app.py

"""
Phramer AI - Main Gradio Interface
By Pariente AI, for MIA TV Series

Multimodal tool that reads images and turns them into refined, photo-realistic prompts.
Ready for Midjourney, Flux or any generative engine.
"""

import gradio as gr
import logging
import warnings
import os
from typing import Tuple

from config import APP_CONFIG, ENVIRONMENT
from processor import process_image_simple, phramer_optimizer
from utils import setup_logging, clean_memory

# Configure environment
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Setup logging
setup_logging(ENVIRONMENT["log_level"])
logger = logging.getLogger(__name__)


def process_image_interface(image) -> Tuple[str, str, str]:
    """
    Main interface function for image processing
    
    Args:
        image: Input image from Gradio interface
        
    Returns:
        Tuple of (prompt, analysis_report, score_html)
    """
    try:
        if image is None:
            return (
                "Please upload an image to analyze",
                "No image provided for analysis.",
                '<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Quality Score</div></div>'
            )
        
        logger.info("Processing image through Phramer AI interface")
        prompt, report, score_html = process_image_simple(image)
        
        return prompt, report, score_html
        
    except Exception as e:
        logger.error(f"Interface processing error: {e}", exc_info=True)
        error_msg = f"Processing failed: {str(e)}"
        
        return (
            "❌ Processing failed",
            f"**Error:** {error_msg}\n\nPlease try again with a different image.",
            '<div style="text-align: center; padding: 1rem; color: red;"><div style="font-size: 2rem;">0</div><div style="font-size: 0.875rem;">Error</div></div>'
        )


def clear_interface() -> Tuple[str, str, str]:
    """Clear all interface outputs and free memory"""
    clean_memory()
    logger.info("Interface cleared")
    
    return (
        "",
        "",
        '<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Quality Score</div></div>'
    )


def get_stats_info() -> str:
    """Get current processing statistics"""
    try:
        stats = phramer_optimizer.get_enhanced_stats()
        
        stats_text = f"""**Processing Statistics:**
• **Total Images:** {stats['total_processed']}
• **Successful:** {stats['successful_analyses']}
• **Failed:** {stats['failed_analyses']}
• **Success Rate:** {stats['success_rate']:.1%}
• **Average Time:** {stats['average_processing_time']:.1f}s
• **Device:** {stats['device_info']['device'].upper()}
"""
        return stats_text
        
    except Exception as e:
        logger.error(f"Stats retrieval error: {e}")
        return "Statistics unavailable"


def create_interface():
    """Create the main Gradio interface"""
    
    # Updated CSS with Phramer AI branding
    css = """
    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap');
    
    .gradio-container {
        max-width: 1600px !important;
        margin: 0 auto !important;
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
        background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%) !important;
    }
    
    /* Text visibility fixes */
    .markdown-text, .markdown-text *, 
    .prose, .prose *,
    .gr-markdown, .gr-markdown *,
    div[class*="markdown"], div[class*="markdown"] * {
        color: #1f2937 !important;
    }
    
    .markdown-text h1, .markdown-text h2, .markdown-text h3, 
    .prose h1, .prose h2, .prose h3,
    .gr-markdown h1, .gr-markdown h2, .gr-markdown h3 {
        color: #111827 !important;
        font-weight: 700 !important;
    }
    
    .markdown-text p, .markdown-text li, .markdown-text ul, .markdown-text ol,
    .prose p, .prose li, .prose ul, .prose ol,
    .gr-markdown p, .gr-markdown li, .gr-markdown ul, .gr-markdown ol {
        color: #374151 !important;
    }
    
    .markdown-text strong, .prose strong, .gr-markdown strong {
        color: #111827 !important;
        font-weight: 700 !important;
    }
    
    /* Header styling */
    .main-header {
        text-align: center;
        padding: 3rem 0 4rem 0;
        background: linear-gradient(135deg, #0c0a09 0%, #1c1917 30%, #292524 60%, #44403c 100%);
        color: white;
        margin: -2rem -2rem 3rem -2rem;
        border-radius: 0 0 32px 32px;
        box-shadow: 0 20px 50px -10px rgba(0, 0, 0, 0.25);
        position: relative;
        overflow: hidden;
    }
    
    .main-header::before {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;
        background: linear-gradient(45deg, rgba(59, 130, 246, 0.1) 0%, rgba(147, 51, 234, 0.1) 50%, rgba(236, 72, 153, 0.1) 100%);
        z-index: 1;
    }
    
    .main-title {
        font-size: 4rem !important;
        font-weight: 900 !important;
        margin: 0 0 1rem 0 !important;
        letter-spacing: -0.05em !important;
        background: linear-gradient(135deg, #60a5fa 0%, #3b82f6 25%, #8b5cf6 50%, #a855f7 75%, #ec4899 100%);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        background-clip: text;
        position: relative;
        z-index: 2;
    }
    
    .subtitle {
        font-size: 1.5rem !important;
        font-weight: 500 !important;
        opacity: 0.95 !important;
        margin: 0 0 0.5rem 0 !important;
        position: relative;
        z-index: 2;
        color: #ffffff !important;
    }
    
    .tagline {
        font-size: 1.1rem !important;
        font-weight: 400 !important;
        opacity: 0.85 !important;
        margin: 0 !important;
        position: relative;
        z-index: 2;
        color: #e5e7eb !important;
        font-style: italic;
    }
    
    .prompt-output {
        font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', monospace !important;
        font-size: 15px !important;
        line-height: 1.8 !important;
        background: linear-gradient(135deg, #ffffff 0%, #f8fafc 100%) !important;
        border: 2px solid #e2e8f0 !important;
        border-radius: 20px !important;
        padding: 2.5rem !important;
        box-shadow: 0 20px 50px -10px rgba(0, 0, 0, 0.1) !important;
        transition: all 0.3s ease !important;
        color: #1f2937 !important;
    }
    
    .prompt-output:hover {
        box-shadow: 0 25px 60px -5px rgba(0, 0, 0, 0.15) !important;
        transform: translateY(-2px) !important;
    }
    """
    
    with gr.Blocks(
        theme=gr.themes.Soft(),
        title="Phramer AI",
        css=css
    ) as interface:
        
        gr.HTML("""
        <div class="main-header">
            <div class="main-title">Phramer AI</div>
            <div class="subtitle">By Pariente AI, for MIA TV Series</div>
            <div class="tagline">Multimodal tool that reads images and turns them into refined, photo-realistic prompts</div>
        </div>
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("## Image Analysis")
                
                image_input = gr.Image(
                    label="Upload image for analysis",
                    type="pil",
                    height=500
                )
                
                analyze_btn = gr.Button(
                    "🔍 Generate Prompt",
                    variant="primary",
                    size="lg"
                )
                
                gr.Markdown("""
                ### How Phramer AI Works:
                
                **1. Deep Analysis:** Custom Bagel architecture analyzes your image for semantic-visual understanding.
                
                **2. Knowledge Enhancement:** Applies curated photographic knowledge base with camera settings and composition principles.
                
                **3. Prompt Generation:** Creates structured prompts with technical details, mood, and style specifications.
                
                **4. Multi-Engine Ready:** Optimized for Flux, Midjourney, and other diffusion platforms.
                
                **Perfect for:** Cinematic storyboards, photorealistic scenes, visual concept exploration.
                
                **Supported formats:** JPG, PNG, WebP up to 1024px
                """)
                
                # Statistics
                with gr.Accordion("📊 Processing Statistics", open=False):
                    stats_output = gr.Markdown(value="No processing completed yet.")
                    refresh_stats_btn = gr.Button("Refresh Stats", size="sm")
            
            with gr.Column(scale=1):
                gr.Markdown("## Generated Prompt")
                
                score_output = gr.HTML(
                    value='<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Quality Score</div></div>'
                )
                
                prompt_output = gr.Textbox(
                    label="🎯 Photo-Realistic Prompt",
                    placeholder="Upload an image to generate a refined prompt ready for any generative engine...",
                    lines=12,
                    max_lines=20,
                    show_copy_button=True
                )
                
                info_output = gr.Markdown(value="")
                
                clear_btn = gr.Button("🗑️ Clear Analysis", size="sm")
        
        # Event handlers
        analyze_btn.click(
            fn=process_image_interface,
            inputs=[image_input],
            outputs=[prompt_output, info_output, score_output]
        )
        
        clear_btn.click(
            fn=clear_interface,
            outputs=[prompt_output, info_output, score_output]
        )
        
        refresh_stats_btn.click(
            fn=get_stats_info,
            outputs=stats_output
        )
        
        gr.Markdown("""
        ---
        ### About Phramer AI
        
        **Phramer AI** is an advanced multimodal system developed by **Pariente AI** for the **MIA TV Series** creative pipeline.
        
        This tool bridges the gap between image understanding and generative prompting, analyzing uploaded images through 
        a custom Bagel architecture and enhancing them with professional photographic knowledge to create detailed, 
        structured prompts ready for Flux, Midjourney, or any diffusion-based platform.
        
        Whether creating cinematic storyboards, photorealistic scenes, or exploring visual concepts, Phramer AI delivers 
        refined prompts with camera settings, composition hints, mood specifications, and style guidance.
        
        **Pariente AI** • Advanced Multimodal AI Research & Development • **MIA TV Series**
        """)
    
    return interface


def main():
    """Main application entry point"""
    logger.info("Starting Phramer AI by Pariente AI")
    
    # Create and launch interface
    demo = create_interface()
    
    # Launch with proper configuration
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True,
        show_error=True
    )


if __name__ == "__main__":
    main()