"""
HeartWatch AI - ECG Analysis Demo
==================================

A Gradio-based web application for AI-powered ECG analysis using DeepECG models.

Features:
- 77-class ECG diagnosis
- LVEF < 40% prediction
- LVEF < 50% prediction
- 5-year AFib risk assessment
- Interactive 12-lead ECG visualization
"""

import os
import logging
import numpy as np
import gradio as gr
from pathlib import Path

# Local imports
from inference import DeepECGInference
from visualization import (
    plot_ecg_waveform,
    plot_diagnosis_bars,
    plot_risk_gauges,
)

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Global inference engine
inference_engine = None

# Sample ECG descriptions - mapped by file stem (with underscores replaced by spaces and title-cased)
# The files are: Atrial_Flutter.npy, Normal_Sinus_Rhythm.npy, Ventricular_Tachycardia.npy
# They get sorted alphabetically: Atrial Flutter, Normal Sinus Rhythm, Ventricular Tachycardia
# We want to display them as Sample 1, Sample 2, Sample 3
SAMPLE_FILE_TO_DISPLAY = {
    "Atrial Flutter": "Sample 1",
    "Normal Sinus Rhythm": "Sample 2",
    "Ventricular Tachycardia": "Sample 3",
}

SAMPLE_DESCRIPTIONS = {
    "Sample 1": "Atrial Flutter - A rapid but regular atrial rhythm, typically around 250-350 bpm in the atria.",
    "Sample 2": "Normal Sinus Rhythm - A healthy heart rhythm with regular beats originating from the sinus node.",
    "Sample 3": "Ventricular Tachycardia - A fast heart rhythm originating from the ventricles, potentially life-threatening.",
}

# Reverse mapping: display name to real condition info for analysis results
DISPLAY_TO_CONDITION = {
    "Sample 1": {
        "name": "Atrial Flutter",
        "description": "A rapid but regular atrial rhythm, typically around 250-350 bpm in the atria."
    },
    "Sample 2": {
        "name": "Normal Sinus Rhythm",
        "description": "A healthy heart rhythm with regular beats originating from the sinus node."
    },
    "Sample 3": {
        "name": "Ventricular Tachycardia",
        "description": "A fast heart rhythm originating from the ventricles, potentially life-threatening."
    },
}


def load_inference_engine():
    """Load the inference engine on startup."""
    global inference_engine
    if inference_engine is None:
        logger.info("Loading DeepECG inference engine...")
        inference_engine = DeepECGInference()
        inference_engine.load_models()
        logger.info("Inference engine loaded successfully")
    return inference_engine


def get_sample_ecgs():
    """Get list of sample ECG files from demo_data directory."""
    sample_dir = Path(__file__).parent / "demo_data" / "samples"
    if not sample_dir.exists():
        logger.warning(f"Sample directory not found: {sample_dir}")
        return []

    samples = []
    for npy_file in sorted(sample_dir.glob("*.npy")):
        original_name = npy_file.stem.replace("_", " ").title()
        # Map to new display name (Sample 1, Sample 2, Sample 3)
        display_name = SAMPLE_FILE_TO_DISPLAY.get(original_name, original_name)
        samples.append({
            "path": str(npy_file),
            "name": display_name,
            "original_name": original_name,
            "description": SAMPLE_DESCRIPTIONS.get(display_name, "Sample ECG recording")
        })
    logger.info(f"Found {len(samples)} sample ECGs")
    return samples


def analyze_ecg(ecg_signal: np.ndarray, filename: str = "ECG Analysis", condition_info: dict = None):
    """
    Analyze an ECG signal and return all visualizations.

    Args:
        ecg_signal: ECG signal array
        filename: Name to display
        condition_info: Optional dict with 'name' and 'description' for the condition

    Returns:
        Tuple of (ecg_plot, diagnosis_plot, risk_plot, summary_text)
    """
    engine = load_inference_engine()

    # Run inference
    results = engine.predict(ecg_signal)

    # Generate ECG waveform plot
    ecg_fig = plot_ecg_waveform(ecg_signal, sample_rate=250, title=filename)

    # Generate diagnosis bar chart
    if "diagnosis_77" in results:
        probs = results["diagnosis_77"]["probabilities"]
        class_names = results["diagnosis_77"]["class_names"]
        diagnosis_dict = dict(zip(class_names, probs))
        diagnosis_fig = plot_diagnosis_bars(diagnosis_dict, top_n=10)
    else:
        diagnosis_fig = None

    # Generate risk gauges
    lvef_40 = results.get("lvef_40", 0.0)
    lvef_50 = results.get("lvef_50", 0.0)
    afib_5y = results.get("afib_5y", 0.0)
    risk_fig = plot_risk_gauges(lvef_40, lvef_50, afib_5y)

    # Generate modern HTML summary with styled diagnosis cards
    inference_time = results.get("inference_time_ms", 0)

    # Build the diagnosis cards HTML with modern dark theme design
    diagnosis_html = '<div class="diagnosis-dashboard-title">Top 5 Diagnoses</div>'
    if "diagnosis_77" in results:
        probs = results["diagnosis_77"]["probabilities"]
        class_names = results["diagnosis_77"]["class_names"]
        top_indices = np.argsort(probs)[::-1][:5]

        for i, idx in enumerate(top_indices, 1):
            prob_pct = probs[idx] * 100
            # Determine severity class based on probability
            if prob_pct < 30:
                severity_class = "severity-low"
            elif prob_pct < 60:
                severity_class = "severity-medium"
            else:
                severity_class = "severity-high"

            # Create smooth gradient progress bar (no segments)
            diagnosis_html += f"""
            <div class="diagnosis-row {severity_class}">
                <span class="diagnosis-rank">#{i}</span>
                <span class="diagnosis-name" title="{class_names[idx]}">{class_names[idx]}</span>
                <div class="diagnosis-bar-container">
                    <div class="diagnosis-bar-track">
                        <div class="diagnosis-bar-fill" style="width: {prob_pct}%;"></div>
                    </div>
                </div>
                <span class="diagnosis-percent">{prob_pct:.0f}%</span>
            </div>
            """

    # Determine display title and description
    if condition_info:
        display_title = condition_info.get("name", filename)
        condition_desc = condition_info.get("description", "")
        condition_html = f'<p style="color: #666; font-size: 0.95em; margin: 8px 0 16px 0; font-style: italic;">{condition_desc}</p>' if condition_desc else ""
    else:
        display_title = filename
        condition_html = ""

    summary = f"""
<div style="padding: 10px; font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;">

<h2 style="margin: 0 0 8px 0; color: #333;">Analysis Results: {display_title}</h2>
{condition_html}

<div style="display: inline-block; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 8px 16px; border-radius: 20px; font-size: 0.9em; margin-bottom: 20px;">
Inference Time: {inference_time:.1f} ms
</div>

<h3 style="margin: 20px 0 12px 0; color: #444;">Risk Predictions</h3>

<table style="width: 100%; border-collapse: collapse; margin-bottom: 20px; background: #f8f9fa; border-radius: 8px; overflow: hidden;">
<thead>
<tr style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white;">
<th style="padding: 12px 16px; text-align: left; font-weight: 600;">Risk Factor</th>
<th style="padding: 12px 16px; text-align: left; font-weight: 600;">Probability</th>
</tr>
</thead>
<tbody>
<tr style="border-bottom: 1px solid #e9ecef;">
<td style="padding: 12px 16px;">LVEF &lt; 40%</td>
<td style="padding: 12px 16px; font-weight: 600;">{lvef_40*100:.1f}%</td>
</tr>
<tr style="border-bottom: 1px solid #e9ecef;">
<td style="padding: 12px 16px;">LVEF &lt; 50%</td>
<td style="padding: 12px 16px; font-weight: 600;">{lvef_50*100:.1f}%</td>
</tr>
<tr>
<td style="padding: 12px 16px;">5-year AFib Risk</td>
<td style="padding: 12px 16px; font-weight: 600;">{afib_5y*100:.1f}%</td>
</tr>
</tbody>
</table>

<div class="diagnosis-dashboard">
{diagnosis_html}
</div>

</div>
"""

    return ecg_fig, diagnosis_fig, risk_fig, summary


def analyze_uploaded_file(file):
    """Handle uploaded .npy file."""
    if file is None:
        return None, None, None, "<p style='color: #666;'>Please upload a .npy file containing ECG data.</p>"

    try:
        # In Gradio 4.x with type="filepath", file is a string path
        file_path = file if isinstance(file, str) else file.name
        ecg_signal = np.load(file_path)
        filename = Path(file_path).stem.replace("_", " ").title()
        return analyze_ecg(ecg_signal, filename)
    except Exception as e:
        logger.error(f"Error loading file: {e}")
        return None, None, None, f"<p style='color: #dc3545;'>Error loading file: {str(e)}</p>"


def analyze_sample_by_name(sample_name: str):
    """Analyze a sample ECG by its name."""
    if not sample_name:
        return None, None, None, "<p style='color: #666;'>Please select a sample ECG.</p>"

    samples = get_sample_ecgs()
    for sample in samples:
        if sample["name"] == sample_name:
            try:
                ecg_signal = np.load(sample["path"])
                # Get the real condition info for display
                condition_info = DISPLAY_TO_CONDITION.get(sample_name)
                return analyze_ecg(ecg_signal, sample["name"], condition_info)
            except Exception as e:
                logger.error(f"Error loading sample: {e}")
                return None, None, None, f"<p style='color: #dc3545;'>Error loading sample: {str(e)}</p>"

    return None, None, None, "<p style='color: #dc3545;'>Sample not found.</p>"


def create_demo_interface():
    """Create the Gradio interface."""

    # Get samples at startup
    samples = get_sample_ecgs()
    sample_names = [s["name"] for s in samples]

    # Custom CSS for styling with modern animated header
    custom_css = """
    .gradio-container {
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
    }

    /* Animated Header Styles */
    .main-header {
        text-align: center;
        padding: 40px 24px;
        background: linear-gradient(-45deg, #ee7752, #e73c7e, #c0392b, #e74c3c);
        background-size: 400% 400%;
        animation: gradientShift 8s ease infinite;
        color: white;
        border-radius: 16px;
        margin-bottom: 24px;
        box-shadow: 0 10px 40px rgba(231, 76, 60, 0.3);
        position: relative;
        overflow: hidden;
    }

    .main-header::before {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;
        background: radial-gradient(circle at 30% 50%, rgba(255,255,255,0.1) 0%, transparent 50%);
        pointer-events: none;
    }

    @keyframes gradientShift {
        0% { background-position: 0% 50%; }
        50% { background-position: 100% 50%; }
        100% { background-position: 0% 50%; }
    }

    .header-content {
        position: relative;
        z-index: 2;
        display: flex;
        flex-direction: column;
        align-items: center;
        gap: 12px;
    }

    /* Pulsing Heart Container */
    .heart-container {
        position: relative;
        width: 100px;
        height: 100px;
        display: flex;
        align-items: center;
        justify-content: center;
    }

    /* Heart SVG Animation */
    .heart-svg {
        width: 80px;
        height: 80px;
        animation: heartbeat 1.2s ease-in-out infinite;
        filter: drop-shadow(0 0 20px rgba(255,255,255,0.5));
    }

    @keyframes heartbeat {
        0% { transform: scale(1); }
        14% { transform: scale(1.15); }
        28% { transform: scale(1); }
        42% { transform: scale(1.1); }
        70% { transform: scale(1); }
    }

    /* ECG Line Animation */
    .ecg-line {
        position: absolute;
        width: 200px;
        height: 40px;
        left: 50%;
        transform: translateX(-50%);
        bottom: -10px;
    }

    .ecg-path {
        stroke: rgba(255,255,255,0.8);
        stroke-width: 2;
        fill: none;
        stroke-linecap: round;
        stroke-dasharray: 200;
        stroke-dashoffset: 200;
        animation: ecgDraw 2s ease-in-out infinite;
    }

    @keyframes ecgDraw {
        0% { stroke-dashoffset: 200; opacity: 0; }
        10% { opacity: 1; }
        50% { stroke-dashoffset: 0; opacity: 1; }
        90% { opacity: 1; }
        100% { stroke-dashoffset: -200; opacity: 0; }
    }

    .main-header h1 {
        margin: 0;
        font-size: 2.8em;
        font-weight: 700;
        letter-spacing: -0.02em;
        text-shadow: 0 2px 10px rgba(0,0,0,0.2);
    }

    .main-header p {
        margin: 0;
        opacity: 0.95;
        font-size: 1.2em;
        font-weight: 400;
        letter-spacing: 0.02em;
    }

    .sample-card {
        padding: 16px;
        border-radius: 8px;
        background: #f8f9fa;
        margin: 8px 0;
        border-left: 4px solid #e74c3c;
    }

    .quick-start {
        background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%);
        padding: 18px 20px;
        border-radius: 12px;
        margin: 20px 0;
        border-left: 5px solid #4caf50;
        box-shadow: 0 2px 8px rgba(76, 175, 80, 0.15);
    }

    /* Dark Theme Diagnosis Dashboard */
    .diagnosis-dashboard {
        background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
        border-radius: 16px;
        padding: 24px;
        margin-top: 8px;
        box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3), inset 0 1px 0 rgba(255, 255, 255, 0.05);
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
    }

    .diagnosis-dashboard-title {
        color: #ffffff;
        font-size: 0.85em;
        font-weight: 600;
        text-transform: uppercase;
        letter-spacing: 0.1em;
        margin-bottom: 20px;
        padding-bottom: 12px;
        border-bottom: 1px solid rgba(255, 255, 255, 0.1);
        text-shadow: 0 0 20px rgba(255, 255, 255, 0.3);
    }

    .diagnosis-row {
        display: flex;
        align-items: center;
        padding: 12px 16px;
        margin: 8px 0;
        background: rgba(255, 255, 255, 0.03);
        border-radius: 10px;
        transition: all 0.2s ease;
        border: 1px solid rgba(255, 255, 255, 0.05);
    }

    .diagnosis-row:hover {
        background: rgba(255, 255, 255, 0.08);
        transform: translateX(4px);
    }

    .diagnosis-rank {
        font-size: 0.9em;
        font-weight: 700;
        color: rgba(255, 255, 255, 0.5);
        width: 36px;
        flex-shrink: 0;
    }

    .diagnosis-name {
        font-size: 0.95em;
        font-weight: 500;
        color: #ffffff;
        min-width: 120px;
        max-width: 180px;
        flex-shrink: 0;
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
        text-shadow: 0 1px 2px rgba(0, 0, 0, 0.3);
    }

    .diagnosis-bar-container {
        flex: 1;
        display: flex;
        align-items: center;
        margin: 0 16px;
        min-width: 80px;
    }

    .diagnosis-bar-track {
        width: 100%;
        height: 6px;
        background: rgba(255, 255, 255, 0.1);
        border-radius: 3px;
        position: relative;
        overflow: hidden;
    }

    .diagnosis-bar-fill {
        height: 100%;
        border-radius: 3px;
        transition: width 0.6s cubic-bezier(0.4, 0, 0.2, 1);
        position: relative;
    }

    /* Animated shine effect on bars */
    .diagnosis-bar-fill::after {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;
        background: linear-gradient(
            90deg,
            transparent 0%,
            rgba(255, 255, 255, 0.3) 50%,
            transparent 100%
        );
        animation: shine 2s ease-in-out infinite;
    }

    @keyframes shine {
        0% { transform: translateX(-100%); }
        100% { transform: translateX(100%); }
    }

    .diagnosis-percent {
        font-size: 0.9em;
        font-weight: 700;
        width: 55px;
        text-align: right;
        flex-shrink: 0;
        text-shadow: 0 0 10px currentColor;
    }

    /* Color classes for severity with glow effects */
    .severity-low .diagnosis-bar-fill {
        background: linear-gradient(90deg, #00c853 0%, #69f0ae 100%);
        box-shadow: 0 0 12px rgba(0, 200, 83, 0.5), 0 0 4px rgba(0, 200, 83, 0.3);
    }
    .severity-low .diagnosis-percent {
        color: #69f0ae;
    }

    .severity-medium .diagnosis-bar-fill {
        background: linear-gradient(90deg, #ff9800 0%, #ffc107 100%);
        box-shadow: 0 0 12px rgba(255, 152, 0, 0.5), 0 0 4px rgba(255, 152, 0, 0.3);
    }
    .severity-medium .diagnosis-percent {
        color: #ffc107;
    }

    .severity-high .diagnosis-bar-fill {
        background: linear-gradient(90deg, #f44336 0%, #ff5252 100%);
        box-shadow: 0 0 12px rgba(244, 67, 54, 0.5), 0 0 4px rgba(244, 67, 54, 0.3);
    }
    .severity-high .diagnosis-percent {
        color: #ff5252;
    }

    /* Responsive Design for Diagnosis Dashboard */
    @media (max-width: 768px) {
        .diagnosis-dashboard {
            padding: 16px;
            border-radius: 12px;
        }

        .diagnosis-row {
            padding: 10px 12px;
            flex-wrap: wrap;
        }

        .diagnosis-rank {
            width: 28px;
            font-size: 0.85em;
        }

        .diagnosis-name {
            flex: 1;
            min-width: 100px;
            max-width: none;
            font-size: 0.9em;
        }

        .diagnosis-bar-container {
            order: 3;
            width: 100%;
            margin: 8px 0 0 0;
            flex-basis: 100%;
        }

        .diagnosis-percent {
            width: auto;
            margin-left: auto;
            font-size: 0.85em;
        }
    }

    @media (max-width: 480px) {
        .diagnosis-dashboard {
            padding: 12px;
            margin-top: 4px;
        }

        .diagnosis-dashboard-title {
            font-size: 0.75em;
            margin-bottom: 12px;
            padding-bottom: 8px;
        }

        .diagnosis-row {
            padding: 8px 10px;
            margin: 6px 0;
        }

        .diagnosis-rank {
            width: 24px;
            font-size: 0.8em;
        }

        .diagnosis-name {
            font-size: 0.85em;
        }

        .diagnosis-percent {
            font-size: 0.8em;
        }

        .diagnosis-bar-track {
            height: 5px;
        }
    }

    /* Footer Styles */
    .footer-container {
        margin-top: 40px;
        padding: 30px;
        background: linear-gradient(135deg, #2c3e50 0%, #1a252f 100%);
        border-radius: 16px;
        color: white;
        text-align: center;
    }

    .footer-content {
        max-width: 800px;
        margin: 0 auto;
    }

    .footer-acknowledgement {
        font-size: 1em;
        margin-bottom: 16px;
        padding-bottom: 16px;
        border-bottom: 1px solid rgba(255,255,255,0.2);
    }

    .footer-acknowledgement a {
        color: #3498db;
        text-decoration: none;
        font-weight: 600;
    }

    .footer-acknowledgement a:hover {
        text-decoration: underline;
    }

    .footer-disclaimer {
        font-size: 0.9em;
        color: rgba(255,255,255,0.7);
        padding: 12px 20px;
        background: rgba(231, 76, 60, 0.2);
        border-radius: 8px;
        border: 1px solid rgba(231, 76, 60, 0.3);
    }

    .footer-disclaimer strong {
        color: #e74c3c;
    }
    """

    with gr.Blocks(css=custom_css, title="HeartWatch AI", theme=gr.themes.Soft()) as demo:
        # Animated Header with Pulsing Heart
        gr.HTML("""
        <div class="main-header">
            <div class="header-content">
                <div class="heart-container">
                    <svg class="heart-svg" viewBox="0 0 32 29.6">
                        <path fill="white" d="M23.6,0c-3.4,0-6.3,2.7-7.6,5.6C14.7,2.7,11.8,0,8.4,0C3.8,0,0,3.8,0,8.4c0,9.4,9.5,11.9,16,21.2c6.1-9.3,16-12.1,16-21.2C32,3.8,28.2,0,23.6,0z"/>
                    </svg>
                    <svg class="ecg-line" viewBox="0 0 200 40">
                        <path class="ecg-path" d="M0,20 L40,20 L50,20 L55,5 L60,35 L65,10 L70,25 L75,20 L120,20 L130,20 L135,8 L140,32 L145,12 L150,24 L155,20 L200,20"/>
                    </svg>
                </div>
                <h1>HeartWatch AI</h1>
                <p>AI-Powered 12-Lead ECG Analysis</p>
            </div>
        </div>
        """)

        # Quick start notice
        gr.HTML("""
        <div class="quick-start">
            <strong>🚀 Quick Start:</strong> Select a sample ECG below and click "Analyze" to see the AI analysis instantly!
        </div>
        """)

        with gr.Tabs() as tabs:
            # Tab 1: Try Sample ECGs (DEFAULT - First Tab)
            with gr.TabItem("🎯 Try Sample ECGs", id=0):
                gr.Markdown("""
                ### Select a Sample ECG
                Choose from our collection of real ECG recordings to see the AI analysis in action.
                """)

                with gr.Row():
                    with gr.Column(scale=1):
                        # Sample selection with radio buttons for better UX
                        if sample_names:
                            sample_radio = gr.Radio(
                                choices=sample_names,
                                value=sample_names[0],
                                label="Available ECG Samples",
                                info="Click on a sample to select it"
                            )

                            analyze_sample_btn = gr.Button(
                                "🔍 Analyze Selected ECG",
                                variant="primary",
                                size="lg"
                            )
                        else:
                            gr.Markdown("⚠️ No sample ECGs found. Please use the Upload tab.")
                            sample_radio = gr.Radio(choices=[], label="No samples available")
                            analyze_sample_btn = gr.Button("Analyze", interactive=False)

                    with gr.Column(scale=2):
                        sample_summary = gr.HTML(
                            value="<p>👆 Select a sample and click <strong>Analyze</strong> to see results.</p>",
                            label="Analysis Summary"
                        )

                with gr.Row():
                    sample_ecg_plot = gr.Plot(label="12-Lead ECG Waveform")

                with gr.Row():
                    with gr.Column():
                        sample_diagnosis_plot = gr.Plot(label="Diagnosis Probabilities")
                    with gr.Column():
                        sample_risk_plot = gr.Plot(label="Risk Assessment Gauges")

                if sample_names:
                    analyze_sample_btn.click(
                        fn=analyze_sample_by_name,
                        inputs=[sample_radio],
                        outputs=[sample_ecg_plot, sample_diagnosis_plot, sample_risk_plot, sample_summary]
                    )

            # Tab 2: Upload Your Own ECG
            with gr.TabItem("📤 Upload Your ECG", id=1):
                gr.Markdown("""
                ### Upload Your Own ECG Recording
                Have your own ECG data? Upload it here for analysis.
                """)

                with gr.Row():
                    with gr.Column(scale=1):
                        file_input = gr.File(
                            label="Upload ECG File (.npy)",
                            file_types=[".npy"],
                            type="filepath"
                        )
                        analyze_btn = gr.Button(
                            "🔍 Analyze Uploaded ECG",
                            variant="primary",
                            size="lg"
                        )

                        gr.Markdown("""
                        **Expected Format:**
                        - **File type:** NumPy array (.npy)
                        - **Shape:** (2500, 12) or (12, 2500)
                        - **Leads:** I, II, III, aVR, aVL, aVF, V1-V6
                        - **Duration:** 10 seconds at 250 Hz

                        **Tip:** Use `numpy.save('ecg.npy', signal)` to create compatible files.
                        """)

                    with gr.Column(scale=2):
                        upload_summary = gr.HTML(
                            value="<p>👆 Upload a .npy file and click <strong>Analyze</strong> to see results.</p>",
                            label="Summary"
                        )

                with gr.Row():
                    upload_ecg_plot = gr.Plot(label="12-Lead ECG Waveform")

                with gr.Row():
                    with gr.Column():
                        upload_diagnosis_plot = gr.Plot(label="Diagnosis Probabilities")
                    with gr.Column():
                        upload_risk_plot = gr.Plot(label="Risk Assessment Gauges")

                analyze_btn.click(
                    fn=analyze_uploaded_file,
                    inputs=[file_input],
                    outputs=[upload_ecg_plot, upload_diagnosis_plot, upload_risk_plot, upload_summary]
                )

            # Tab 3: About
            with gr.TabItem("ℹ️ About", id=2):
                gr.Markdown("""
                ## About HeartWatch AI

                HeartWatch AI is a deep learning-based ECG analysis system powered by state-of-the-art models.

                ### 🧠 AI Models

                | Model | Description |
                |-------|-------------|
                | **77-Class Diagnosis** | Detects 77 different ECG patterns and cardiac conditions |
                | **LVEF < 40%** | Predicts reduced left ventricular ejection fraction |
                | **LVEF < 50%** | Predicts moderately reduced ejection fraction |
                | **5-Year AFib Risk** | Estimates risk of developing Atrial Fibrillation |

                ### 📊 Technical Details

                - **Architecture:** EfficientNetV2 (TorchScript optimized)
                - **Input:** 12-lead ECG, 10 seconds, 250 Hz
                - **Inference:** CPU-optimized for accessibility
                - **Training Data:** Large clinical ECG datasets

                ### ⚠️ Important Disclaimer

                **This is a research demonstration tool.**

                The predictions provided should **NOT** be used for clinical decision-making.
                Always consult qualified healthcare professionals for medical advice and diagnosis.

                ### 📚 References

                - Models based on the DeepECG project
                - Sample ECGs from MIT-BIH Arrhythmia Database (PhysioNet)

                ---
                *Built with Gradio and PyTorch*
                """)

        # Modern Footer with Acknowledgement and Disclaimer
        gr.HTML("""
        <div class="footer-container">
            <div class="footer-content">
                <div class="footer-acknowledgement">
                    Based on <a href="https://github.com/HeartWise-AI/DeepECG_Docker" target="_blank">HeartWise-AI/DeepECG_Docker</a>
                </div>
                <div class="footer-disclaimer">
                    <strong>Disclaimer:</strong> This is a research demo. Not for clinical use.
                </div>
            </div>
        </div>
        """)

    return demo


# Create and launch the demo
if __name__ == "__main__":
    # Create and launch demo
    demo = create_demo_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )