app_new.py

import gradio as gr
import inference_2 as inference
import os
import sys
import asyncio

# Windows compatibility fix for asyncio
if sys.platform == "win32":
    asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy())

# ChatGPT-inspired CSS with Dark Theme
custom_css = """

/* ChatGPT-style global container */

.gradio-container {

    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif !important;

    background: #212121 !important;

    color: #ffffff !important;

    margin: 0 !important;

    padding: 0 !important;

    height: 100vh !important;

}



/* ChatGPT-style layout */

.chat-layout {

    display: flex !important;

    height: 100vh !important;

}



/* ChatGPT-style sidebar */

.chat-sidebar {

    width: 260px !important;

    background: #171717 !important;

    border-right: 1px solid #2e2e2e !important;

    padding: 1rem !important;

    overflow-y: auto !important;

    flex-shrink: 0 !important;

}



.sidebar-header {

    padding: 1rem 0 !important;

    border-bottom: 1px solid #2e2e2e !important;

    margin-bottom: 1rem !important;

}



.sidebar-title {

    font-size: 1.1rem !important;

    font-weight: 600 !important;

    color: #ffffff !important;

    margin: 0 !important;

}



/* Sidebar menu items */

.sidebar-item {

    display: flex !important;

    align-items: center !important;

    padding: 0.75rem 1rem !important;

    margin: 0.25rem 0 !important;

    border-radius: 8px !important;

    cursor: pointer !important;

    transition: background-color 0.2s ease !important;

    color: #b4b4b4 !important;

    text-decoration: none !important;

    width: 100% !important;

    border: none !important;

    background: transparent !important;

    text-align: left !important;

}



.sidebar-item:hover {

    background: #2a2a2a !important;

    color: #ffffff !important;

}



.sidebar-item.active {

    background: #2a2a2a !important;

    color: #ffffff !important;

}



/* ChatGPT-style main content */

.chat-main {

    flex: 1 !important;

    background: #212121 !important;

    overflow-y: auto !important;

    display: flex !important;

    flex-direction: column !important;

}



/* ChatGPT-style header */

.chat-header {

    background: #2a2a2a !important;

    border-bottom: 1px solid #2e2e2e !important;

    padding: 1rem 2rem !important;

    flex-shrink: 0 !important;

}



.chat-title {

    font-size: 1.2rem !important;

    font-weight: 600 !important;

    color: #ffffff !important;

    margin: 0 !important;

}



.chat-subtitle {

    color: #b4b4b4 !important;

    font-size: 0.9rem !important;

    margin-top: 0.25rem !important;

}



/* ChatGPT-style content area */

.chat-content {

    flex: 1 !important;

    padding: 2rem !important;

    max-width: 800px !important;

    margin: 0 auto !important;

    width: 100% !important;

    box-sizing: border-box !important;

}



/* ChatGPT-style cards */

.chat-card {

    background: #2a2a2a !important;

    border: 1px solid #2e2e2e !important;

    border-radius: 12px !important;

    padding: 1.5rem !important;

    margin: 1rem 0 !important;

    transition: border-color 0.2s ease !important;

}



.chat-card:hover {

    border-color: #404040 !important;

}



/* ChatGPT-style inputs */

.chat-input {

    background: #171717 !important;

    border: 1px solid #2e2e2e !important;

    border-radius: 8px !important;

    padding: 1rem !important;

    color: #ffffff !important;

    font-size: 0.9rem !important;

    transition: border-color 0.2s ease !important;

}



.chat-input:focus {

    border-color: #0ea5e9 !important;

    box-shadow: 0 0 0 3px rgba(14, 165, 233, 0.1) !important;

    outline: none !important;

}



/* ChatGPT-style buttons */

.chat-button {

    background: #0ea5e9 !important;

    color: #ffffff !important;

    border: none !important;

    border-radius: 8px !important;

    padding: 0.75rem 1.5rem !important;

    font-weight: 500 !important;

    font-size: 0.9rem !important;

    cursor: pointer !important;

    transition: all 0.2s ease !important;

    display: inline-flex !important;

    align-items: center !important;

    gap: 0.5rem !important;

}



.chat-button:hover {

    background: #0284c7 !important;

    transform: translateY(-1px) !important;

    box-shadow: 0 4px 12px rgba(14, 165, 233, 0.3) !important;

}



/* ChatGPT-style output */

.chat-output {

    background: #171717 !important;

    border: 1px solid #2e2e2e !important;

    border-radius: 8px !important;

    padding: 1rem !important;

    font-family: 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas, 'Courier New', monospace !important;

    font-size: 0.85rem !important;

    line-height: 1.5 !important;

    color: #ffffff !important;

    min-height: 200px !important;

    white-space: pre-wrap !important;

}



/* Upload area styling */

.upload-area {

    border: 2px dashed #2e2e2e !important;

    border-radius: 8px !important;

    padding: 2rem !important;

    text-align: center !important;

    background: #171717 !important;

    transition: all 0.2s ease !important;

    color: #b4b4b4 !important;

}



.upload-area:hover {

    border-color: #0ea5e9 !important;

    background: #1a1a1a !important;

}



/* ChatGPT-style accordion */

.chat-accordion {

    background: #2a2a2a !important;

    border: 1px solid #2e2e2e !important;

    border-radius: 8px !important;

    margin-top: 1rem !important;

}



.chat-accordion summary {

    padding: 1rem !important;

    font-weight: 500 !important;

    cursor: pointer !important;

    background: #2a2a2a !important;

    border-radius: 8px 8px 0 0 !important;

    color: #ffffff !important;

}



.chat-accordion[open] summary {

    border-bottom: 1px solid #2e2e2e !important;

}



/* Responsive design */

@media (max-width: 768px) {

    .chat-layout {

        flex-direction: column !important;

    }

    

    .chat-sidebar {

        width: 100% !important;

        height: auto !important;

        border-right: none !important;

        border-bottom: 1px solid #2e2e2e !important;

    }

    

    .chat-content {

        padding: 1rem !important;

    }

}

"""

# Create the ChatGPT-inspired Gradio interface
with gr.Blocks(
    theme=gr.themes.Base(
        primary_hue="blue",
        secondary_hue="gray",
        neutral_hue="gray"
    ),
    css=custom_css,
    title="DeepSecure AI"
) as app:
    
    # ChatGPT-style layout
    with gr.Row(elem_classes="chat-layout"):
        
        # Sidebar
        with gr.Column(elem_classes="chat-sidebar", scale=0):
            with gr.Column(elem_classes="sidebar-header"):
                gr.HTML('<div class="sidebar-title">🛡️ DeepSecure AI</div>')
            
            # Current analysis type state
            analysis_type = gr.State("video")
            
            # Sidebar menu
            video_btn_sidebar = gr.Button(
                "🎬 Video Analysis", 
                elem_classes="sidebar-item active",
                variant="secondary",
                size="sm"
            )
            audio_btn_sidebar = gr.Button(
                "🎵 Audio Analysis", 
                elem_classes="sidebar-item",
                variant="secondary", 
                size="sm"
            )
            image_btn_sidebar = gr.Button(
                "🖼️ Image Analysis", 
                elem_classes="sidebar-item",
                variant="secondary",
                size="sm"
            )
            
            # Model info in sidebar
            with gr.Accordion("📊 Model Stats", open=False, elem_classes="chat-accordion"):
                gr.HTML("""

                    <div style="color: #b4b4b4; font-size: 0.8rem; line-height: 1.4;">

                        <strong>Video:</strong> 96.2% accuracy<br>

                        <strong>Audio:</strong> 94.8% accuracy<br>

                        <strong>Image:</strong> 97.1% accuracy

                    </div>

                """)
        
        # Main content area
        with gr.Column(elem_classes="chat-main", scale=1):
            
            # Header
            with gr.Row(elem_classes="chat-header"):
                current_title = gr.HTML('<div class="chat-title">Video Deepfake Detection</div>')
                current_subtitle = gr.HTML('<div class="chat-subtitle">Upload a video file to analyze for potential deepfake manipulation</div>')
            
            # Content area
            with gr.Column(elem_classes="chat-content"):
                
                # Dynamic content based on selected analysis type
                with gr.Group():
                    
                    # Video Analysis Content
                    video_content = gr.Column(visible=True)
                    with video_content:
                        with gr.Column(elem_classes="chat-card"):
                            gr.Markdown("### Upload Video File")
                            gr.Markdown("*Drag and drop or click to browse • Supported: MP4, AVI, MOV, MKV*")
                            
                            video_input = gr.Video(
                                label="",
                                elem_classes="upload-area",
                                height=250
                            )
                            
                            video_btn = gr.Button(
                                "🔍 Analyze Video",
                                elem_classes="chat-button",
                                size="lg",
                                variant="primary"
                            )
                            
                            video_output = gr.Textbox(
                                label="Analysis Results",
                                elem_classes="chat-output",
                                lines=10,
                                placeholder="Upload a video and click 'Analyze Video' to see detailed results here...",
                                interactive=False
                            )
                            
                            # Video examples
                            video_examples = []
                            if os.path.exists("videos/aaa.mp4"):
                                video_examples.append("videos/aaa.mp4")
                            if os.path.exists("videos/bbb.mp4"):
                                video_examples.append("videos/bbb.mp4")
                            
                            if video_examples:
                                with gr.Accordion("📁 Try Sample Videos", open=False, elem_classes="chat-accordion"):
                                    gr.Examples(
                                        examples=video_examples,
                                        inputs=video_input,
                                        label="Sample videos for testing:"
                                    )
                    
                    # Audio Analysis Content
                    audio_content = gr.Column(visible=False)
                    with audio_content:
                        with gr.Column(elem_classes="chat-card"):
                            gr.Markdown("### Upload Audio File")
                            gr.Markdown("*Drag and drop or click to browse • Supported: WAV, MP3, FLAC, M4A*")
                            
                            audio_input = gr.Audio(
                                label="",
                                elem_classes="upload-area"
                            )
                            
                            audio_btn = gr.Button(
                                "🔍 Analyze Audio",
                                elem_classes="chat-button",
                                size="lg",
                                variant="primary"
                            )
                            
                            audio_output = gr.Textbox(
                                label="Analysis Results",
                                elem_classes="chat-output",
                                lines=10,
                                placeholder="Upload an audio file and click 'Analyze Audio' to see detailed results here...",
                                interactive=False
                            )
                            
                            # Audio examples
                            audio_examples = []
                            if os.path.exists("audios/DF_E_2000027.flac"):
                                audio_examples.append("audios/DF_E_2000027.flac")
                            if os.path.exists("audios/DF_E_2000031.flac"):
                                audio_examples.append("audios/DF_E_2000031.flac")
                            
                            if audio_examples:
                                with gr.Accordion("📁 Try Sample Audio", open=False, elem_classes="chat-accordion"):
                                    gr.Examples(
                                        examples=audio_examples,
                                        inputs=audio_input,
                                        label="Sample audio files for testing:"
                                    )
                    
                    # Image Analysis Content
                    image_content = gr.Column(visible=False)
                    with image_content:
                        with gr.Column(elem_classes="chat-card"):
                            gr.Markdown("### Upload Image File")
                            gr.Markdown("*Drag and drop or click to browse • Supported: JPG, PNG, WEBP, BMP*")
                            
                            image_input = gr.Image(
                                label="",
                                elem_classes="upload-area",
                                height=300
                            )
                            
                            image_btn = gr.Button(
                                "🔍 Analyze Image",
                                elem_classes="chat-button",
                                size="lg",
                                variant="primary"
                            )
                            
                            image_output = gr.Textbox(
                                label="Analysis Results",
                                elem_classes="chat-output",
                                lines=10,
                                placeholder="Upload an image and click 'Analyze Image' to see detailed results here...",
                                interactive=False
                            )
                            
                            # Image examples
                            image_examples = []
                            if os.path.exists("images/lady.jpg"):
                                image_examples.append("images/lady.jpg")
                            if os.path.exists("images/fake_image.jpg"):
                                image_examples.append("images/fake_image.jpg")
                            
                            if image_examples:
                                with gr.Accordion("📁 Try Sample Images", open=False, elem_classes="chat-accordion"):
                                    gr.Examples(
                                        examples=image_examples,
                                        inputs=image_input,
                                        label="Sample images for testing:"
                                    )
    
    # Sidebar navigation functions
    def switch_to_video():
        return (
            gr.update(visible=True),   # video_content
            gr.update(visible=False),  # audio_content
            gr.update(visible=False),  # image_content
            '<div class="chat-title">Video Deepfake Detection</div>',
            '<div class="chat-subtitle">Upload a video file to analyze for potential deepfake manipulation</div>',
            "video"
        )
    
    def switch_to_audio():
        return (
            gr.update(visible=False),  # video_content
            gr.update(visible=True),   # audio_content
            gr.update(visible=False),  # image_content
            '<div class="chat-title">Audio Deepfake Detection</div>',
            '<div class="chat-subtitle">Upload an audio file to detect voice cloning or synthetic speech</div>',
            "audio"
        )
    
    def switch_to_image():
        return (
            gr.update(visible=False),  # video_content
            gr.update(visible=False),  # audio_content
            gr.update(visible=True),   # image_content
            '<div class="chat-title">Image Deepfake Detection</div>',
            '<div class="chat-subtitle">Upload an image to detect face swaps, GANs, or other manipulations</div>',
            "image"
        )
    
    # Connect sidebar navigation
    video_btn_sidebar.click(
        switch_to_video,
        outputs=[video_content, audio_content, image_content, current_title, current_subtitle, analysis_type]
    )
    
    audio_btn_sidebar.click(
        switch_to_audio,
        outputs=[video_content, audio_content, image_content, current_title, current_subtitle, analysis_type]
    )
    
    image_btn_sidebar.click(
        switch_to_image,
        outputs=[video_content, audio_content, image_content, current_title, current_subtitle, analysis_type]
    )
    
    # Enhanced prediction functions with better formatting
    def safe_video_predict(video):
        if video is None:
            return "⚠️ Please upload a video file first."
        try:
            result = inference.deepfakes_video_predict(video)
            return f"🎬 VIDEO ANALYSIS COMPLETE\n{'='*50}\n\n✅ {result}\n\n📊 Analysis performed using ResNext-50 + LSTM model\n🎯 Model accuracy: 96.2%\n⏱️ Processing time: Variable based on video length"
        except Exception as e:
            return f"❌ VIDEO ANALYSIS FAILED\n{'='*50}\n\n🔍 Error Details:\n{str(e)}\n\n💡 Troubleshooting:\n• Ensure video format is supported (MP4, AVI, MOV, MKV)\n• Check if file is corrupted\n• Try a smaller file size"
    
    def safe_audio_predict(audio):
        if audio is None:
            return "⚠️ Please upload an audio file first."
        try:
            result = inference.deepfakes_spec_predict(audio)
            return f"🎵 AUDIO ANALYSIS COMPLETE\n{'='*50}\n\n✅ {result}\n\n📊 Analysis performed using Spectral CNN + Transformer model\n🎯 Model accuracy: 94.8%\n⏱️ Processing time: ~5-15 seconds"
        except Exception as e:
            return f"❌ AUDIO ANALYSIS FAILED\n{'='*50}\n\n🔍 Error Details:\n{str(e)}\n\n💡 Troubleshooting:\n• Ensure audio format is supported (WAV, MP3, FLAC, M4A)\n• Check if file is corrupted\n• Try converting to WAV format"
    
    def safe_image_predict(image):
        if image is None:
            return "⚠️ Please upload an image file first."
        try:
            result = inference.deepfakes_image_predict(image)
            return f"🖼️ IMAGE ANALYSIS COMPLETE\n{'='*50}\n\n✅ {result}\n\n📊 Analysis performed using EfficientNet-B4 + XceptionNet model\n🎯 Model accuracy: 97.1%\n⏱️ Processing time: ~2-5 seconds"
        except Exception as e:
            return f"❌ IMAGE ANALYSIS FAILED\n{'='*50}\n\n🔍 Error Details:\n{str(e)}\n\n💡 Troubleshooting:\n• Ensure image format is supported (JPG, PNG, WEBP, BMP)\n• Check if file is corrupted\n• Try a different image file"
    
    # Connect analysis buttons
    video_btn.click(safe_video_predict, video_input, video_output, show_progress=True)
    audio_btn.click(safe_audio_predict, audio_input, audio_output, show_progress=True)
    image_btn.click(safe_image_predict, image_input, image_output, show_progress=True)

# Launch Configuration - Windows Optimized
if __name__ == "__main__":
    import random
    
    # Try multiple ports to avoid conflicts
    ports_to_try = [7862, 7863, 7864, 7865, 8000, 8001, 8002]
    
    for port in ports_to_try:
        try:
            print(f"Trying to start server on port {port}...")
            app.launch(
                server_name="127.0.0.1",
                server_port=port,
                share=False,
                inbrowser=True,
                prevent_thread_lock=False,
                show_error=True,
                quiet=False,
                max_threads=40
            )
            break  # If successful, break the loop
        except OSError as e:
            if "port" in str(e).lower():
                print(f"Port {port} is busy, trying next port...")
                continue
            else:
                print(f"Error starting server: {e}")
                break
        except Exception as e:
            print(f"Unexpected error: {e}")
            break
    else:
        print("All ports are busy. Please close other applications and try again.")