# app.py

import gradio as gr
from tabs.speech_stress_analysis import create_voice_stress_tab
from tabs.speech_emotion_recognition import create_emotion_recognition_tab
from tabs.FACS_analysis import create_facs_analysis_tab
from tabs.heart_rate_variability import create_heart_rate_variability_tab
from tabs.deception_detection import create_deception_detection_tab, load_models
import logging
import torch
from typing import Dict

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

# Custom CSS for better styling
CUSTOM_CSS = """
/* Global styles */
.gradio-container {
    font-family: 'Arial', sans-serif;
    max-width: 1200px;
    margin: auto;
    padding: 20px;
    background-color: #f8f9fa;
}

/* Header styling */
h1 {
    color: #2c3e50;
    text-align: center;
    padding: 20px 0;
    margin-bottom: 30px;
    border-bottom: 2px solid #3498db;
}

/* Tab navigation styling */
.gradio-tabs-nav {
    background-color: #ffffff;
    border-radius: 8px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    margin-bottom: 20px;
}

/* Content areas */
.content-area {
    background: white;
    padding: 20px;
    border-radius: 8px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    margin-top: 20px;
}

/* Results area */
.results-area {
    background-color: #ffffff;
    padding: 20px;
    border-radius: 8px;
    margin-top: 20px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}

/* Disclaimer styling */
.disclaimer {
    background-color: #f8f9fa;
    border-left: 4px solid #3498db;
    padding: 15px;
    margin-top: 30px;
    font-size: 0.9em;
    color: #666;
}
"""

# HTML content
HEADER_HTML = """
<div style="text-align: center; padding: 20px;">
    <h1>AI-Driven Multimodal Emotional State Analysis</h1>
    <p style="font-size: 1.2em; color: #666;">
        Comprehensive analysis of stress, emotion, and truthfulness through facial expressions, 
        heart rate variability, and speech patterns.
    </p>
</div>
"""

DISCLAIMER_HTML = """
<div class="disclaimer">
    <h3>Important Notice</h3>
    <p>This application provides AI-driven analysis for:</p>
    <ul>
        <li>Stress and emotion detection</li>
        <li>Heart rate variability analysis</li>
        <li>Speech pattern analysis</li>
        <li>Truth/deception indication</li>
    </ul>
    <p><strong>Disclaimer:</strong> This tool is for research and informational purposes only. 
    It should not be used as a substitute for professional medical advice, diagnosis, or treatment. 
    The deception detection feature is experimental and should not be used as definitive proof 
    of truthfulness or deception.</p>
</div>
"""

# Tab structure
TAB_STRUCTURE = [
    ("Visual Analysis", [
        ("FACS Analysis", create_facs_analysis_tab),
        ("Heart Rate Variability", create_heart_rate_variability_tab),
        ("Truth/Deception Detection", create_deception_detection_tab)  # Pass models here
    ]),
    ("Speech Analysis", [
        ("Speech Stress", create_voice_stress_tab),
        ("Speech Emotion", create_emotion_recognition_tab)
    ])
]

def create_demo(models: Dict[str, torch.nn.Module]):
    """Create and configure the Gradio interface."""
    with gr.Blocks(css=CUSTOM_CSS, title="Multimodal Emotional State Analysis") as demo:
        # Header
        gr.HTML(HEADER_HTML)
        
        # Main content area with Tabs
        with gr.Tabs():
            for main_tab, sub_tabs in TAB_STRUCTURE:
                with gr.Tab(main_tab):
                    with gr.Column():
                        with gr.Tabs():
                            for sub_tab, create_fn in sub_tabs:
                                with gr.Tab(sub_tab):
                                    if main_tab == "Visual Analysis" and sub_tab == "Truth/Deception Detection":
                                        # Pass loaded models to the deception detection tab
                                        create_fn(models)
                                    else:
                                        create_fn()
                    # Add help information below sub-tabs
                    if main_tab == "Visual Analysis":
                        gr.Markdown("""
                        ### Visual Analysis Features
                        - **FACS Analysis**: Facial Action Coding System for emotion detection
                        - **Heart Rate Variability**: Stress and wellness indicators
                        - **Truth/Deception Detection**: Physiological response analysis
                        
                        **For best results:**
                        1. Use good lighting
                        2. Face the camera directly
                        3. Minimize movement during recording
                        """)
                    elif main_tab == "Speech Analysis":
                        gr.Markdown("""
                        ### Speech Analysis Features
                        - **Speech Stress**: Voice stress analysis
                        - **Speech Emotion**: Emotional content detection
                        
                        **For best results:**
                        1. Use a quiet environment
                        2. Speak clearly
                        3. Avoid background noise
                        """)

        # Disclaimer
        gr.HTML(DISCLAIMER_HTML)
    
    return demo

def main():
    """Main function to run the application."""
    # Load models once and pass them to the deception detection tab
    models_loaded = load_models()
    if not models_loaded:
        logger.error("No models loaded. Exiting application.")
        return

    # Initialize Gradio interface
    demo = create_demo(models_loaded)

    # Configure and launch the interface
    demo.queue()  # Enable queuing without specific concurrency count
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=True,
        show_error=True
    )

if __name__ == "__main__":
    main()