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app.py

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+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()