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8f81f8b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import gradio as gr
import pandas as pd
import torch
from predict import RiskPredictor
# Initialize the predictor
predictor = RiskPredictor()
def predict_risk(age, bmi, systolic_bp, diastolic_bp, cholesterol, heart_rate,
smoking, steps, stress, physical_activity, sleep, family_history,
diet_quality, alcohol, risk_score):
input_data = {
'age': age,
'bmi': bmi,
'systolic_bp': systolic_bp,
'diastolic_bp': diastolic_bp,
'cholesterol_mg_dl': cholesterol,
'resting_heart_rate': heart_rate,
'smoking_status': smoking,
'daily_steps': steps,
'stress_level': stress,
'physical_activity_hours_per_week': physical_activity,
'sleep_hours': sleep,
'family_history_heart_disease': family_history,
'diet_quality_score': diet_quality,
'alcohol_units_per_week': alcohol,
'heart_disease_risk_score': risk_score
}
prediction = predictor.predict_single(input_data)
# Return mapping for color-coded feedback
result_text = f"## Predicted Category: {prediction.upper()}"
if prediction == 'Low':
description = "✅ Low risk! Excellent heart health habits."
color = "#2ed573"
elif prediction == 'Medium':
description = "⚠️ Moderate risk. Consider heart-healthy changes."
color = "#ffa502"
else:
description = "🚨 High risk! Please consult a health professional."
color = "#ff4757"
return f'<div style="background-color: {color}; padding: 20px; border-radius: 10px; color: white;">{result_text}<br>{description}</div>'
# Gradio Interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🏥 CardioGuard: RNN Heart Risk Predictor")
gr.Markdown("Enter patient data below to analyze cardiovascular risk using a Deep Learning RNN model.")
with gr.Row():
with gr.Column():
age = gr.Number(label="Age", value=45)
bmi = gr.Number(label="BMI", value=26.5)
systolic = gr.Number(label="Systolic BP", value=130)
diastolic = gr.Number(label="Diastolic BP", value=85)
cholesterol = gr.Number(label="Cholesterol (mg/dl)", value=210)
with gr.Column():
smoking = gr.Dropdown(["Never", "Former", "Current"], label="Smoking Status", value="Never")
family_history = gr.Dropdown(["No", "Yes"], label="Family History", value="No")
steps = gr.Number(label="Daily Steps", value=6000)
heart_rate = gr.Number(label="Resting Heart Rate", value=72)
risk_score = gr.Number(label="Internal Risk Score (0-100)", value=35.0)
with gr.Column():
stress = gr.Slider(1, 10, step=1, label="Stress Level", value=5)
diet = gr.Slider(1, 10, step=1, label="Diet Quality", value=6)
activity = gr.Number(label="Physical Activity (hrs/wk)", value=3.5)
sleep = gr.Number(label="Sleep Hours", value=7.5)
alcohol = gr.Number(label="Alcohol Units/wk", value=2.0)
btn = gr.Button("Analyze Risk Profile", variant="primary")
output = gr.HTML()
btn.click(predict_risk, inputs=[
age, bmi, systolic, diastolic, cholesterol, heart_rate,
smoking, steps, stress, activity, sleep, family_history,
diet, alcohol, risk_score
], outputs=output)
if __name__ == "__main__":
demo.launch()
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