app.py

"""
Webapp Front End
"""

import gradio as gr
import joblib
import pandas as pd

MODEL_PATH = "Random_Foresttest_model.pkl"

try:
    rf_model = joblib.load(MODEL_PATH)
except FileNotFoundError as e:
    raise FileNotFoundError(
        f"Model file not found at {MODEL_PATH}. Please check the path."
    ) from e

# Define the prediction function
def predict_with_model(State: float, Sex: float, GeneralHealth: float, PhysicalHealthDays: float, MentalHealthDays: float, LastCheckupTime: float, PhysicalActivities: float, SleepHours: float, HadStroke: float, HadArthritis: float, HadDiabetes: float, SmokerStatus: float, ECigaretteUsage: float, RaceEthnicityCategory: float, AgeCategory: float, HeightInMeters: float, WeightInKilograms: float, BMI: float, AlcoholDrinkers: float, HighRiskLastYear: float):
    try:
        # Prepare input as a DataFrame
        input_data = pd.DataFrame([[State, Sex, GeneralHealth, PhysicalHealthDays, MentalHealthDays, LastCheckupTime, PhysicalActivities, SleepHours, HadStroke, HadArthritis, HadDiabetes, SmokerStatus, ECigaretteUsage, RaceEthnicityCategory, AgeCategory, HeightInMeters, WeightInKilograms, BMI, AlcoholDrinkers, HighRiskLastYear]], columns=['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities', 'SleepHours', 'HadStroke', 'HadArthritis', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage', 'RaceEthnicityCategory', 'AgeCategory', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HighRiskLastYear'])
        prediction = rf_model.predict(input_data)
        return "Heart Disease Risk" if prediction[0] == 1 else "No Risk"
    except Exception as e:
        return f"Error during prediction: {e}"

# Define the Gradio interface
with gr.Blocks() as app:
    gr.Markdown("# Heart Disease Prediction App")
    gr.Markdown("### Provide input values for the features below and get a prediction.")

    input_components = []
    for feature in ['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities', 'SleepHours', 'HadStroke', 'HadArthritis', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage', 'RaceEthnicityCategory', 'AgeCategory', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HighRiskLastYear']:
        input_components.append(gr.Slider(0, 100, step=1, label=feature))

    predict_button = gr.Button("Predict")
    output = gr.Textbox(label="Prediction Result")

    # Connect prediction logic
    predict_button.click(
        fn=predict_with_model,
        inputs=input_components,
        outputs=output,
    )

# Launch the app
app.launch()