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import gradio as gr
import joblib
import numpy as np
import pandas as pd
# Load the trained model
model_path = "trained_model.pkl"
def load_model(path):
try:
return joblib.load(path)
except Exception as e:
raise ValueError(f"Error loading model: {e}")
# Define the prediction function
def predict_heart_disease(
PhysicalHealthDays: float,
MentalHealthDays: float,
SleepHours: float,
BMI: float,
PhysicalActivities: str,
AlcoholDrinkers: str,
HIVTesting: str,
RemovedTeeth: str,
HighRiskLastYear: str,
CovidPos: str,
):
try:
model = load_model(model_path)
# Encode categorical inputs as integers
physical_activities = 1 if PhysicalActivities.lower() == "yes" else 0
alcohol_drinkers = 1 if AlcoholDrinkers.lower() == "yes" else 0
hiv_testing = 1 if HIVTesting.lower() == "yes" else 0
removed_teeth = 1 if RemovedTeeth.lower() == "yes" else 0
high_risk_last_year = 1 if HighRiskLastYear.lower() == "yes" else 0
covid_pos = 1 if CovidPos.lower() == "yes" else 0
# Combine inputs into a numpy array for prediction
features = np.array([
PhysicalHealthDays, MentalHealthDays, SleepHours, BMI,
physical_activities, alcohol_drinkers, hiv_testing,
removed_teeth, high_risk_last_year, covid_pos
]).reshape(1, -1)
# Predict with the model
prediction = model.predict(features)
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 and receive a prediction.")
with gr.Row():
PhysicalHealthDays = gr.Slider(0, 30, label="Physical Health Days")
MentalHealthDays = gr.Slider(0, 30, label="Mental Health Days")
SleepHours = gr.Slider(0, 24, label="Average Sleep Hours")
BMI = gr.Slider(10, 50, label="Body Mass Index (BMI)")
with gr.Row():
PhysicalActivities = gr.Radio(["Yes", "No"], label="Engaged in Physical Activities?")
AlcoholDrinkers = gr.Radio(["Yes", "No"], label="Consumes Alcohol?")
HIVTesting = gr.Radio(["Yes", "No"], label="Tested for HIV?")
RemovedTeeth = gr.Radio(["Yes", "No"], label="Has Removed Teeth?")
HighRiskLastYear = gr.Radio(["Yes", "No"], label="High Risk Last Year?")
CovidPos = gr.Radio(["Yes", "No"], label="Tested Positive for COVID-19?")
predict_button = gr.Button("Predict")
output = gr.Textbox(label="Prediction Result")
# Connect prediction logic
predict_button.click(
fn=predict_heart_disease,
inputs=[
PhysicalHealthDays, MentalHealthDays, SleepHours, BMI,
PhysicalActivities, AlcoholDrinkers, HIVTesting,
RemovedTeeth, HighRiskLastYear, CovidPos
],
outputs=output,
)
# Launch the app
app.launch()
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