Update app.py

app.py

@@ -1,96 +1,109 @@
 """
-Ultra Supreme Flux Optimizer - Main Gradio Interface
+Frame 0 Laboratory for MIA - Main Gradio Interface
+FLUX Prompt Optimizer with clean, professional interface
 """
 
 import gradio as gr
-import torch
-import gc
 import logging
 import warnings
 import os
+from typing import Tuple
 
-from optimizer import UltraSupremeOptimizer
-from constants import SCORE_GRADES
+from config import APP_CONFIG, ENVIRONMENT
+from processor import process_image_simple, flux_optimizer
+from utils import setup_logging, clean_memory
 
-# Configure warnings and environment
+# Configure environment
 warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", category=UserWarning)
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
+# Setup logging
+setup_logging(ENVIRONMENT["log_level"])
 logger = logging.getLogger(__name__)
 
-# NO inicializar el optimizer globalmente
-optimizer = None
 
-
-def get_optimizer():
-    """Get or create optimizer instance"""
-    global optimizer
-    if optimizer is None:
-        optimizer = UltraSupremeOptimizer()
-    return optimizer
-
-
-def process_ultra_supreme_analysis(image):
-    """Process image and generate ultra supreme analysis"""
-    try:
-        # Obtener optimizer (lazy initialization)
-        opt = get_optimizer()
-        
-        prompt, info, score, breakdown = opt.generate_ultra_supreme_prompt(image)
+def process_image_interface(image) -> Tuple[str, str, str]:
+    """
+    Main interface function for image processing
+    
+    Args:
+        image: Input image from Gradio interface
         
-        # Find appropriate grade based on score
-        grade_info = None
-        for threshold, grade_data in sorted(SCORE_GRADES.items(), reverse=True):
-            if score >= threshold:
-                grade_info = grade_data
-                break
+    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>'
+            )
         
-        if not grade_info:
-            grade_info = SCORE_GRADES[0]  # Default to lowest grade
-            
-        score_html = f'''
-        <div style="text-align: center; padding: 2rem; background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%); border: 3px solid {grade_info["color"]}; border-radius: 16px; margin: 1rem 0; box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1);">
-            <div style="font-size: 3rem; font-weight: 800; color: {grade_info["color"]}; margin: 0; text-shadow: 0 2px 4px rgba(0,0,0,0.1);">{score}</div>
-            <div style="font-size: 1.25rem; color: #15803d; margin: 0.5rem 0; text-transform: uppercase; letter-spacing: 0.1em; font-weight: 700;">{grade_info["grade"]}</div>
-            <div style="font-size: 1rem; color: #15803d; margin: 0; text-transform: uppercase; letter-spacing: 0.05em; font-weight: 500;">Ultra Supreme Intelligence Score</div>
-        </div>
-        '''
+        logger.info("Processing image through interface")
+        prompt, report, score_html = process_image_simple(image)
         
-        return prompt, info, score_html
+        return prompt, report, score_html
         
     except Exception as e:
-        logger.error(f"Ultra supreme wrapper error: {e}", exc_info=True)
-        error_msg = str(e)
-        if "GPU" in error_msg:
-            error_msg = "GPU processing failed. The system will automatically retry with CPU."
-        return "❌ Processing failed", f"Error: {error_msg}", '<div style="text-align: center; color: red;">Error</div>'
+        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_outputs():
-    """Clear all outputs and free memory"""
-    gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-    return "", "", '<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Ultra Supreme Score</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 = flux_optimizer.get_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 Gradio interface"""
+    """Create the main Gradio interface"""
     
+    # Custom CSS for clean, professional look
     css = """
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap');
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap');
     
     .gradio-container {
-        max-width: 1600px !important;
+        max-width: 1400px !important;
         margin: 0 auto !important;
         font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
         background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%) !important;
     }
     
-    /* FIX CRÍTICO PARA TEXTO BLANCO SOBRE BLANCO */
+    /* Text visibility fixes */
     .markdown-text, .markdown-text *, 
     .prose, .prose *,
     .gr-markdown, .gr-markdown *,
@@ -102,246 +115,252 @@ def create_interface():
     .prose h1, .prose h2, .prose h3,
     .gr-markdown h1, .gr-markdown h2, .gr-markdown h3 {
         color: #111827 !important;
-        font-weight: 700 !important;
+        font-weight: 600 !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 {
+    .markdown-text p, .markdown-text li,
+    .prose p, .prose li,
+    .gr-markdown p, .gr-markdown li {
         color: #374151 !important;
     }
     
     .markdown-text strong, .prose strong, .gr-markdown strong {
         color: #111827 !important;
-        font-weight: 700 !important;
-    }
-    
-    /* Asegurar que las listas sean visibles */
-    ul, ol {
-        color: #374151 !important;
-    }
-    
-    li {
-        color: #374151 !important;
-    }
-    
-    /* Bullets de listas */
-    ul li::marker {
-        color: #374151 !important;
+        font-weight: 600 !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%);
+        padding: 2.5rem 0 3rem 0;
+        background: linear-gradient(135deg, #1e293b 0%, #334155 50%, #475569 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;
+        margin: -2rem -2rem 2rem -2rem;
+        border-radius: 0 0 24px 24px;
+        box-shadow: 0 10px 30px -5px rgba(0, 0, 0, 0.2);
     }
     
     .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;
+        font-size: 2.5rem !important;
+        font-weight: 700 !important;
+        margin: 0 0 0.5rem 0 !important;
+        letter-spacing: -0.025em !important;
+        color: #ffffff !important;
     }
     
     .subtitle {
-        font-size: 1.5rem !important;
-        font-weight: 500 !important;
-        opacity: 0.95 !important;
+        font-size: 1.125rem !important;
+        font-weight: 400 !important;
+        opacity: 0.9 !important;
         margin: 0 !important;
-        position: relative;
-        z-index: 2;
-        color: #ffffff !important;
+        color: #cbd5e1 !important;
     }
     
+    /* Prompt output styling */
     .prompt-output {
-        font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', monospace !important;
-        font-size: 15px !important;
-        line-height: 1.8 !important;
+        font-family: 'SF Mono', 'Monaco', 'Consolas', monospace !important;
+        font-size: 14px !important;
+        line-height: 1.6 !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;
+        border-radius: 12px !important;
+        padding: 1.5rem !important;
+        box-shadow: 0 4px 15px -2px rgba(0, 0, 0, 0.08) !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;
-    }
-    
-    /* Fix para el output de información */
-    .gr-textbox label {
-        color: #374151 !important;
-    }
-    
-    /* Fix para footer */
-    footer, .footer, [class*="footer"] {
-        color: #374151 !important;
-    }
-    
-    footer *, .footer *, [class*="footer"] * {
-        color: #374151 !important;
-    }
-    
-    footer a, .footer a, [class*="footer"] a {
-        color: #3b82f6 !important;
-        text-decoration: underline;
+        box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.12) !important;
+        transform: translateY(-1px) !important;
     }
     
-    footer a:hover, .footer a:hover, [class*="footer"] a:hover {
-        color: #2563eb !important;
-    }
-    
-    /* Botones */
+    /* Button styling */
     .gr-button-primary {
         background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%) !important;
         border: none !important;
         color: white !important;
+        font-weight: 500 !important;
+        padding: 0.75rem 1.5rem !important;
+        border-radius: 8px !important;
+        transition: all 0.2s ease !important;
     }
     
     .gr-button-primary:hover {
         background: linear-gradient(135deg, #2563eb 0%, #1d4ed8 100%) !important;
-        transform: translateY(-1px);
-        box-shadow: 0 4px 12px rgba(37, 99, 235, 0.3);
+        transform: translateY(-1px) !important;
+        box-shadow: 0 4px 12px rgba(37, 99, 235, 0.3) !important;
     }
     
-    /* Asegurar que TODOS los elementos de texto sean visibles */
-    * {
-        -webkit-text-fill-color: initial !important;
+    .gr-button-secondary {
+        background: #f1f5f9 !important;
+        border: 1px solid #cbd5e1 !important;
+        color: #475569 !important;
+        font-weight: 500 !important;
+        border-radius: 8px !important;
     }
     
-    /* Solo el título principal mantiene su gradiente */
-    .main-title {
-        -webkit-text-fill-color: transparent !important;
+    .gr-button-secondary:hover {
+        background: #e2e8f0 !important;
+        border-color: #94a3b8 !important;
+    }
+    
+    /* Image upload area */
+    .gr-file-upload {
+        border: 2px dashed #cbd5e1 !important;
+        border-radius: 12px !important;
+        background: #f8fafc !important;
+    }
+    
+    /* Info boxes */
+    .info-box {
+        background: linear-gradient(135deg, #f0f9ff 0%, #e0f2fe 100%);
+        border: 1px solid #0284c7;
+        border-radius: 12px;
+        padding: 1rem;
+        margin: 1rem 0;
     }
     """
     
     with gr.Blocks(
-        theme=gr.themes.Soft(),
-        title="🚀 Ultra Supreme Flux Optimizer",
+        theme=gr.themes.Soft(
+            primary_hue="blue",
+            secondary_hue="slate",
+            neutral_hue="slate"
+        ),
+        title="Frame 0 Laboratory for MIA",
         css=css
     ) as interface:
         
+        # Header
         gr.HTML("""
         <div class="main-header">
-            <div class="main-title">🚀 ULTRA SUPREME FLUX OPTIMIZER</div>
-            <div class="subtitle">Maximum Absolute Intelligence • Triple CLIP Analysis • Zero Compromise • Research Supremacy</div>
+            <div class="main-title">Frame 0 Laboratory for MIA</div>
+            <div class="subtitle">Advanced Image Analysis & FLUX Prompt Optimization</div>
         </div>
         """)
         
+        # Main interface
         with gr.Row():
+            # Left column - Input
             with gr.Column(scale=1):
-                gr.Markdown("## 🧠 Ultra Supreme Analysis Engine")
+                gr.Markdown("## Image Analysis")
                 
                 image_input = gr.Image(
-                    label="Upload image for MAXIMUM intelligence analysis",
+                    label="Upload Image for Analysis",
                     type="pil",
-                    height=500
+                    height=400
                 )
                 
-                analyze_btn = gr.Button(
-                    "🚀 ULTRA SUPREME ANALYSIS",
-                    variant="primary",
-                    size="lg"
-                )
+                with gr.Row():
+                    analyze_btn = gr.Button(
+                        "🔍 Analyze Image",
+                        variant="primary",
+                        size="lg"
+                    )
+                    
+                    clear_btn = gr.Button(
+                        "🗑️ Clear",
+                        variant="secondary",
+                        size="lg"
+                    )
                 
+                # Information panel
                 gr.Markdown("""
-                ### 🔬 Maximum Absolute Intelligence
+                ### How it works:
                 
-                **🚀 Triple CLIP Interrogation:**
-                • Fast analysis for broad contextual mapping
-                • Classic analysis for detailed feature extraction  
-                • Best analysis for maximum depth intelligence
+                **1. Image Analysis:** Advanced AI models analyze your image to understand content, composition, and style.
                 
-                **🧠 Ultra Deep Feature Extraction:**
-                • Micro-age detection with confidence scoring
-                • Cultural/religious context with semantic analysis
-                • Facial micro-features and expression mapping
-                • Emotional state and micro-expression detection
-                • Environmental lighting and atmospheric analysis
-                • Body language and pose interpretation
-                • Technical photography optimization
+                **2. FLUX Optimization:** Applies proven rules for FLUX image generation including camera settings, lighting, and technical parameters.
                 
-                **⚡ Absolute Maximum Intelligence** - No configuration, no limits, no compromise.
+                **3. Quality Scoring:** Evaluates the optimized prompt across multiple dimensions for best results.
+                
+                **Supported formats:** JPG, PNG, WebP up to 1024px
                 """)
+                
+                # Statistics (collapsible)
+                with gr.Accordion("📊 Processing Stats", open=False):
+                    stats_output = gr.Markdown(value="No processing completed yet.")
+                    
+                    refresh_stats_btn = gr.Button("Refresh Stats", size="sm")
+                    refresh_stats_btn.click(
+                        fn=get_stats_info,
+                        outputs=stats_output
+                    )
             
+            # Right column - Output
             with gr.Column(scale=1):
-                gr.Markdown("## ⚡ Ultra Supreme Result")
+                gr.Markdown("## Results")
+                
+                # Score display
+                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>'
+                )
                 
+                # Optimized prompt
                 prompt_output = gr.Textbox(
-                    label="🚀 Ultra Supreme Optimized Flux Prompt",
-                    placeholder="Upload an image to witness absolute maximum intelligence analysis...",
-                    lines=12,
-                    max_lines=20,
+                    label="🎯 Optimized FLUX Prompt",
+                    placeholder="Upload an image to generate an optimized prompt...",
+                    lines=8,
+                    max_lines=15,
                     elem_classes=["prompt-output"],
                     show_copy_button=True
                 )
                 
-                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;">Ultra Supreme Score</div></div>'
-                )
-                
-                info_output = gr.Markdown(value="")
-                
-                clear_btn = gr.Button("🗑️ Clear Ultra Analysis", size="sm")
+                # Analysis report  
+                with gr.Accordion("📋 Detailed Analysis", open=True):
+                    info_output = gr.Markdown(value="")
         
         # Event handlers
         analyze_btn.click(
-            fn=process_ultra_supreme_analysis,
+            fn=process_image_interface,
             inputs=[image_input],
             outputs=[prompt_output, info_output, score_output]
         )
         
         clear_btn.click(
-            fn=clear_outputs,
+            fn=clear_interface,
             outputs=[prompt_output, info_output, score_output]
         )
         
+        # Footer
         gr.Markdown("""
         ---
-        ### 🏆 Ultra Supreme Research Foundation
-        
-        This system represents the **absolute pinnacle** of image analysis and Flux prompt optimization. Using triple CLIP interrogation, 
-        ultra-deep feature extraction, cultural context awareness, and emotional intelligence mapping, it achieves maximum possible 
-        understanding and applies research-validated Flux rules with supreme intelligence.
+        **Frame 0 Laboratory for MIA** • Advanced AI Research & Development
         
-        **🔬 Pariente AI Research Laboratory** • **�� Ultra Supreme Intelligence Engine**
+        This tool uses state-of-the-art vision-language models to analyze images and generate optimized prompts 
+        for FLUX image generation. The system applies proven optimization rules including camera configurations, 
+        lighting setups, and technical parameters for best results.
         """)
     
     return interface
 
 
-# Main execution
-if __name__ == "__main__":
+def main():
+    """Main application entry point"""
+    logger.info("Starting Frame 0 Laboratory for MIA")
+    
+    # Create and launch interface
     demo = create_interface()
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True,
-        show_error=True
-    )
+    
+    # Launch configuration
+    launch_config = {
+        "server_name": "0.0.0.0",
+        "server_port": 7860,
+        "show_error": True,
+        "show_tips": True,
+        "enable_queue": True
+    }
+    
+    # Add share option for Spaces
+    if ENVIRONMENT["is_spaces"]:
+        launch_config["share"] = False  # Spaces handles sharing
+    else:
+        launch_config["share"] = True  # Enable sharing for local
+    
+    logger.info(f"Launching on port {launch_config['server_port']}")
+    demo.launch(**launch_config)
+
+
+if __name__ == "__main__":
+    main()