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import gradio as gr
import joblib
import pandas as pd
import json
# Load the trained model
model = joblib.load("optimized_heart_failure_model.pkl")
# Load feature names used during training
with open("feature_names.json", "r") as f:
feature_names = json.load(f)
# Define the prediction function
def predict_heart_failure(age_category, alcohol_drinkers, chest_scan, covid_positive, physical_health_days, mental_health_days, sleep_hours, bmi):
# Initialize all features to 0
input_data = {feature: 0 for feature in feature_names}
# Handle Age Category
input_data[f"AgeCategory_{age_category}"] = 1 # Set the selected age category to 1
# Handle categorical inputs
input_data[f"AlcoholDrinkers_{alcohol_drinkers}"] = 1 # Alcohol Drinkers
input_data[f"ChestScan_{chest_scan}"] = 1 # Chest Scan
input_data[f"CovidPos_{covid_positive}"] = 1 # Covid Positive
# Handle numeric inputs
input_data["PhysicalHealthDays"] = physical_health_days
input_data["MentalHealthDays"] = mental_health_days
input_data["SleepHours"] = sleep_hours
input_data["BMI"] = bmi
# Create a DataFrame with the required feature names
input_df = pd.DataFrame([input_data])
# Ensure all required features are present
for feature in feature_names:
if feature not in input_df.columns:
input_df[feature] = 0 # Fill missing features with default value (e.g., 0)
# Ensure no extra features are included
input_df = input_df[feature_names]
# Make a prediction
prediction = model.predict(input_df)[0]
return "High likelihood of heart failure" if prediction == 1 else "Low likelihood of heart failure"
# Define Gradio inputs
gradio_inputs = [
gr.Radio(
["18-24", "25-34", "35-44", "45-54", "55-64", "65-74", "75-79", "80 or older"],
label="Age Category"
),
gr.Radio(["Yes", "No"], label="Alcohol Drinkers"),
gr.Radio(["Yes", "No"], label="Chest Scan"),
gr.Radio(["Yes", "No"], label="COVID Positive"),
gr.Slider(0, 30, step=1, label="Physical Health Days"),
gr.Slider(0, 30, step=1, label="Mental Health Days"),
gr.Slider(0, 12, step=0.5, label="Sleep Hours"),
gr.Slider(10, 50, step=0.1, label="BMI")
]
# Set up the Gradio interface
interface = gr.Interface(
fn=predict_heart_failure,
inputs=gradio_inputs,
outputs="text",
title="Heart Failure Risk Prediction",
description="This app predicts the likelihood of heart failure based on health and lifestyle inputs."
)
# Launch the app
if __name__ == "__main__":
interface.launch() |