[add]App

requirements.txt

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+joblib
+sklearn.tree 

app.py

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+import gradio as gr
+import joblib
+
+m = joblib.load('tree_model.pkl')
+
+def predict(gender, ever_married, work_type, residence_type, smoking_status, age, hypertension, heart_disease, avg_glucose_level, bmi):
+    input_data = {
+        "gender": gender,
+        "ever_married": ever_married,
+        "work_type": work_type,
+        "residence_type": residence_type,
+        "smoking_status": smoking_status,
+        "age": age,
+        "hypertension": hypertension,
+        "heart_disease": heart_disease,
+        "avg_glucose_level": avg_glucose_level,
+        "bmi": bmi
+    }
+
+    prediction = m.predict([input_data])[0]
+    return prediction
+
+inputs = [
+    gr.inputs.Dropdown(choices=["Male", "Female", "Other"], label="Gender"),
+    gr.inputs.Dropdown(choices=["Yes", "No"], label="Ever Married"),
+    gr.inputs.Dropdown(choices=['Govt_job', 'Never_worked', 'Private', 'Self-employed', 'children'], label="Work Type"),
+    gr.inputs.Dropdown(choices=["Urban", "Rural"], label="Residence Type"),
+    gr.inputs.Dropdown(choices=["Unknown", "never smoked", "formerly smoked", "smokes"], label="Smoking Status"),
+    gr.inputs.Number(label="Age"),
+    gr.inputs.Number(label="Hypertension"),
+    gr.inputs.Number(label="Heart Disease"),
+    gr.inputs.Number(label="Average Glucose Level"),
+    gr.inputs.Number(label="BMI")
+]
+
+output = gr.outputs.Label(label="Predição")
+
+intf = gr.Interface(fn=predict, inputs=inputs, outputs=output)
+
+intf.launch()

tree_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:baef62872ac892408b37279caafb47df8a9a8ce130a845bed33085a4ed812a06
+size 2777