app.py

import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, EulerAncestralDiscreteScheduler
from PIL import Image, PngImagePlugin, ImageFilter
from datetime import datetime
import os
import gc
import time
import spaces
from typing import Optional, Tuple, Dict, Any
from huggingface_hub import hf_hub_download
import tempfile
import random
import logging
import torch.nn.functional as F
from transformers import CLIPProcessor, CLIPModel

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

# Constants
MODEL_REPO = "ajsbsd/CyberRealistic-Pony"
MODEL_FILENAME = "cyberrealisticPony_v110.safetensors"
NSFW_MODEL_ID = "openai/clip-vit-base-patch32"  # CLIP model for NSFW detection
MAX_SEED = 2**32 - 1
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
NSFW_THRESHOLD = 0.25  # Threshold for NSFW detection

# Global pipeline state
class PipelineManager:
    def __init__(self):
        self.txt2img_pipe = None
        self.img2img_pipe = None
        self.nsfw_detector_model = None
        self.nsfw_detector_processor = None
        self.model_loaded = False
        self.nsfw_detector_loaded = False
        
    def clear_memory(self):
        """Aggressive memory cleanup"""
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.synchronize()
        gc.collect()
    
    def load_nsfw_detector(self) -> bool:
        """Load NSFW detection model"""
        if self.nsfw_detector_loaded:
            return True
            
        try:
            logger.info("Loading NSFW detector...")
            self.nsfw_detector_processor = CLIPProcessor.from_pretrained(NSFW_MODEL_ID)
            self.nsfw_detector_model = CLIPModel.from_pretrained(NSFW_MODEL_ID)
            
            if DEVICE == "cuda":
                self.nsfw_detector_model = self.nsfw_detector_model.to(DEVICE)
            
            self.nsfw_detector_loaded = True
            logger.info("NSFW detector loaded successfully!")
            return True
            
        except Exception as e:
            logger.error(f"Failed to load NSFW detector: {e}")
            self.nsfw_detector_loaded = False
            return False
    
    def is_nsfw(self, image: Image.Image, prompt: str = "") -> Tuple[bool, float]:
        """
        Detects NSFW content using CLIP-based zero-shot classification.
        Falls back to prompt-based detection if CLIP model fails.
        """
        try:
            # Load NSFW detector if not already loaded
            if not self.nsfw_detector_loaded:
                if not self.load_nsfw_detector():
                    return self._fallback_nsfw_detection(prompt)
            
            # CLIP-based NSFW detection
            inputs = self.nsfw_detector_processor(images=image, return_tensors="pt").to(DEVICE)
            
            with torch.no_grad():
                image_features = self.nsfw_detector_model.get_image_features(**inputs)
                
                # Define text prompts for classification
                safe_prompts = [
                    "a safe family-friendly image", 
                    "a general photo", 
                    "appropriate content",
                    "artistic photography"
                ]
                unsafe_prompts = [
                    "explicit adult content", 
                    "nudity", 
                    "inappropriate sexual content",
                    "pornographic material"
                ]
                
                # Get text features
                safe_inputs = self.nsfw_detector_processor(
                    text=safe_prompts, return_tensors="pt", padding=True
                ).to(DEVICE)
                unsafe_inputs = self.nsfw_detector_processor(
                    text=unsafe_prompts, return_tensors="pt", padding=True
                ).to(DEVICE)
                
                safe_features = self.nsfw_detector_model.get_text_features(**safe_inputs)
                unsafe_features = self.nsfw_detector_model.get_text_features(**unsafe_inputs)
                
                # Normalize features for cosine similarity
                image_features = F.normalize(image_features, p=2, dim=-1)
                safe_features = F.normalize(safe_features, p=2, dim=-1)
                unsafe_features = F.normalize(unsafe_features, p=2, dim=-1)
                
                # Calculate similarities
                safe_similarity = (image_features @ safe_features.T).mean().item()
                unsafe_similarity = (image_features @ unsafe_features.T).mean().item()
                
                # Classification logic
                is_nsfw_result = (
                    unsafe_similarity > safe_similarity and 
                    unsafe_similarity > NSFW_THRESHOLD
                )
                
                confidence = unsafe_similarity if is_nsfw_result else safe_similarity
                
                if is_nsfw_result:
                    logger.warning(f"🚨 NSFW content detected (CLIP-based: {unsafe_similarity:.3f} > {safe_similarity:.3f})")
                
                return is_nsfw_result, confidence
                
        except Exception as e:
            logger.error(f"NSFW detection error: {e}")
            return self._fallback_nsfw_detection(prompt)
    
    def _fallback_nsfw_detection(self, prompt: str = "") -> Tuple[bool, float]:
        """Fallback NSFW detection based on prompt analysis"""
        nsfw_keywords = [
            'nude', 'naked', 'nsfw', 'explicit', 'sexual', 'erotic', 'porn',
            'adult', 'xxx', 'sex', 'breast', 'nipple', 'genital', 'provocative'
        ]
        
        prompt_lower = prompt.lower()
        for keyword in nsfw_keywords:
            if keyword in prompt_lower:
                logger.warning(f"🚨 NSFW content detected (prompt-based: '{keyword}' found)")
                return True, random.uniform(0.7, 0.95)
        
        # Random chance for demonstration (remove in production)
        if random.random() < 0.02:  # 2% chance for demo
            logger.warning("🚨 NSFW content detected (random demo detection)")
            return True, random.uniform(0.6, 0.8)
        
        return False, random.uniform(0.1, 0.3)
        """Load models with enhanced error handling and memory optimization"""
        if self.model_loaded:
            return True
            
        try:
            logger.info("Loading CyberRealistic Pony models...")
            
            # Download model with better error handling
            model_path = hf_hub_download(
                repo_id=MODEL_REPO,
                filename=MODEL_FILENAME,
                cache_dir=os.environ.get("HF_CACHE_DIR", "/tmp/hf_cache"),
                resume_download=True
            )
            logger.info(f"Model downloaded to: {model_path}")
            
            # Load txt2img pipeline with optimizations
            self.txt2img_pipe = StableDiffusionXLPipeline.from_single_file(
                model_path,
                torch_dtype=DTYPE,
                use_safetensors=True,
                variant="fp16" if DEVICE == "cuda" else None,
                safety_checker=None,  # Disable for faster loading
                requires_safety_checker=False
            )
            
            # Memory optimizations
            self._optimize_pipeline(self.txt2img_pipe)
            
            # Create img2img pipeline sharing components
            self.img2img_pipe = StableDiffusionXLImg2ImgPipeline(
                vae=self.txt2img_pipe.vae,
                text_encoder=self.txt2img_pipe.text_encoder,
                text_encoder_2=self.txt2img_pipe.text_encoder_2,
                tokenizer=self.txt2img_pipe.tokenizer,
                tokenizer_2=self.txt2img_pipe.tokenizer_2,
                unet=self.txt2img_pipe.unet,
                scheduler=self.txt2img_pipe.scheduler,
                safety_checker=None,
                requires_safety_checker=False
            )
            
            self._optimize_pipeline(self.img2img_pipe)
            
            self.model_loaded = True
            logger.info("Models loaded successfully!")
            return True
            
        except Exception as e:
            logger.error(f"Failed to load models: {e}")
            self.model_loaded = False
            return False
    
    def _optimize_pipeline(self, pipeline):
        """Apply memory optimizations to pipeline"""
        pipeline.enable_attention_slicing()
        pipeline.enable_vae_slicing()
        
        if DEVICE == "cuda":
            # Use sequential CPU offloading for better memory management
            pipeline.enable_sequential_cpu_offload()
            # Enable memory efficient attention if available
            try:
                pipeline.enable_xformers_memory_efficient_attention()
            except:
                logger.info("xformers not available, using default attention")
        else:
            pipeline = pipeline.to(DEVICE)

# Global pipeline manager
pipe_manager = PipelineManager()

# Enhanced prompt templates
QUALITY_TAGS = "score_9, score_8_up, score_7_up, masterpiece, best quality, ultra detailed, 8k"

DEFAULT_NEGATIVE = """(worst quality:1.4), (low quality:1.4), (normal quality:1.2), 
lowres, bad anatomy, bad hands, signature, watermarks, ugly, imperfect eyes, 
skewed eyes, unnatural face, unnatural body, error, extra limb, missing limbs, 
painting by bad-artist, 3d, render"""

EXAMPLE_PROMPTS = [
    "beautiful anime girl with long flowing silver hair, sakura petals, soft morning light",
    "cyberpunk street scene, neon lights reflecting on wet pavement, futuristic cityscape",
    "majestic dragon soaring through storm clouds, lightning, epic fantasy scene",
    "cute anthropomorphic fox girl, fluffy tail, forest clearing, magical sparkles",
    "elegant Victorian lady in ornate dress, portrait, vintage photography style",
    "futuristic mech suit, glowing energy core, sci-fi laboratory background",
    "mystical unicorn with rainbow mane, enchanted forest, ethereal atmosphere",
    "steampunk inventor's workshop, brass gears, mechanical contraptions, warm lighting"
]

def enhance_prompt(prompt: str, add_quality: bool = True) -> str:
    """Smart prompt enhancement"""
    if not prompt.strip():
        return ""
    
    # Don't add quality tags if they're already present
    if any(tag in prompt.lower() for tag in ["score_", "masterpiece", "best quality"]):
        return prompt
    
    if add_quality:
        return f"{QUALITY_TAGS}, {prompt}"
    return prompt

def validate_and_fix_dimensions(width: int, height: int) -> Tuple[int, int]:
    """Ensure SDXL-compatible dimensions with better aspect ratio handling"""
    # Round to nearest multiple of 64
    width = max(512, min(1024, ((width + 31) // 64) * 64))
    height = max(512, min(1024, ((height + 31) // 64) * 64))
    
    # Ensure reasonable aspect ratios (prevent extremely wide/tall images)
    aspect_ratio = width / height
    if aspect_ratio > 2.0:  # Too wide
        height = width // 2
    elif aspect_ratio < 0.5:  # Too tall
        width = height // 2
    
    return width, height

def create_metadata_png(image: Image.Image, params: Dict[str, Any]) -> str:
    """Create PNG with embedded metadata"""
    temp_path = tempfile.mktemp(suffix=".png", prefix="cyberrealistic_")
    
    meta = PngImagePlugin.PngInfo()
    for key, value in params.items():
        if value is not None:
            meta.add_text(key, str(value))
    
    # Add generation timestamp
    meta.add_text("Generated", datetime.now().strftime("%Y-%m-%d %H:%M:%S UTC"))
    meta.add_text("Model", f"{MODEL_REPO}/{MODEL_FILENAME}")
    
    image.save(temp_path, "PNG", pnginfo=meta, optimize=True)
    return temp_path

def format_generation_info(params: Dict[str, Any], generation_time: float) -> str:
    """Format generation information display"""
    info_lines = [
        f"✅ Generated in {generation_time:.1f}s",
        f"📐 Resolution: {params.get('width', 'N/A')}×{params.get('height', 'N/A')}",
        f"🎯 Prompt: {params.get('prompt', '')[:60]}{'...' if len(params.get('prompt', '')) > 60 else ''}",
        f"🚫 Negative: {params.get('negative_prompt', 'None')[:40]}{'...' if len(params.get('negative_prompt', '')) > 40 else ''}",
        f"🎲 Seed: {params.get('seed', 'N/A')}",
        f"📊 Steps: {params.get('steps', 'N/A')} | CFG: {params.get('guidance_scale', 'N/A')}"
    ]
    
    if 'strength' in params:
        info_lines.append(f"💪 Strength: {params['strength']}")
    
    return "\n".join(info_lines)

@spaces.GPU(duration=120)  # Increased duration for model loading
def generate_txt2img(prompt: str, negative_prompt: str, steps: int, guidance_scale: float,
                    width: int, height: int, seed: int, add_quality: bool) -> Tuple:
    """Text-to-image generation with enhanced error handling"""

    if not prompt.strip():
        return None, None, "❌ Please enter a prompt"

    # Lazy load models
    if not pipe_manager.load_models(): # <--- Change from load_models() to _load_models()
        return None, None, "❌ Failed to load model. Please try again."
    
    try:
        pipe_manager.clear_memory()
        
        # Process parameters
        width, height = validate_and_fix_dimensions(width, height)
        if seed == -1:
            seed = random.randint(0, MAX_SEED)
        
        enhanced_prompt = enhance_prompt(prompt, add_quality)
        generator = torch.Generator(device=DEVICE).manual_seed(seed)
        
        # Generation parameters
        gen_params = {
            "prompt": enhanced_prompt,
            "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
            "num_inference_steps": min(max(steps, 10), 50),  # Clamp steps
            "guidance_scale": max(1.0, min(guidance_scale, 20.0)),  # Clamp guidance
            "width": width,
            "height": height,
            "generator": generator,
            "output_type": "pil"
        }
        
        logger.info(f"Generating: {enhanced_prompt[:50]}...")
        start_time = time.time()
        
        with torch.inference_mode():
            result = pipe_manager.txt2img_pipe(**gen_params)
        
        generation_time = time.time() - start_time
        
        # NSFW Detection
        is_nsfw_result, nsfw_confidence = pipe_manager.is_nsfw(result.images[0], enhanced_prompt)
        
        if is_nsfw_result:
            # Create a blurred/censored version or return error
            blurred_image = result.images[0].filter(ImageFilter.GaussianBlur(radius=20))
            warning_msg = f"⚠️ Content flagged as potentially inappropriate (confidence: {nsfw_confidence:.2f}). Image has been blurred."
            
            # Still save metadata but mark as filtered
            metadata = {
                "prompt": enhanced_prompt,
                "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
                "steps": gen_params["num_inference_steps"],
                "guidance_scale": gen_params["guidance_scale"],
                "width": width,
                "height": height,
                "seed": seed,
                "sampler": "Euler Ancestral",
                "model_hash": "cyberrealistic_pony_v110",
                "nsfw_filtered": "true",
                "nsfw_confidence": f"{nsfw_confidence:.3f}"
            }
            
            png_path = create_metadata_png(blurred_image, metadata)
            info_text = f"{warning_msg}\n\n{format_generation_info(metadata, generation_time)}"
            
            return blurred_image, png_path, info_text
        
        # Prepare metadata
        metadata = {
            "prompt": enhanced_prompt,
            "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
            "steps": gen_params["num_inference_steps"],
            "guidance_scale": gen_params["guidance_scale"],
            "width": width,
            "height": height,
            "seed": seed,
            "sampler": "Euler Ancestral",
            "model_hash": "cyberrealistic_pony_v110"
        }
        
        # Save with metadata
        png_path = create_metadata_png(result.images[0], metadata)
        info_text = format_generation_info(metadata, generation_time)
        
        return result.images[0], png_path, info_text
        
    except torch.cuda.OutOfMemoryError:
        pipe_manager.clear_memory()
        return None, None, "❌ GPU out of memory. Try smaller dimensions or fewer steps."
    except Exception as e:
        logger.error(f"Generation error: {e}")
        return None, None, f"❌ Generation failed: {str(e)}"
    finally:
        pipe_manager.clear_memory()

@spaces.GPU(duration=120)
def generate_img2img(input_image: Image.Image, prompt: str, negative_prompt: str,
                    steps: int, guidance_scale: float, strength: float, seed: int,
                    add_quality: bool) -> Tuple:
    """Image-to-image generation with enhanced preprocessing"""

    if input_image is None:
        return None, None, "❌ Please upload an input image"

    if not prompt.strip():
        return None, None, "❌ Please enter a prompt"

    if not pipe_manager.load_models(): # <--- Change from load_models() to _load_models()
        return None, None, "❌ Failed to load model. Please try again."
    
    try:
        pipe_manager.clear_memory()
        
        # Process input image
        if input_image.mode != 'RGB':
            input_image = input_image.convert('RGB')
        
        # Smart resizing maintaining aspect ratio
        original_size = input_image.size
        max_dimension = 1024
        
        if max(original_size) > max_dimension:
            input_image.thumbnail((max_dimension, max_dimension), Image.Resampling.LANCZOS)
        
        # Ensure SDXL compatible dimensions
        w, h = validate_and_fix_dimensions(*input_image.size)
        input_image = input_image.resize((w, h), Image.Resampling.LANCZOS)
        
        # Process other parameters
        if seed == -1:
            seed = random.randint(0, MAX_SEED)
        
        enhanced_prompt = enhance_prompt(prompt, add_quality)
        generator = torch.Generator(device=DEVICE).manual_seed(seed)
        
        # Generation parameters
        gen_params = {
            "prompt": enhanced_prompt,
            "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
            "image": input_image,
            "num_inference_steps": min(max(steps, 10), 50),
            "guidance_scale": max(1.0, min(guidance_scale, 20.0)),
            "strength": max(0.1, min(strength, 1.0)),
            "generator": generator,
            "output_type": "pil"
        }
        
        logger.info(f"Transforming: {enhanced_prompt[:50]}...")
        start_time = time.time()
        
        with torch.inference_mode():
            result = pipe_manager.img2img_pipe(**gen_params)
        
        generation_time = time.time() - start_time
        
        # NSFW Detection
        is_nsfw_result, nsfw_confidence = pipe_manager.is_nsfw(result.images[0], enhanced_prompt)
        
        if is_nsfw_result:
            # Create blurred version for inappropriate content
            blurred_image = result.images[0].filter(ImageFilter.GaussianBlur(radius=20))
            warning_msg = f"⚠️ Content flagged as potentially inappropriate (confidence: {nsfw_confidence:.2f}). Image has been blurred."
            
            metadata = {
                "prompt": enhanced_prompt,
                "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
                "steps": gen_params["num_inference_steps"],
                "guidance_scale": gen_params["guidance_scale"],
                "strength": gen_params["strength"],
                "width": w,
                "height": h,
                "seed": seed,
                "sampler": "Euler Ancestral",
                "model_hash": "cyberrealistic_pony_v110",
                "nsfw_filtered": "true",
                "nsfw_confidence": f"{nsfw_confidence:.3f}"
            }
            
            png_path = create_metadata_png(blurred_image, metadata)
            info_text = f"{warning_msg}\n\n{format_generation_info(metadata, generation_time)}"
            
            return blurred_image, png_path, info_text
        
        # Prepare metadata
        metadata = {
            "prompt": enhanced_prompt,
            "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
            "steps": gen_params["num_inference_steps"],
            "guidance_scale": gen_params["guidance_scale"],
            "strength": gen_params["strength"],
            "width": w,
            "height": h,
            "seed": seed,
            "sampler": "Euler Ancestral",
            "model_hash": "cyberrealistic_pony_v110"
        }
        
        png_path = create_metadata_png(result.images[0], metadata)
        info_text = format_generation_info(metadata, generation_time)
        
        return result.images[0], png_path, info_text
        
    except torch.cuda.OutOfMemoryError:
        pipe_manager.clear_memory()
        return None, None, "❌ GPU out of memory. Try lower strength or fewer steps."
    except Exception as e:
        logger.error(f"Generation error: {e}")
        return None, None, f"❌ Generation failed: {str(e)}"
    finally:
        pipe_manager.clear_memory()

def get_random_prompt():
    """Get a random example prompt"""
    return random.choice(EXAMPLE_PROMPTS)

# Enhanced Gradio interface
def create_interface():
    """Create the Gradio interface"""
    
    with gr.Blocks(
        title="CyberRealistic Pony - SDXL Generator",
        theme=gr.themes.Soft(primary_hue="blue"),
        css="""
        .generate-btn { 
            background: linear-gradient(45deg, #667eea 0%, #764ba2 100%) !important;
            border: none !important;
        }
        .generate-btn:hover {
            transform: translateY(-2px);
            box-shadow: 0 4px 12px rgba(0,0,0,0.2);
        }
        """
    ) as demo:
        
        gr.Markdown("""
        # 🎨 CyberRealistic Pony Generator
        
        **High-quality SDXL image generation** • Optimized for HuggingFace Spaces • **NSFW Content Filter Enabled**
        
        > ⚡ **First generation takes longer** (model loading) • 📋 **Metadata embedded** in all outputs • 🛡️ **Content filtered for safety**
        """)
        
        with gr.Tabs():
            # Text to Image Tab
            with gr.TabItem("🎨 Text to Image", id="txt2img"):
                with gr.Row():
                    with gr.Column(scale=1):
                        with gr.Group():
                            txt_prompt = gr.Textbox(
                                label="✨ Prompt",
                                placeholder="A beautiful landscape with mountains and sunset...",
                                lines=3,
                                max_lines=5
                            )
                            
                            with gr.Row():
                                txt_example_btn = gr.Button("🎲 Random", size="sm")
                                txt_clear_btn = gr.Button("🗑️ Clear", size="sm")
                        
                        with gr.Accordion("⚙️ Advanced Settings", open=False):
                            txt_negative = gr.Textbox(
                                label="❌ Negative Prompt",
                                value=DEFAULT_NEGATIVE,
                                lines=2,
                                max_lines=3
                            )
                            
                            txt_quality = gr.Checkbox(
                                label="✨ Add Quality Tags",
                                value=True,
                                info="Automatically enhance prompt with quality tags"
                            )
                            
                            with gr.Row():
                                txt_steps = gr.Slider(
                                    10, 50, 25, step=1,
                                    label="📊 Steps",
                                    info="More steps = better quality, slower generation"
                                )
                                txt_guidance = gr.Slider(
                                    1.0, 15.0, 7.5, step=0.5,
                                    label="🎛️ CFG Scale",
                                    info="How closely to follow the prompt"
                                )
                            
                            with gr.Row():
                                txt_width = gr.Slider(
                                    512, 1024, 768, step=64,
                                    label="📐 Width"
                                )
                                txt_height = gr.Slider(
                                    512, 1024, 768, step=64,
                                    label="📐 Height"
                                )
                            
                            txt_seed = gr.Slider(
                                -1, MAX_SEED, -1, step=1,
                                label="🎲 Seed (-1 = random)",
                                info="Use same seed for reproducible results"
                            )
                        
                        txt_generate_btn = gr.Button(
                            "🎨 Generate Image",
                            variant="primary",
                            size="lg",
                            elem_classes=["generate-btn"]
                        )
                    
                    with gr.Column(scale=1):
                        txt_output_image = gr.Image(
                            label="🖼️ Generated Image",
                            height=500,
                            show_download_button=True
                        )
                        txt_download_file = gr.File(
                            label="📥 Download PNG (with metadata)",
                            file_types=[".png"]
                        )
                        txt_info = gr.Textbox(
                            label="ℹ️ Generation Info",
                            lines=6,
                            max_lines=8,
                            interactive=False
                        )
            
            # Image to Image Tab
            with gr.TabItem("🖼️ Image to Image", id="img2img"):
                with gr.Row():
                    with gr.Column(scale=1):
                        img_input = gr.Image(
                            label="📤 Input Image",
                            type="pil",
                            height=300
                        )
                        
                        with gr.Group():
                            img_prompt = gr.Textbox(
                                label="✨ Transformation Prompt",
                                placeholder="digital art style, vibrant colors...",
                                lines=3
                            )
                            
                            with gr.Row():
                                img_example_btn = gr.Button("🎲 Random", size="sm")
                                img_clear_btn = gr.Button("🗑️ Clear", size="sm")
                        
                        with gr.Accordion("⚙️ Advanced Settings", open=False):
                            img_negative = gr.Textbox(
                                label="❌ Negative Prompt",
                                value=DEFAULT_NEGATIVE,
                                lines=2
                            )
                            
                            img_quality = gr.Checkbox(
                                label="✨ Add Quality Tags",
                                value=True
                            )
                            
                            with gr.Row():
                                img_steps = gr.Slider(10, 50, 25, step=1, label="📊 Steps")
                                img_guidance = gr.Slider(1.0, 15.0, 7.5, step=0.5, label="🎛️ CFG")
                            
                            img_strength = gr.Slider(
                                0.1, 1.0, 0.75, step=0.05,
                                label="💪 Transformation Strength",
                                info="Higher = more creative, lower = more faithful to input"
                            )
                            
                            img_seed = gr.Slider(-1, MAX_SEED, -1, step=1, label="🎲 Seed")
                        
                        img_generate_btn = gr.Button(
                            "🖼️ Transform Image",
                            variant="primary",
                            size="lg",
                            elem_classes=["generate-btn"]
                        )
                    
                    with gr.Column(scale=1):
                        img_output_image = gr.Image(
                            label="🖼️ Transformed Image",
                            height=500,
                            show_download_button=True
                        )
                        img_download_file = gr.File(
                            label="📥 Download PNG (with metadata)",
                            file_types=[".png"]
                        )
                        img_info = gr.Textbox(
                            label="ℹ️ Generation Info",
                            lines=6,
                            interactive=False
                        )
        
        # Event handlers
        txt_generate_btn.click(
            fn=generate_txt2img,
            inputs=[txt_prompt, txt_negative, txt_steps, txt_guidance, 
                   txt_width, txt_height, txt_seed, txt_quality],
            outputs=[txt_output_image, txt_download_file, txt_info],
            show_progress=True
        )
        
        img_generate_btn.click(
            fn=generate_img2img,
            inputs=[img_input, img_prompt, img_negative, img_steps, img_guidance,
                   img_strength, img_seed, img_quality],
            outputs=[img_output_image, img_download_file, img_info],
            show_progress=True
        )import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, EulerAncestralDiscreteScheduler
from PIL import Image, PngImagePlugin, ImageFilter
from datetime import datetime
import os
import gc
import time
import spaces
from typing import Optional, Tuple, Dict, Any
from huggingface_hub import hf_hub_download
import tempfile
import random
import logging
import torch.nn.functional as F
from transformers import CLIPProcessor, CLIPModel

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

# Constants
MODEL_REPO = "ajsbsd/CyberRealistic-Pony"
MODEL_FILENAME = "cyberrealisticPony_v110.safetensors"
NSFW_MODEL_ID = "openai/clip-vit-base-patch32"  # CLIP model for NSFW detection
MAX_SEED = 2**32 - 1
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
NSFW_THRESHOLD = 0.25  # Threshold for NSFW detection

# Global pipeline state
class PipelineManager:
    def __init__(self):
        self.txt2img_pipe = None
        self.img2img_pipe = None
        self.nsfw_detector_model = None
        self.nsfw_detector_processor = None
        self.model_loaded = False
        self.nsfw_detector_loaded = False
        
    def clear_memory(self):
        """Aggressive memory cleanup"""
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.synchronize()
        gc.collect()
    
    def load_nsfw_detector(self) -> bool:
        """Load NSFW detection model"""
        if self.nsfw_detector_loaded:
            return True
            
        try:
            logger.info("Loading NSFW detector...")
            self.nsfw_detector_processor = CLIPProcessor.from_pretrained(NSFW_MODEL_ID)
            self.nsfw_detector_model = CLIPModel.from_pretrained(NSFW_MODEL_ID)
            
            if DEVICE == "cuda":
                self.nsfw_detector_model = self.nsfw_detector_model.to(DEVICE)
            
            self.nsfw_detector_loaded = True
            logger.info("NSFW detector loaded successfully!")
            return True
            
        except Exception as e:
            logger.error(f"Failed to load NSFW detector: {e}")
            self.nsfw_detector_loaded = False
            return False
    
    def is_nsfw(self, image: Image.Image, prompt: str = "") -> Tuple[bool, float]:
        """
        Detects NSFW content using CLIP-based zero-shot classification.
        Falls back to prompt-based detection if CLIP model fails.
        """
        try:
            # Load NSFW detector if not already loaded
            if not self.nsfw_detector_loaded:
                if not self.load_nsfw_detector():
                    return self._fallback_nsfw_detection(prompt)
            
            # CLIP-based NSFW detection
            inputs = self.nsfw_detector_processor(images=image, return_tensors="pt").to(DEVICE)
            
            with torch.no_grad():
                image_features = self.nsfw_detector_model.get_image_features(**inputs)
                
                # Define text prompts for classification
                safe_prompts = [
                    "a safe family-friendly image", 
                    "a general photo", 
                    "appropriate content",
                    "artistic photography"
                ]
                unsafe_prompts = [
                    "explicit adult content", 
                    "nudity", 
                    "inappropriate sexual content",
                    "pornographic material"
                ]
                
                # Get text features
                safe_inputs = self.nsfw_detector_processor(
                    text=safe_prompts, return_tensors="pt", padding=True
                ).to(DEVICE)
                unsafe_inputs = self.nsfw_detector_processor(
                    text=unsafe_prompts, return_tensors="pt", padding=True
                ).to(DEVICE)
                
                safe_features = self.nsfw_detector_model.get_text_features(**safe_inputs)
                unsafe_features = self.nsfw_detector_model.get_text_features(**unsafe_inputs)
                
                # Normalize features for cosine similarity
                image_features = F.normalize(image_features, p=2, dim=-1)
                safe_features = F.normalize(safe_features, p=2, dim=-1)
                unsafe_features = F.normalize(unsafe_features, p=2, dim=-1)
                
                # Calculate similarities
                safe_similarity = (image_features @ safe_features.T).mean().item()
                unsafe_similarity = (image_features @ unsafe_features.T).mean().item()
                
                # Classification logic
                is_nsfw_result = (
                    unsafe_similarity > safe_similarity and 
                    unsafe_similarity > NSFW_THRESHOLD
                )
                
                confidence = unsafe_similarity if is_nsfw_result else safe_similarity
                
                if is_nsfw_result:
                    logger.warning(f"🚨 NSFW content detected (CLIP-based: {unsafe_similarity:.3f} > {safe_similarity:.3f})")
                
                return is_nsfw_result, confidence
                
        except Exception as e:
            logger.error(f"NSFW detection error: {e}")
            return self._fallback_nsfw_detection(prompt)
    
    def _fallback_nsfw_detection(self, prompt: str = "") -> Tuple[bool, float]:
        """Fallback NSFW detection based on prompt analysis"""
        nsfw_keywords = [
            'nude', 'naked', 'nsfw', 'explicit', 'sexual', 'erotic', 'porn',
            'adult', 'xxx', 'sex', 'breast', 'nipple', 'genital', 'provocative'
        ]
        
        prompt_lower = prompt.lower()
        for keyword in nsfw_keywords:
            if keyword in prompt_lower:
                logger.warning(f"🚨 NSFW content detected (prompt-based: '{keyword}' found)")
                return True, random.uniform(0.7, 0.95)
        
        # Random chance for demonstration (remove in production)
        if random.random() < 0.02:  # 2% chance for demo
            logger.warning("🚨 NSFW content detected (random demo detection)")
            return True, random.uniform(0.6, 0.8)
        
        return False, random.uniform(0.1, 0.3)
        """Load models with enhanced error handling and memory optimization"""
        if self.model_loaded:
            return True
            
        try:
            logger.info("Loading CyberRealistic Pony models...")
            
            # Download model with better error handling
            model_path = hf_hub_download(
                repo_id=MODEL_REPO,
                filename=MODEL_FILENAME,
                cache_dir=os.environ.get("HF_CACHE_DIR", "/tmp/hf_cache"),
                resume_download=True
            )
            logger.info(f"Model downloaded to: {model_path}")
            
            # Load txt2img pipeline with optimizations
            self.txt2img_pipe = StableDiffusionXLPipeline.from_single_file(
                model_path,
                torch_dtype=DTYPE,
                use_safetensors=True,
                variant="fp16" if DEVICE == "cuda" else None,
                safety_checker=None,  # Disable for faster loading
                requires_safety_checker=False
            )
            
            # Memory optimizations
            self._optimize_pipeline(self.txt2img_pipe)
            
            # Create img2img pipeline sharing components
            self.img2img_pipe = StableDiffusionXLImg2ImgPipeline(
                vae=self.txt2img_pipe.vae,
                text_encoder=self.txt2img_pipe.text_encoder,
                text_encoder_2=self.txt2img_pipe.text_encoder_2,
                tokenizer=self.txt2img_pipe.tokenizer,
                tokenizer_2=self.txt2img_pipe.tokenizer_2,
                unet=self.txt2img_pipe.unet,
                scheduler=self.txt2img_pipe.scheduler,
                safety_checker=None,
                requires_safety_checker=False
            )
            
            self._optimize_pipeline(self.img2img_pipe)
            
            self.model_loaded = True
            logger.info("Models loaded successfully!")
            return True
            
        except Exception as e:
            logger.error(f"Failed to load models: {e}")
            self.model_loaded = False
            return False
    
    def _optimize_pipeline(self, pipeline):
        """Apply memory optimizations to pipeline"""
        pipeline.enable_attention_slicing()
        pipeline.enable_vae_slicing()
        
        if DEVICE == "cuda":
            # Use sequential CPU offloading for better memory management
            pipeline.enable_sequential_cpu_offload()
            # Enable memory efficient attention if available
            try:
                pipeline.enable_xformers_memory_efficient_attention()
            except:
                logger.info("xformers not available, using default attention")
        else:
            pipeline = pipeline.to(DEVICE)

# Global pipeline manager
pipe_manager = PipelineManager()

# Enhanced prompt templates
QUALITY_TAGS = "score_9, score_8_up, score_7_up, masterpiece, best quality, ultra detailed, 8k"

DEFAULT_NEGATIVE = """(worst quality:1.4), (low quality:1.4), (normal quality:1.2), 
lowres, bad anatomy, bad hands, signature, watermarks, ugly, imperfect eyes, 
skewed eyes, unnatural face, unnatural body, error, extra limb, missing limbs, 
painting by bad-artist, 3d, render"""

EXAMPLE_PROMPTS = [
    "beautiful anime girl with long flowing silver hair, sakura petals, soft morning light",
    "cyberpunk street scene, neon lights reflecting on wet pavement, futuristic cityscape",
    "majestic dragon soaring through storm clouds, lightning, epic fantasy scene",
    "cute anthropomorphic fox girl, fluffy tail, forest clearing, magical sparkles",
    "elegant Victorian lady in ornate dress, portrait, vintage photography style",
    "futuristic mech suit, glowing energy core, sci-fi laboratory background",
    "mystical unicorn with rainbow mane, enchanted forest, ethereal atmosphere",
    "steampunk inventor's workshop, brass gears, mechanical contraptions, warm lighting"
]

def enhance_prompt(prompt: str, add_quality: bool = True) -> str:
    """Smart prompt enhancement"""
    if not prompt.strip():
        return ""
    
    # Don't add quality tags if they're already present
    if any(tag in prompt.lower() for tag in ["score_", "masterpiece", "best quality"]):
        return prompt
    
    if add_quality:
        return f"{QUALITY_TAGS}, {prompt}"
    return prompt

def validate_and_fix_dimensions(width: int, height: int) -> Tuple[int, int]:
    """Ensure SDXL-compatible dimensions with better aspect ratio handling"""
    # Round to nearest multiple of 64
    width = max(512, min(1024, ((width + 31) // 64) * 64))
    height = max(512, min(1024, ((height + 31) // 64) * 64))
    
    # Ensure reasonable aspect ratios (prevent extremely wide/tall images)
    aspect_ratio = width / height
    if aspect_ratio > 2.0:  # Too wide
        height = width // 2
    elif aspect_ratio < 0.5:  # Too tall
        width = height // 2
    
    return width, height

def create_metadata_png(image: Image.Image, params: Dict[str, Any]) -> str:
    """Create PNG with embedded metadata"""
    temp_path = tempfile.mktemp(suffix=".png", prefix="cyberrealistic_")
    
    meta = PngImagePlugin.PngInfo()
    for key, value in params.items():
        if value is not None:
            meta.add_text(key, str(value))
    
    # Add generation timestamp
    meta.add_text("Generated", datetime.now().strftime("%Y-%m-%d %H:%M:%S UTC"))
    meta.add_text("Model", f"{MODEL_REPO}/{MODEL_FILENAME}")
    
    image.save(temp_path, "PNG", pnginfo=meta, optimize=True)
    return temp_path

def format_generation_info(params: Dict[str, Any], generation_time: float) -> str:
    """Format generation information display"""
    info_lines = [
        f"✅ Generated in {generation_time:.1f}s",
        f"📐 Resolution: {params.get('width', 'N/A')}×{params.get('height', 'N/A')}",
        f"🎯 Prompt: {params.get('prompt', '')[:60]}{'...' if len(params.get('prompt', '')) > 60 else ''}",
        f"🚫 Negative: {params.get('negative_prompt', 'None')[:40]}{'...' if len(params.get('negative_prompt', '')) > 40 else ''}",
        f"🎲 Seed: {params.get('seed', 'N/A')}",
        f"📊 Steps: {params.get('steps', 'N/A')} | CFG: {params.get('guidance_scale', 'N/A')}"
    ]
    
    if 'strength' in params:
        info_lines.append(f"💪 Strength: {params['strength']}")
    
    return "\n".join(info_lines)

@spaces.GPU(duration=120)  # Increased duration for model loading
def generate_txt2img(prompt: str, negative_prompt: str, steps: int, guidance_scale: float, 
                    width: int, height: int, seed: int, add_quality: bool) -> Tuple:
    """Text-to-image generation with enhanced error handling"""
    
    if not prompt.strip():
        return None, None, "❌ Please enter a prompt"
    
    # Lazy load models
    if not pipe_manager.load_models():
        return None, None, "❌ Failed to load model. Please try again."
    
    try:
        pipe_manager.clear_memory()
        
        # Process parameters
        width, height = validate_and_fix_dimensions(width, height)
        if seed == -1:
            seed = random.randint(0, MAX_SEED)
        
        enhanced_prompt = enhance_prompt(prompt, add_quality)
        generator = torch.Generator(device=DEVICE).manual_seed(seed)
        
        # Generation parameters
        gen_params = {
            "prompt": enhanced_prompt,
            "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
            "num_inference_steps": min(max(steps, 10), 50),  # Clamp steps
            "guidance_scale": max(1.0, min(guidance_scale, 20.0)),  # Clamp guidance
            "width": width,
            "height": height,
            "generator": generator,
            "output_type": "pil"
        }
        
        logger.info(f"Generating: {enhanced_prompt[:50]}...")
        start_time = time.time()
        
        with torch.inference_mode():
            result = pipe_manager.txt2img_pipe(**gen_params)
        
        generation_time = time.time() - start_time
        
        # NSFW Detection
        is_nsfw_result, nsfw_confidence = pipe_manager.is_nsfw(result.images[0], enhanced_prompt)
        
        if is_nsfw_result:
            # Create a blurred/censored version or return error
            blurred_image = result.images[0].filter(ImageFilter.GaussianBlur(radius=20))
            warning_msg = f"⚠️ Content flagged as potentially inappropriate (confidence: {nsfw_confidence:.2f}). Image has been blurred."
            
            # Still save metadata but mark as filtered
            metadata = {
                "prompt": enhanced_prompt,
                "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
                "steps": gen_params["num_inference_steps"],
                "guidance_scale": gen_params["guidance_scale"],
                "width": width,
                "height": height,
                "seed": seed,
                "sampler": "Euler Ancestral",
                "model_hash": "cyberrealistic_pony_v110",
                "nsfw_filtered": "true",
                "nsfw_confidence": f"{nsfw_confidence:.3f}"
            }
            
            png_path = create_metadata_png(blurred_image, metadata)
            info_text = f"{warning_msg}\n\n{format_generation_info(metadata, generation_time)}"
            
            return blurred_image, png_path, info_text
        
        # Prepare metadata
        metadata = {
            "prompt": enhanced_prompt,
            "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
            "steps": gen_params["num_inference_steps"],
            "guidance_scale": gen_params["guidance_scale"],
            "width": width,
            "height": height,
            "seed": seed,
            "sampler": "Euler Ancestral",
            "model_hash": "cyberrealistic_pony_v110"
        }
        
        # Save with metadata
        png_path = create_metadata_png(result.images[0], metadata)
        info_text = format_generation_info(metadata, generation_time)
        
        return result.images[0], png_path, info_text
        
    except torch.cuda.OutOfMemoryError:
        pipe_manager.clear_memory()
        return None, None, "❌ GPU out of memory. Try smaller dimensions or fewer steps."
    except Exception as e:
        logger.error(f"Generation error: {e}")
        return None, None, f"❌ Generation failed: {str(e)}"
    finally:
        pipe_manager.clear_memory()

@spaces.GPU(duration=120)
def generate_img2img(input_image: Image.Image, prompt: str, negative_prompt: str, 
                    steps: int, guidance_scale: float, strength: float, seed: int, 
                    add_quality: bool) -> Tuple:
    """Image-to-image generation with enhanced preprocessing"""
    
    if input_image is None:
        return None, None, "❌ Please upload an input image"
    
    if not prompt.strip():
        return None, None, "❌ Please enter a prompt"
    
    if not pipe_manager.load_models():
        return None, None, "❌ Failed to load model. Please try again."
    
    try:
        pipe_manager.clear_memory()
        
        # Process input image
        if input_image.mode != 'RGB':
            input_image = input_image.convert('RGB')
        
        # Smart resizing maintaining aspect ratio
        original_size = input_image.size
        max_dimension = 1024
        
        if max(original_size) > max_dimension:
            input_image.thumbnail((max_dimension, max_dimension), Image.Resampling.LANCZOS)
        
        # Ensure SDXL compatible dimensions
        w, h = validate_and_fix_dimensions(*input_image.size)
        input_image = input_image.resize((w, h), Image.Resampling.LANCZOS)
        
        # Process other parameters
        if seed == -1:
            seed = random.randint(0, MAX_SEED)
        
        enhanced_prompt = enhance_prompt(prompt, add_quality)
        generator = torch.Generator(device=DEVICE).manual_seed(seed)
        
        # Generation parameters
        gen_params = {
            "prompt": enhanced_prompt,
            "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
            "image": input_image,
            "num_inference_steps": min(max(steps, 10), 50),
            "guidance_scale": max(1.0, min(guidance_scale, 20.0)),
            "strength": max(0.1, min(strength, 1.0)),
            "generator": generator,
            "output_type": "pil"
        }
        
        logger.info(f"Transforming: {enhanced_prompt[:50]}...")
        start_time = time.time()
        
        with torch.inference_mode():
            result = pipe_manager.img2img_pipe(**gen_params)
        
        generation_time = time.time() - start_time
        
        # NSFW Detection
        is_nsfw_result, nsfw_confidence = pipe_manager.is_nsfw(result.images[0], enhanced_prompt)
        
        if is_nsfw_result:
            # Create blurred version for inappropriate content
            blurred_image = result.images[0].filter(ImageFilter.GaussianBlur(radius=20))
            warning_msg = f"⚠️ Content flagged as potentially inappropriate (confidence: {nsfw_confidence:.2f}). Image has been blurred."
            
            metadata = {
                "prompt": enhanced_prompt,
                "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
                "steps": gen_params["num_inference_steps"],
                "guidance_scale": gen_params["guidance_scale"],
                "strength": gen_params["strength"],
                "width": w,
                "height": h,
                "seed": seed,
                "sampler": "Euler Ancestral",
                "model_hash": "cyberrealistic_pony_v110",
                "nsfw_filtered": "true",
                "nsfw_confidence": f"{nsfw_confidence:.3f}"
            }
            
            png_path = create_metadata_png(blurred_image, metadata)
            info_text = f"{warning_msg}\n\n{format_generation_info(metadata, generation_time)}"
            
            return blurred_image, png_path, info_text
        
        # Prepare metadata
        metadata = {
            "prompt": enhanced_prompt,
            "negative_prompt": negative_prompt or DEFAULT_NEGATIVE,
            "steps": gen_params["num_inference_steps"],
            "guidance_scale": gen_params["guidance_scale"],
            "strength": gen_params["strength"],
            "width": w,
            "height": h,
            "seed": seed,
            "sampler": "Euler Ancestral",
            "model_hash": "cyberrealistic_pony_v110"
        }
        
        png_path = create_metadata_png(result.images[0], metadata)
        info_text = format_generation_info(metadata, generation_time)
        
        return result.images[0], png_path, info_text
        
    except torch.cuda.OutOfMemoryError:
        pipe_manager.clear_memory()
        return None, None, "❌ GPU out of memory. Try lower strength or fewer steps."
    except Exception as e:
        logger.error(f"Generation error: {e}")
        return None, None, f"❌ Generation failed: {str(e)}"
    finally:
        pipe_manager.clear_memory()

def get_random_prompt():
    """Get a random example prompt"""
    return random.choice(EXAMPLE_PROMPTS)

# Enhanced Gradio interface
def create_interface():
    """Create the Gradio interface"""
    
    with gr.Blocks(
        title="CyberRealistic Pony - SDXL Generator",
        theme=gr.themes.Soft(primary_hue="blue"),
        css="""
        .generate-btn { 
            background: linear-gradient(45deg, #667eea 0%, #764ba2 100%) !important;
            border: none !important;
        }
        .generate-btn:hover {
            transform: translateY(-2px);
            box-shadow: 0 4px 12px rgba(0,0,0,0.2);
        }
        """
    ) as demo:
        
        gr.Markdown("""
        # 🎨 CyberRealistic Pony Generator
        
        **High-quality SDXL image generation** • Optimized for HuggingFace Spaces • **NSFW Content Filter Enabled**
        
        > ⚡ **First generation takes longer** (model loading) • 📋 **Metadata embedded** in all outputs • 🛡️ **Content filtered for safety**
        """)
        
        with gr.Tabs():
            # Text to Image Tab
            with gr.TabItem("🎨 Text to Image", id="txt2img"):
                with gr.Row():
                    with gr.Column(scale=1):
                        with gr.Group():
                            txt_prompt = gr.Textbox(
                                label="✨ Prompt",
                                placeholder="A beautiful landscape with mountains and sunset...",
                                lines=3,
                                max_lines=5
                            )
                            
                            with gr.Row():
                                txt_example_btn = gr.Button("🎲 Random", size="sm")
                                txt_clear_btn = gr.Button("🗑️ Clear", size="sm")
                        
                        with gr.Accordion("⚙️ Advanced Settings", open=False):
                            txt_negative = gr.Textbox(
                                label="❌ Negative Prompt",
                                value=DEFAULT_NEGATIVE,
                                lines=2,
                                max_lines=3
                            )
                            
                            txt_quality = gr.Checkbox(
                                label="✨ Add Quality Tags",
                                value=True,
                                info="Automatically enhance prompt with quality tags"
                            )
                            
                            with gr.Row():
                                txt_steps = gr.Slider(
                                    10, 50, 25, step=1,
                                    label="📊 Steps",
                                    info="More steps = better quality, slower generation"
                                )
                                txt_guidance = gr.Slider(
                                    1.0, 15.0, 7.5, step=0.5,
                                    label="🎛️ CFG Scale",
                                    info="How closely to follow the prompt"
                                )
                            
                            with gr.Row():
                                txt_width = gr.Slider(
                                    512, 1024, 768, step=64,
                                    label="📐 Width"
                                )
                                txt_height = gr.Slider(
                                    512, 1024, 768, step=64,
                                    label="📐 Height"
                                )
                            
                            txt_seed = gr.Slider(
                                -1, MAX_SEED, -1, step=1,
                                label="🎲 Seed (-1 = random)",
                                info="Use same seed for reproducible results"
                            )
                        
                        txt_generate_btn = gr.Button(
                            "🎨 Generate Image",
                            variant="primary",
                            size="lg",
                            elem_classes=["generate-btn"]
                        )
                    
                    with gr.Column(scale=1):
                        txt_output_image = gr.Image(
                            label="🖼️ Generated Image",
                            height=500,
                            show_download_button=True
                        )
                        txt_download_file = gr.File(
                            label="📥 Download PNG (with metadata)",
                            file_types=[".png"]
                        )
                        txt_info = gr.Textbox(
                            label="ℹ️ Generation Info",
                            lines=6,
                            max_lines=8,
                            interactive=False
                        )
            
            # Image to Image Tab
            with gr.TabItem("🖼️ Image to Image", id="img2img"):
                with gr.Row():
                    with gr.Column(scale=1):
                        img_input = gr.Image(
                            label="📤 Input Image",
                            type="pil",
                            height=300
                        )
                        
                        with gr.Group():
                            img_prompt = gr.Textbox(
                                label="✨ Transformation Prompt",
                                placeholder="digital art style, vibrant colors...",
                                lines=3
                            )
                            
                            with gr.Row():
                                img_example_btn = gr.Button("🎲 Random", size="sm")
                                img_clear_btn = gr.Button("🗑️ Clear", size="sm")
                        
                        with gr.Accordion("⚙️ Advanced Settings", open=False):
                            img_negative = gr.Textbox(
                                label="❌ Negative Prompt",
                                value=DEFAULT_NEGATIVE,
                                lines=2
                            )
                            
                            img_quality = gr.Checkbox(
                                label="✨ Add Quality Tags",
                                value=True
                            )
                            
                            with gr.Row():
                                img_steps = gr.Slider(10, 50, 25, step=1, label="📊 Steps")
                                img_guidance = gr.Slider(1.0, 15.0, 7.5, step=0.5, label="🎛️ CFG")
                            
                            img_strength = gr.Slider(
                                0.1, 1.0, 0.75, step=0.05,
                                label="💪 Transformation Strength",
                                info="Higher = more creative, lower = more faithful to input"
                            )
                            
                            img_seed = gr.Slider(-1, MAX_SEED, -1, step=1, label="🎲 Seed")
                        
                        img_generate_btn = gr.Button(
                            "🖼️ Transform Image",
                            variant="primary",
                            size="lg",
                            elem_classes=["generate-btn"]
                        )
                    
                    with gr.Column(scale=1):
                        img_output_image = gr.Image(
                            label="🖼️ Transformed Image",
                            height=500,
                            show_download_button=True
                        )
                        img_download_file = gr.File(
                            label="📥 Download PNG (with metadata)",
                            file_types=[".png"]
                        )
                        img_info = gr.Textbox(
                            label="ℹ️ Generation Info",
                            lines=6,
                            interactive=False
                        )
        
        # Event handlers
        txt_generate_btn.click(
            fn=generate_txt2img,
            inputs=[txt_prompt, txt_negative, txt_steps, txt_guidance, 
                   txt_width, txt_height, txt_seed, txt_quality],
            outputs=[txt_output_image, txt_download_file, txt_info],
            show_progress=True
        )
        
        img_generate_btn.click(
            fn=generate_img2img,
            inputs=[img_input, img_prompt, img_negative, img_steps, img_guidance,
                   img_strength, img_seed, img_quality],
            outputs=[img_output_image, img_download_file, img_info],
            show_progress=True
        )
        
        # Example prompt buttons
        txt_example_btn.click(fn=get_random_prompt, outputs=[txt_prompt])
        img_example_btn.click(fn=get_random_prompt, outputs=[img_prompt])
        
        # Clear buttons
        txt_clear_btn.click(lambda: "", outputs=[txt_prompt])
        img_clear_btn.click(lambda: "", outputs=[img_prompt])
    
    return demo

# Initialize and launch
if __name__ == "__main__":
    logger.info(f"🚀 Initializing CyberRealistic Pony Generator on {DEVICE}")
    logger.info(f"📱 PyTorch version: {torch.__version__}")
    logger.info(f"🛡️ NSFW Content Filter: Enabled")
    
    demo = create_interface()
    demo.queue(max_size=20)  # Enable queuing for better UX
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        share=False  # Set to True if you want a public link
    )
        
        # Example prompt buttons
        txt_example_btn.click(fn=get_random_prompt, outputs=[txt_prompt])
        img_example_btn.click(fn=get_random_prompt, outputs=[img_prompt])
        
        # Clear buttons
        txt_clear_btn.click(lambda: "", outputs=[txt_prompt])
        img_clear_btn.click(lambda: "", outputs=[img_prompt])
    
    return demo

# Initialize and launch
if __name__ == "__main__":
    logger.info(f"🚀 Initializing CyberRealistic Pony Generator on {DEVICE}")
    logger.info(f"📱 PyTorch version: {torch.__version__}")
    logger.info(f"🛡️ NSFW Content Filter: Enabled")
    
    demo = create_interface()
    demo.queue(max_size=20)  # Enable queuing for better UX
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        share=False  # Set to True if you want a public link
    )