Update app.py

app.py

@@ -21,7 +21,7 @@ pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev
 # Load LoRA data
 flux_loras_raw = [
     {
-        "image": "examples/1.webp",
+        "image": "examples/1.png",
         "title": "Studio Ghibli",
         "repo": "openfree/flux-chatgpt-ghibli-lora",
         "trigger_word": "ghibli",
@@ -29,7 +29,7 @@ flux_loras_raw = [
         "likes": 0
     },
     {
-        "image": "examples/2.webp",
+        "image": "examples/2.png",
         "title": "Winslow Homer",
         "repo": "openfree/winslow-homer",
         "trigger_word": "homer",
@@ -37,7 +37,7 @@ flux_loras_raw = [
         "likes": 0
     },
     {
-        "image": "examples/3.webp",
+        "image": "examples/3.png",
         "title": "Van Gogh",
         "repo": "openfree/van-gogh",
         "trigger_word": "gogh",
@@ -45,7 +45,7 @@ flux_loras_raw = [
         "likes": 0
     },
     {
-        "image": "examples/4.webp",
+        "image": "examples/4.png",
         "title": "Paul Cézanne",
         "repo": "openfree/paul-cezanne",
         "trigger_word": "Cezanne",
@@ -53,7 +53,7 @@ flux_loras_raw = [
         "likes": 0
     },
     {
-        "image": "examples/5.webp",
+        "image": "examples/5.png",
         "title": "Renoir",
         "repo": "openfree/pierre-auguste-renoir",
         "trigger_word": "Renoir",
@@ -61,7 +61,7 @@ flux_loras_raw = [
         "likes": 0
     },
     {
-        "image": "examples/6.webp",
+        "image": "examples/6.png",
         "title": "Claude Monet",
         "repo": "openfree/claude-monet",
         "trigger_word": "claude monet",
@@ -69,7 +69,7 @@ flux_loras_raw = [
         "likes": 0
     },
     {
-        "image": "examples/7.webp",
+        "image": "examples/7.png",
         "title": "Fantasy Art",
         "repo": "openfree/myt-flux-fantasy",
         "trigger_word": "fantasy",
@@ -227,36 +227,783 @@ def classify_gallery(flux_loras):
         sorted_gallery = sorted(flux_loras, key=lambda x: x.get("likes", 0), reverse=True)
         gallery_items = []
         
-        # Color mapping for each style
-        style_colors = {
-            "Studio Ghibli": "81C784/FFFFFF",  # Green
-            "Winslow Homer": "64B5F6/FFFFFF",  # Blue
-            "Van Gogh": "FFD54F/333333",       # Yellow
-            "Paul Cézanne": "FF8A65/FFFFFF",   # Orange
-            "Renoir": "F06292/FFFFFF",         # Pink
-            "Claude Monet": "9575CD/FFFFFF",   # Purple
-            "Fantasy Art": "4FC3F7/FFFFFF"     # Light Blue
+        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/"])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"]
                 
-                # If image is a local file path, use a styled placeholder
-                if isinstance(image_url, str) and (image_url.startswith("examples/") or image_url.startswith("/home/") or image_url.startswith("samples/") or not image_url.startswith("http")):
-                    color = style_colors.get(title, "E0E7FF/818CF8")
-                    image_url = f"https://via.placeholder.com/512x512/{color}?text={title.replace(' ', '+')}"
-                    
-                gallery_items.append((image_url, 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 after filtering")
+            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)):
@@ -707,4 +1454,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 demo.queue(default_concurrency_limit=None)
-demo.launch()
+demo.launch(allowed_paths=["examples/"])