Update app.py

app.py

@@ -725,733 +725,5 @@ with gr.Blocks(css=css) as demo:
         outputs=[gallery, gr_flux_loras]
     )
 
-demo.queue(default_concurrency_limit=None)
-demo.launch(allowed_paths=["examples/"])import gradio as gr
-import numpy as np
-import spaces
-import torch
-import random
-import json
-import os
-from PIL import Image
-from diffusers import FluxKontextPipeline
-from diffusers.utils import load_image
-from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, list_repo_files
-from safetensors.torch import load_file
-import requests
-import re
-
-# Load Kontext model
-MAX_SEED = np.iinfo(np.int32).max
-
-pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
-
-# Load LoRA data
-flux_loras_raw = [
-    {
-        "image": "examples/1.png",
-        "title": "Studio Ghibli",
-        "repo": "openfree/flux-chatgpt-ghibli-lora",
-        "trigger_word": "ghibli",
-        "weights": "pytorch_lora_weights.safetensors",
-        "likes": 0
-    },
-    {
-        "image": "examples/2.png",
-        "title": "Winslow Homer",
-        "repo": "openfree/winslow-homer",
-        "trigger_word": "homer",
-        "weights": "pytorch_lora_weights.safetensors",
-        "likes": 0
-    },
-    {
-        "image": "examples/3.png",
-        "title": "Van Gogh",
-        "repo": "openfree/van-gogh",
-        "trigger_word": "gogh",
-        "weights": "pytorch_lora_weights.safetensors",
-        "likes": 0
-    },
-    {
-        "image": "examples/4.png",
-        "title": "Paul Cézanne",
-        "repo": "openfree/paul-cezanne",
-        "trigger_word": "Cezanne",
-        "weights": "pytorch_lora_weights.safetensors",
-        "likes": 0
-    },
-    {
-        "image": "examples/5.png",
-        "title": "Renoir",
-        "repo": "openfree/pierre-auguste-renoir",
-        "trigger_word": "Renoir",
-        "weights": "pytorch_lora_weights.safetensors",
-        "likes": 0
-    },
-    {
-        "image": "examples/6.png",
-        "title": "Claude Monet",
-        "repo": "openfree/claude-monet",
-        "trigger_word": "claude monet",
-        "weights": "pytorch_lora_weights.safetensors",
-        "likes": 0
-    },
-    {
-        "image": "examples/7.png",
-        "title": "Fantasy Art",
-        "repo": "openfree/myt-flux-fantasy",
-        "trigger_word": "fantasy",
-        "weights": "pytorch_lora_weights.safetensors",
-        "likes": 0
-    }
-]
-print(f"Loaded {len(flux_loras_raw)} LoRAs")
-# Global variables for LoRA management
-current_lora = None
-lora_cache = {}
-
-def load_lora_weights(repo_id, weights_filename):
-    """Load LoRA weights from HuggingFace"""
-    try:
-        # First try with the specified filename
-        try:
-            lora_path = hf_hub_download(repo_id=repo_id, filename=weights_filename)
-            if repo_id not in lora_cache:
-                lora_cache[repo_id] = lora_path
-            return lora_path
-        except Exception as e:
-            print(f"Failed to load {weights_filename}, trying to find alternative LoRA files...")
-            
-            # If the specified file doesn't exist, try to find any .safetensors file
-            from huggingface_hub import list_repo_files
-            try:
-                files = list_repo_files(repo_id)
-                safetensors_files = [f for f in files if f.endswith(('.safetensors', '.bin')) and 'lora' in f.lower()]
-                
-                if not safetensors_files:
-                    # Try without 'lora' in filename
-                    safetensors_files = [f for f in files if f.endswith('.safetensors')]
-                
-                if safetensors_files:
-                    # Try the first available file
-                    for file in safetensors_files:
-                        try:
-                            print(f"Trying alternative file: {file}")
-                            lora_path = hf_hub_download(repo_id=repo_id, filename=file)
-                            if repo_id not in lora_cache:
-                                lora_cache[repo_id] = lora_path
-                            print(f"Successfully loaded alternative LoRA file: {file}")
-                            return lora_path
-                        except:
-                            continue
-                            
-                print(f"No suitable LoRA files found in {repo_id}")
-                return None
-                
-            except Exception as list_error:
-                print(f"Error listing files in repo {repo_id}: {list_error}")
-                return None
-                
-    except Exception as e:
-        print(f"Error loading LoRA from {repo_id}: {e}")
-        return None
-
-def update_selection(selected_state: gr.SelectData, flux_loras):
-    """Update UI when a LoRA is selected"""
-    if selected_state.index >= len(flux_loras):
-        return "### No LoRA selected", gr.update(), None
-    
-    lora = flux_loras[selected_state.index]
-    lora_title = lora["title"]
-    lora_repo = lora["repo"]
-    trigger_word = lora["trigger_word"]
-    
-    # Create a more informative selected text
-    updated_text = f"### 🎨 Selected Style: {lora_title}"
-    new_placeholder = f"Describe additional details, e.g., 'wearing a red hat' or 'smiling'"
-    
-    return updated_text, gr.update(placeholder=new_placeholder), selected_state.index
-
-def get_huggingface_lora(link):
-    """Download LoRA from HuggingFace link"""
-    split_link = link.split("/")
-    if len(split_link) == 2:
-        try:
-            model_card = ModelCard.load(link)
-            trigger_word = model_card.data.get("instance_prompt", "")
-            
-            # Try to find the correct safetensors file
-            files = list_repo_files(link)
-            safetensors_files = [f for f in files if f.endswith('.safetensors')]
-            
-            # Prioritize files with 'lora' in the name
-            lora_files = [f for f in safetensors_files if 'lora' in f.lower()]
-            if lora_files:
-                safetensors_file = lora_files[0]
-            elif safetensors_files:
-                safetensors_file = safetensors_files[0]
-            else:
-                # Try .bin files as fallback
-                bin_files = [f for f in files if f.endswith('.bin') and 'lora' in f.lower()]
-                if bin_files:
-                    safetensors_file = bin_files[0]
-                else:
-                    safetensors_file = "pytorch_lora_weights.safetensors"  # Default fallback
-            
-            print(f"Found LoRA file: {safetensors_file} in {link}")
-            return split_link[1], safetensors_file, trigger_word
-            
-        except Exception as e:
-            print(f"Error in get_huggingface_lora: {e}")
-            # Try basic detection
-            try:
-                files = list_repo_files(link)
-                safetensors_file = next((f for f in files if f.endswith('.safetensors')), "pytorch_lora_weights.safetensors")
-                return split_link[1], safetensors_file, ""
-            except:
-                raise Exception(f"Error loading LoRA: {e}")
-    else:
-        raise Exception("Invalid HuggingFace repository format")
-
-def load_custom_lora(link):
-    """Load custom LoRA from user input"""
-    if not link:
-        return gr.update(visible=False), "", gr.update(visible=False), None, gr.Gallery(selected_index=None), "### 🎨 Select an art style from the gallery", None
-    
-    try:
-        repo_name, weights_file, trigger_word = get_huggingface_lora(link)
-        
-        card = f'''
-        <div class="custom_lora_card">
-            <div style="display: flex; align-items: center; margin-bottom: 12px;">
-                <span style="font-size: 18px; margin-right: 8px;">✅</span>
-                <strong style="font-size: 16px;">Custom LoRA Loaded!</strong>
-            </div>
-            <div style="background: rgba(255, 255, 255, 0.8); padding: 12px; border-radius: 8px;">
-                <h4 style="margin: 0 0 8px 0; color: #333;">{repo_name}</h4>
-                <small style="color: #666;">{"Trigger: <code style='background: #f0f0f0; padding: 2px 6px; border-radius: 4px;'><b>"+trigger_word+"</b></code>" if trigger_word else "No trigger word found"}</small>
-            </div>
-        </div>
-        '''
-        
-        custom_lora_data = {
-            "repo": link,
-            "weights": weights_file,
-            "trigger_word": trigger_word
-        }
-        
-        return gr.update(visible=True), card, gr.update(visible=True), custom_lora_data, gr.Gallery(selected_index=None), f"🎨 Custom Style: {repo_name}", None
-    
-    except Exception as e:
-        return gr.update(visible=True), f"Error: {str(e)}", gr.update(visible=False), None, gr.update(), "### 🎨 Select an art style from the gallery", None
-
-def remove_custom_lora():
-    """Remove custom LoRA"""
-    return "", gr.update(visible=False), gr.update(visible=False), None, None
-
-def classify_gallery(flux_loras):
-    """Sort gallery by likes"""
-    try:
-        sorted_gallery = sorted(flux_loras, key=lambda x: x.get("likes", 0), reverse=True)
-        gallery_items = []
-        
-        for item in sorted_gallery:
-            if "image" in item and "title" in item:
-                image_url = item["image"]
-                title = item["title"]
-                
-                # Try to load local images with PIL
-                if isinstance(image_url, str) and image_url.startswith("examples/"):
-                    try:
-                        import os
-                        # Try different possible paths
-                        possible_paths = [
-                            image_url,
-                            os.path.join(os.getcwd(), image_url),
-                            f"/home/user/app/{image_url}"
-                        ]
-                        
-                        image_loaded = False
-                        for path in possible_paths:
-                            if os.path.exists(path):
-                                try:
-                                    pil_image = Image.open(path)
-                                    gallery_items.append((pil_image, title))
-                                    image_loaded = True
-                                    print(f"✓ Successfully loaded image from: {path}")
-                                    break
-                                except Exception as e:
-                                    print(f"Failed to open image at {path}: {e}")
-                        
-                        if not image_loaded:
-                            print(f"✗ Could not load image: {image_url}")
-                            # Use the original path as fallback
-                            gallery_items.append((image_url, title))
-                    except Exception as e:
-                        print(f"Error processing image {image_url}: {e}")
-                        gallery_items.append((image_url, title))
-                else:
-                    # For URLs or other paths, use as-is
-                    gallery_items.append((image_url, title))
-        
-        if not gallery_items:
-            print("No gallery items found")
-            return [], sorted_gallery
-        
-        print(f"Gallery loaded with {len(gallery_items)} items")
-        return gallery_items, sorted_gallery
-    except Exception as e:
-        print(f"Error in classify_gallery: {e}")
-        import traceback
-        traceback.print_exc()
-        return [], []
-
-def infer_with_lora_wrapper(input_image, prompt, selected_index, custom_lora, seed=42, randomize_seed=False, guidance_scale=2.5, lora_scale=1.0, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
-    """Wrapper function to handle state serialization"""
-    return infer_with_lora(input_image, prompt, selected_index, custom_lora, seed, randomize_seed, guidance_scale, lora_scale, flux_loras, progress)
-
-@spaces.GPU
-def infer_with_lora(input_image, prompt, selected_index, custom_lora, seed=42, randomize_seed=False, guidance_scale=2.5, lora_scale=1.0, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
-    """Generate image with selected LoRA"""
-    global current_lora, pipe
-    
-    # Check if input image is provided
-    if input_image is None:
-        gr.Warning("Please upload your portrait photo first! 📸")
-        return None, seed, gr.update(visible=False)
-    
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    # Determine which LoRA to use
-    lora_to_use = None
-    if custom_lora:
-        lora_to_use = custom_lora
-    elif selected_index is not None and flux_loras and selected_index < len(flux_loras):
-        lora_to_use = flux_loras[selected_index]
-    # Load LoRA if needed
-    if lora_to_use and lora_to_use != current_lora:
-        try:
-            # Unload current LoRA
-            if current_lora:
-                pipe.unload_lora_weights()
-                print(f"Unloaded previous LoRA")
-            
-            # Load new LoRA
-            repo_id = lora_to_use.get("repo", "unknown")
-            weights_file = lora_to_use.get("weights", "pytorch_lora_weights.safetensors")
-            print(f"Loading LoRA: {repo_id} with weights: {weights_file}")
-            
-            lora_path = load_lora_weights(repo_id, weights_file)
-            if lora_path:
-                pipe.load_lora_weights(lora_path, adapter_name="selected_lora")
-                pipe.set_adapters(["selected_lora"], adapter_weights=[lora_scale])
-                print(f"Successfully loaded: {lora_path} with scale {lora_scale}")
-                current_lora = lora_to_use
-            else:
-                print(f"Failed to load LoRA from {repo_id}")
-                gr.Warning(f"Failed to load {lora_to_use.get('title', 'style')}. Please try a different art style.")
-                return None, seed, gr.update(visible=False)
-            
-        except Exception as e:
-            print(f"Error loading LoRA: {e}")
-            # Continue without LoRA
-    else:
-        if lora_to_use:
-            print(f"Using already loaded LoRA: {lora_to_use.get('repo', 'unknown')}")
-    
-    try:
-        # Convert image to RGB
-        input_image = input_image.convert("RGB")
-    except Exception as e:
-        print(f"Error processing image: {e}")
-        gr.Warning("Error processing the uploaded image. Please try a different photo. 📸")
-        return None, seed, gr.update(visible=False)
-    
-    # Check if LoRA is selected
-    if lora_to_use is None:
-        gr.Warning("Please select an art style from the gallery first! 🎨")
-        return None, seed, gr.update(visible=False)
-    
-    # Add trigger word to prompt
-    trigger_word = lora_to_use.get("trigger_word", "")
-    
-    # Special handling for different art styles
-    if trigger_word == "ghibli":
-        prompt = f"Create a Studio Ghibli anime style portrait of the person in the photo, {prompt}. Maintain the facial identity while transforming into whimsical anime art style."
-    elif trigger_word == "homer":
-        prompt = f"Paint the person in Winslow Homer's American realist style, {prompt}. Keep facial features while applying watercolor and marine art techniques."
-    elif trigger_word == "gogh":
-        prompt = f"Transform the portrait into Van Gogh's post-impressionist style with swirling brushstrokes, {prompt}. Maintain facial identity with expressive colors."
-    elif trigger_word == "Cezanne":
-        prompt = f"Render the person in Paul Cézanne's geometric post-impressionist style, {prompt}. Keep facial structure while applying structured brushwork."
-    elif trigger_word == "Renoir":
-        prompt = f"Paint the portrait in Pierre-Auguste Renoir's impressionist style with soft light, {prompt}. Maintain identity with luminous skin tones."
-    elif trigger_word == "claude monet":
-        prompt = f"Create an impressionist portrait in Claude Monet's style with visible brushstrokes, {prompt}. Keep facial features while using light and color."
-    elif trigger_word == "fantasy":
-        prompt = f"Transform into an epic fantasy character portrait, {prompt}. Maintain facial identity while adding magical and fantastical elements."
-    elif trigger_word == ", How2Draw":
-        prompt = f"create a How2Draw sketch of the person of the photo {prompt}, maintain the facial identity of the person and general features"
-    elif trigger_word == ", video game screenshot in the style of THSMS":
-        prompt = f"create a video game screenshot in the style of THSMS with the person from the photo, {prompt}. maintain the facial identity of the person and general features"
-    else:
-        prompt = f"convert the style of this portrait photo to {trigger_word} while maintaining the identity of the person. {prompt}. Make sure to maintain the person's facial identity and features, while still changing the overall style to {trigger_word}."
-    
-    try:
-        image = pipe(
-            image=input_image, 
-            prompt=prompt,
-            guidance_scale=guidance_scale,
-            generator=torch.Generator().manual_seed(seed),
-        ).images[0]
-        
-        return image, seed, gr.update(visible=True)
-    
-    except Exception as e:
-        print(f"Error during inference: {e}")
-        return None, seed, gr.update(visible=False)
-
-# CSS styling with beautiful gradient pastel design
-css = """
-/* Global background and container styling */
-.gradio-container {
-    background: linear-gradient(135deg, #ffeef8 0%, #e6f3ff 25%, #fff4e6 50%, #f0e6ff 75%, #e6fff9 100%);
-    font-family: 'Inter', sans-serif;
-}
-
-/* Main app container */
-#main_app {
-    display: flex;
-    gap: 24px;
-    padding: 20px;
-    background: rgba(255, 255, 255, 0.85);
-    backdrop-filter: blur(20px);
-    border-radius: 24px;
-    box-shadow: 0 10px 40px rgba(0, 0, 0, 0.08);
-}
-
-/* Box column styling */
-#box_column {
-    min-width: 400px;
-}
-
-/* Gallery box with glassmorphism */
-#gallery_box {
-    background: linear-gradient(135deg, rgba(255, 255, 255, 0.9) 0%, rgba(240, 248, 255, 0.9) 100%);
-    border-radius: 20px;
-    padding: 20px;
-    box-shadow: 0 8px 32px rgba(135, 206, 250, 0.2);
-    border: 1px solid rgba(255, 255, 255, 0.8);
-}
-
-/* Input image styling */
-.image-container {
-    border-radius: 16px;
-    overflow: hidden;
-    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1);
-}
-
-/* Gallery styling */
-#gallery {
-    overflow-y: scroll !important;
-    max-height: 400px;
-    padding: 12px;
-    background: rgba(255, 255, 255, 0.5);
-    border-radius: 16px;
-    scrollbar-width: thin;
-    scrollbar-color: #ddd6fe #f5f3ff;
-}
-
-#gallery::-webkit-scrollbar {
-    width: 8px;
-}
-
-#gallery::-webkit-scrollbar-track {
-    background: #f5f3ff;
-    border-radius: 10px;
-}
-
-#gallery::-webkit-scrollbar-thumb {
-    background: linear-gradient(180deg, #c7d2fe 0%, #ddd6fe 100%);
-    border-radius: 10px;
-}
-
-/* Selected LoRA text */
-#selected_lora {
-    background: linear-gradient(135deg, #818cf8 0%, #a78bfa 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    background-clip: text;
-    font-weight: 700;
-    font-size: 18px;
-    text-align: center;
-    padding: 12px;
-    margin-bottom: 16px;
-}
-
-/* Prompt input field */
-#prompt {
-    flex-grow: 1;
-    border: 2px solid transparent;
-    background: linear-gradient(white, white) padding-box,
-                linear-gradient(135deg, #a5b4fc 0%, #e9d5ff 100%) border-box;
-    border-radius: 12px;
-    padding: 12px 16px;
-    font-size: 16px;
-    transition: all 0.3s ease;
-}
-
-#prompt:focus {
-    box-shadow: 0 0 0 4px rgba(165, 180, 252, 0.25);
-}
-
-/* Run button with animated gradient */
-#run_button {
-    background: linear-gradient(135deg, #a78bfa 0%, #818cf8 25%, #60a5fa 50%, #34d399 75%, #fbbf24 100%);
-    background-size: 200% 200%;
-    animation: gradient-shift 3s ease infinite;
-    color: white;
-    border: none;
-    padding: 12px 32px;
-    border-radius: 12px;
-    font-weight: 600;
-    font-size: 16px;
-    cursor: pointer;
-    transition: all 0.3s ease;
-    box-shadow: 0 4px 20px rgba(167, 139, 250, 0.4);
-}
-
-#run_button:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 6px 30px rgba(167, 139, 250, 0.6);
-}
-
-@keyframes gradient-shift {
-    0% { background-position: 0% 50%; }
-    50% { background-position: 100% 50%; }
-    100% { background-position: 0% 50%; }
-}
-
-/* Custom LoRA card */
-.custom_lora_card {
-    background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
-    border: 1px solid #fcd34d;
-    border-radius: 12px;
-    padding: 16px;
-    margin: 12px 0;
-    box-shadow: 0 4px 12px rgba(251, 191, 36, 0.2);
-}
-
-/* Result image container */
-.output-image {
-    border-radius: 16px;
-    overflow: hidden;
-    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.12);
-    margin-top: 20px;
-}
-
-/* Accordion styling */
-.accordion {
-    background: rgba(249, 250, 251, 0.9);
-    border-radius: 12px;
-    border: 1px solid rgba(229, 231, 235, 0.8);
-    margin-top: 16px;
-}
-
-/* Slider styling */
-.slider-container {
-    padding: 8px 0;
-}
-
-input[type="range"] {
-    background: linear-gradient(to right, #e0e7ff 0%, #c7d2fe 100%);
-    border-radius: 8px;
-    height: 6px;
-}
-
-/* Reuse button */
-button:not(#run_button) {
-    background: linear-gradient(135deg, #f0abfc 0%, #c084fc 100%);
-    color: white;
-    border: none;
-    padding: 8px 20px;
-    border-radius: 8px;
-    font-weight: 500;
-    cursor: pointer;
-    transition: all 0.3s ease;
-}
-
-button:not(#run_button):hover {
-    transform: translateY(-1px);
-    box-shadow: 0 4px 16px rgba(192, 132, 252, 0.4);
-}
-
-/* Title styling */
-h1 {
-    background: linear-gradient(135deg, #6366f1 0%, #a855f7 25%, #ec4899 50%, #f43f5e 75%, #f59e0b 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    background-clip: text;
-    text-align: center;
-    font-size: 3.5rem;
-    font-weight: 800;
-    margin-bottom: 8px;
-    text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.1);
-}
-
-h1 small {
-    display: block;
-    background: linear-gradient(135deg, #94a3b8 0%, #64748b 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    background-clip: text;
-    font-size: 1rem;
-    font-weight: 500;
-    margin-top: 8px;
-}
-
-/* Checkbox styling */
-input[type="checkbox"] {
-    accent-color: #8b5cf6;
-}
-
-/* Label styling */
-label {
-    color: #4b5563;
-    font-weight: 500;
-}
-
-/* Group containers */
-.gr-group {
-    background: rgba(255, 255, 255, 0.7);
-    border-radius: 16px;
-    padding: 20px;
-    border: 1px solid rgba(255, 255, 255, 0.9);
-    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.05);
-}
-"""
-
-# Create Gradio interface
-with gr.Blocks(css=css) as demo:
-    gr_flux_loras = gr.State(value=flux_loras_raw)
-    
-    title = gr.HTML(
-        """<h1>✨ Flux-Kontext FaceLORA
-        <small>Transform your portraits with AI-powered style transfer 🎨</small></h1>""",
-    )
-    
-    selected_state = gr.State(value=None)
-    custom_loaded_lora = gr.State(value=None)
-    
-    with gr.Row(elem_id="main_app"):
-        with gr.Column(scale=4, elem_id="box_column"):
-            with gr.Group(elem_id="gallery_box"):
-                input_image = gr.Image(label="Upload your portrait photo 📸", type="pil", height=300)
-                
-                gallery = gr.Gallery(
-                    label="Choose Your Art Style",
-                    allow_preview=False,
-                    columns=3,
-                    elem_id="gallery",
-                    show_share_button=False,
-                    height=400
-                )
-                
-                custom_model = gr.Textbox(
-                    label="🔗 Or use a custom LoRA from HuggingFace", 
-                    placeholder="e.g., username/lora-name",
-                    visible=True
-                )
-                custom_model_card = gr.HTML(visible=False)
-                custom_model_button = gr.Button("❌ Remove custom LoRA", visible=False)
-        
-        with gr.Column(scale=5):
-            with gr.Row():
-                prompt = gr.Textbox(
-                    label="Additional Details (optional)",
-                    show_label=False,
-                    lines=1,
-                    max_lines=1,
-                    placeholder="Describe additional details, e.g., 'wearing a red hat' or 'smiling'",
-                    elem_id="prompt"
-                )
-                run_button = gr.Button("Generate ✨", elem_id="run_button")
-            
-            result = gr.Image(label="Your Artistic Portrait", interactive=False)
-            reuse_button = gr.Button("🔄 Reuse this image", visible=False)
-            
-            with gr.Accordion("⚙️ Advanced Settings", open=False):
-                lora_scale = gr.Slider(
-                    label="Style Strength",
-                    minimum=0,
-                    maximum=2,
-                    step=0.1,
-                    value=1.0,
-                    info="How strongly to apply the art style (1.0 = balanced)"
-                )
-                seed = gr.Slider(
-                    label="Random Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=0,
-                    info="Set to 0 for random results"
-                )
-                randomize_seed = gr.Checkbox(label="🎲 Randomize seed for each generation", value=True)
-                guidance_scale = gr.Slider(
-                    label="Image Guidance",
-                    minimum=1,
-                    maximum=10,
-                    step=0.1,
-                    value=2.5,
-                    info="How closely to follow the input image (lower = more creative)"
-                )
-            
-            prompt_title = gr.Markdown(
-                value="### 🎨 Select an art style from the gallery",
-                visible=True,
-                elem_id="selected_lora",
-            )
-
-    # Event handlers
-    custom_model.input(
-        fn=load_custom_lora,
-        inputs=[custom_model],
-        outputs=[custom_model_card, custom_model_card, custom_model_button, custom_loaded_lora, gallery, prompt_title, selected_state],
-    )
-    
-    custom_model_button.click(
-        fn=remove_custom_lora,
-        outputs=[custom_model, custom_model_button, custom_model_card, custom_loaded_lora, selected_state]
-    )
-    
-    gallery.select(
-        fn=update_selection,
-        inputs=[gr_flux_loras],
-        outputs=[prompt_title, prompt, selected_state],
-        show_progress=False
-    )
-    
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer_with_lora_wrapper,
-        inputs=[input_image, prompt, selected_state, custom_loaded_lora, seed, randomize_seed, guidance_scale, lora_scale, gr_flux_loras],
-        outputs=[result, seed, reuse_button]
-    )
-    
-    reuse_button.click(
-        fn=lambda image: image,
-        inputs=[result],
-        outputs=[input_image]
-    )
-    
-    # Initialize gallery
-    demo.load(
-        fn=classify_gallery, 
-        inputs=[gr_flux_loras], 
-        outputs=[gallery, gr_flux_loras]
-    )
-
 demo.queue(default_concurrency_limit=None)
 demo.launch(allowed_paths=["examples/"])