Update app.py

app.py

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+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 *
+
+# Diab = setup(data = df,target = 'Outcome',normalize=True, session_id=1,fix_imbalance = True)
+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"],
+)
+
+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, default=df["Pregnancies"].mean(), label="Pregnancies"
+)
+Glucose = gr.inputs.Slider(
+    minimum=0, maximum=200, default=df["Glucose"].mean(), label="Glucose"
+)
+BloodPressure = gr.inputs.Slider(
+    minimum=0, maximum=140, 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, default=df["SkinThickness"].mean(), label="SkinThickness"
+)
+Insulin = gr.inputs.Slider(
+    minimum=0, maximum=1000, default=df["Insulin"].mean(), label="Insulin"
+)
+Age = gr.inputs.Slider(minimum=0, maximum=100, 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()