.inputs.Slider(minimum=0, maximum=70, default=df["BMI"].mean(), label="BMI")
SkinThickness = gr.inputs.Slider(
minimum=0, maximum=100,step=1,default=df["SkinThickness"].mean(), label="SkinThickness"
)
Insulin = gr.inputs.Slider(
minimum=0, maximum=1000,step=1,default=df["Insulin"].mean(), label="Insulin"
)
Age = gr.inputs.Slider(minimum=0,maximum=100,step=1,default=df["Age"].mean(), label="Age")
DiabetesPedigreeFunction = gr.inputs.Slider(
minimum=0,
maximum=1,
default=df["DiabetesPedigreeFunction"].mean(),
label="DiabetesPedigreeFunction"
)
gr.Interface(
predict,
[
model,
Pregnancies,
Glucose,
BloodPressure,
BMI,
SkinThickness,
Insulin,
Age,
DiabetesPedigreeFunction
],
"label",
live=True,
).launch()
|