app.py

import os
huggingface_token = os.getenv("HF_TOKEN")
if not huggingface_token:
    print("Warning: Hugging Face token is not set.")

import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time
import requests
import pandas as pd
from transformers import pipeline
from gradio_imageslider import ImageSlider
import numpy as np
import warnings

try:
    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu", token=huggingface_token)
except Exception as e:
    print(f"Translation model load failed: {str(e)}")
    # If the translation model fails to load, return the original text
    def translator(text, max_length=512):
        return [{'translation_text': text}]
        
# Load prompts for randomization
df = pd.read_csv('prompts.csv', header=None)
prompt_values = df.values.flatten()

# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
    loras = json.load(f)

# Load base FLUX model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to(device)

# Settings for LoRA
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)

# Set up image-to-image pipeline
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
    base_model,
    vae=good_vae,
    transformer=pipe.transformer,
    text_encoder=pipe.text_encoder,
    tokenizer=pipe.tokenizer,
    text_encoder_2=pipe.text_encoder_2,
    tokenizer_2=pipe.tokenizer_2,
    torch_dtype=dtype
).to(device)

MAX_SEED = 2**32 - 1
MAX_PIXEL_BUDGET = 1024 * 1024

pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def download_file(url, directory=None):
    if directory is None:
        directory = os.getcwd()  # Use current working directory if not specified
    
    # Get the filename from the URL
    filename = url.split('/')[-1]
    
    # Full path for the downloaded file
    filepath = os.path.join(directory, filename)
    
    # Download the file
    response = requests.get(url)
    response.raise_for_status()  # Raise an exception for bad status codes
    
    # Write the content to the file
    with open(filepath, 'wb') as file:
        file.write(response.content)
    
    return filepath

def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
    selected_index = evt.index
    selected_indices = selected_indices or []
    if selected_index in selected_indices:
        selected_indices.remove(selected_index)
    else:
        if len(selected_indices) < 3:
            selected_indices.append(selected_index)
        else:
            gr.Warning("You can select up to 3 LoRAs, remove one to select a new one.")
            return gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), width, height, gr.update(), gr.update(), gr.update()

    selected_info_1 = "Select LoRA 1"
    selected_info_2 = "Select LoRA 2"
    selected_info_3 = "Select LoRA 3"
    
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_scale_3 = 1.15    
    lora_image_1 = None
    lora_image_2 = None
    lora_image_3 = None
    
    if len(selected_indices) >= 1:
        lora1 = loras_state[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras_state[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    if len(selected_indices) >= 3:
        lora3 = loras_state[selected_indices[2]]
        selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}](https://huggingface.co/{lora3['repo']}) ✨"
        lora_image_3 = lora3['image']
        
    if selected_indices:
        last_selected_lora = loras_state[selected_indices[-1]]
        new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
    else:
        new_placeholder = "Type a prompt after selecting a LoRA"

    return gr.update(placeholder=new_placeholder), selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, width, height, lora_image_1, lora_image_2, lora_image_3

def remove_lora(selected_indices, loras_state, index_to_remove):
    if len(selected_indices) > index_to_remove:
        selected_indices.pop(index_to_remove)
    
    selected_info_1 = "Select LoRA 1"
    selected_info_2 = "Select LoRA 2"
    selected_info_3 = "Select LoRA 3"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_scale_3 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    lora_image_3 = None
    
    for i, idx in enumerate(selected_indices):
        lora = loras_state[idx]
        if i == 0:
            selected_info_1 = f"### LoRA 1 Selected: [{lora['title']}]({lora['repo']}) ✨"
            lora_image_1 = lora['image']
        elif i == 1:
            selected_info_2 = f"### LoRA 2 Selected: [{lora['title']}]({lora['repo']}) ✨"
            lora_image_2 = lora['image']
        elif i == 2:
            selected_info_3 = f"### LoRA 3 Selected: [{lora['title']}]({lora['repo']}) ✨"
            lora_image_3 = lora['image']
    
    return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3

def remove_lora_1(selected_indices, loras_state):
    return remove_lora(selected_indices, loras_state, 0)

def remove_lora_2(selected_indices, loras_state):
    return remove_lora(selected_indices, loras_state, 1)

def remove_lora_3(selected_indices, loras_state):
    return remove_lora(selected_indices, loras_state, 2)

def randomize_loras(selected_indices, loras_state):
    try:
        if len(loras_state) < 3:
            raise gr.Error("Not enough LoRAs to randomize.")
        selected_indices = random.sample(range(len(loras_state)), 3)
        lora1 = loras_state[selected_indices[0]]
        lora2 = loras_state[selected_indices[1]]
        lora3 = loras_state[selected_indices[2]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
        selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}](https://huggingface.co/{lora3['repo']}) ✨"
        lora_scale_1 = 1.15
        lora_scale_2 = 1.15
        lora_scale_3 = 1.15
        lora_image_1 = lora1.get('image', 'path/to/default/image.png')
        lora_image_2 = lora2.get('image', 'path/to/default/image.png')
        lora_image_3 = lora3.get('image', 'path/to/default/image.png')
        random_prompt = random.choice(prompt_values)
        return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3, random_prompt
    except Exception as e:
        print(f"Error in randomize_loras: {str(e)}")
        return "Error", "Error", "Error", [], 1.15, 1.15, 1.15, 'path/to/default/image.png', 'path/to/default/image.png', 'path/to/default/image.png', ""

def add_custom_lora(custom_lora, selected_indices, current_loras):
    if custom_lora:
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            existing_item_index = next((index for (index, item) in enumerate(current_loras) if item['repo'] == repo), None)
            if existing_item_index is None:
                if repo.endswith(".safetensors") and repo.startswith("http"):
                    repo = download_file(repo)
                new_item = {
                    "image": image if image else "/home/user/app/custom.png",
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(f"New LoRA: {new_item}")
                existing_item_index = len(current_loras)
                current_loras.append(new_item)
            
            # Update gallery
            gallery_items = [(item["image"], item["title"]) for item in current_loras]
            # Update selected_indices if there's room
            if len(selected_indices) < 3:
                selected_indices.append(existing_item_index)
            else:
                gr.Warning("You can select up to 3 LoRAs, remove one to select a new one.")

            # Update selected_info and images
            selected_info_1 = "Select a LoRA 1"
            selected_info_2 = "Select a LoRA 2"
            selected_info_3 = "Select a LoRA 3"
            lora_scale_1 = 1.15
            lora_scale_2 = 1.15
            lora_scale_3 = 1.15
            lora_image_1 = None
            lora_image_2 = None
            lora_image_3 = None
            if len(selected_indices) >= 1:
                lora1 = current_loras[selected_indices[0]]
                selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨"
                lora_image_1 = lora1['image'] if lora1['image'] else None
            if len(selected_indices) >= 2:
                lora2 = current_loras[selected_indices[1]]
                selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨"
                lora_image_2 = lora2['image'] if lora2['image'] else None
            if len(selected_indices) >= 3:
                lora3 = current_loras[selected_indices[2]]
                selected_info_3 = f"### LoRA 3 Selected: {lora3['title']} ✨"
                lora_image_3 = lora3['image'] if lora3['image'] else None
            print("Finished adding custom LoRA")
            return (
                current_loras,
                gr.update(value=gallery_items),
                selected_info_1, 
                selected_info_2,
                selected_info_3,
                selected_indices,
                lora_scale_1,
                lora_scale_2,
                lora_scale_3,
                lora_image_1,
                lora_image_2,
                lora_image_3,
                gr.update(visible=True)  # Make "Remove Custom LoRA" button visible
            )
        except Exception as e:
            print(e)
            gr.Warning(str(e))
            return current_loras, gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
    else:
        return current_loras, gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()

def remove_custom_lora(selected_indices, current_loras):
    if current_loras:
        custom_lora_repo = current_loras[-1]['repo']
        # Remove from loras list
        current_loras = current_loras[:-1]
        # Remove from selected_indices if selected
        custom_lora_index = len(current_loras)
        if custom_lora_index in selected_indices:
            selected_indices.remove(custom_lora_index)
    # Update gallery
    gallery_items = [(item["image"], item["title"]) for item in current_loras]
    # Update selected_info and images
    selected_info_1 = "Select a LoRA 1"
    selected_info_2 = "Select a LoRA 2"
    selected_info_3 = "Select a LoRA 3"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_scale_3 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    lora_image_3 = None
    if len(selected_indices) >= 1:
        lora1 = current_loras[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = current_loras[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    if len(selected_indices) >= 3:
        lora3 = current_loras[selected_indices[2]]
        selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}]({lora3['repo']}) ✨"
        lora_image_3 = lora3['image']
    # If no custom LoRA remains, hide the "Remove Custom LoRA" button
    remove_button_visibility = gr.update(visible=False) if not any("custom" in lora['repo'] for lora in current_loras) else gr.update(visible=True)
    return (
        current_loras,
        gr.update(value=gallery_items),
        selected_info_1,
        selected_info_2,
        selected_info_3,
        selected_indices,
        lora_scale_1,
        lora_scale_2,
        lora_scale_3,
        lora_image_1,
        lora_image_2,
        lora_image_3,
        remove_button_visibility
    )

@spaces.GPU(duration=75)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
    print("Generating image...")
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    with calculateDuration("Generating image"):
        # Generate image iteratively
        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt_mash,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            joint_attention_kwargs={"scale": 1.0},
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img

@spaces.GPU(duration=75)
def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
    pipe_i2i.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    image_input = load_image(image_input_path)
    final_image = pipe_i2i(
        prompt=prompt_mash,
        image=image_input,
        strength=image_strength,
        num_inference_steps=steps,
        guidance_scale=cfg_scale,
        width=width,
        height=height,
        generator=generator,
        joint_attention_kwargs={"scale": 1.0},
        output_type="pil",
    ).images[0]
    return final_image

def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
    try:
        # Detect and translate Korean text if present
        if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
            try:
                translated = translator(prompt, max_length=512)[0]['translation_text']
                print(f"Original prompt: {prompt}")
                print(f"Translated prompt: {translated}")
                prompt = translated
            except Exception as e:
                print(f"Translation failed: {str(e)}")
                # Use the original prompt if translation fails

        if not selected_indices:
            raise gr.Error("You must select at least one LoRA before proceeding.")

        selected_loras = [loras_state[idx] for idx in selected_indices]

        # Build the prompt with trigger words
        prepends = []
        appends = []
        for lora in selected_loras:
            trigger_word = lora.get('trigger_word', '')
            if trigger_word:
                if lora.get("trigger_position") == "prepend":
                    prepends.append(trigger_word)
                else:
                    appends.append(trigger_word)
        prompt_mash = " ".join(prepends + [prompt] + appends)
        print("Prompt Mash: ", prompt_mash)

        # Unload previous LoRA weights
        with calculateDuration("Unloading LoRA"):
            pipe.unload_lora_weights()
            pipe_i2i.unload_lora_weights()
            
        print(f"Active adapters before loading: {pipe.get_active_adapters()}")
        
        # Load LoRA weights with respective scales
        lora_names = []
        lora_weights = []
        with calculateDuration("Loading LoRA weights"):
            for idx, lora in enumerate(selected_loras):
                try:
                    lora_name = f"lora_{idx}"
                    lora_path = lora['repo']
                    weight_name = lora.get("weights")
                    print(f"Loading LoRA {lora_name} from {lora_path}")
                    if image_input is not None:
                        if weight_name:
                            pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=lora_name)
                        else:
                            pipe_i2i.load_lora_weights(lora_path, adapter_name=lora_name)
                    else:
                        if weight_name:
                            pipe.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=lora_name)
                        else:
                            pipe.load_lora_weights(lora_path, adapter_name=lora_name)
                    lora_names.append(lora_name)
                    lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2 if idx == 1 else lora_scale_3)
                except Exception as e:
                    print(f"Failed to load LoRA {lora_name}: {str(e)}")

            print("Loaded LoRAs:", lora_names)
            print("Adapter weights:", lora_weights)
            
            if lora_names:
                if image_input is not None:
                    pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
                else:
                    pipe.set_adapters(lora_names, adapter_weights=lora_weights)
            else:
                print("No LoRAs were successfully loaded.")
                return None, seed, gr.update(visible=False)

        print(f"Active adapters after loading: {pipe.get_active_adapters()}")

        # Randomize seed if requested
        with calculateDuration("Randomizing seed"):
            if randomize_seed:
                seed = random.randint(0, MAX_SEED)

        if image_input is not None:
            final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
        else:
            image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
            final_image = None
            step_counter = 0
            for image in image_generator:
                step_counter += 1
                final_image = image
                progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
                yield image, seed, gr.update(value=progress_bar, visible=True)

        if final_image is None:
            raise Exception("Failed to generate image")
        
        return final_image, seed, gr.update(visible=False)

    except Exception as e:
        print(f"Error in run_lora: {str(e)}")
        return None, seed, gr.update(visible=False)

run_lora.zerogpu = True

def get_huggingface_safetensors(link):
    split_link = link.split("/")
    if len(split_link) == 2:
        model_card = ModelCard.load(link)
        base_model = model_card.data.get("base_model")
        print(f"Base model: {base_model}")
        if base_model not in ["black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"]:
            raise Exception("Not a FLUX LoRA!")
        image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
        trigger_word = model_card.data.get("instance_prompt", "")
        image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
        fs = HfFileSystem()
        safetensors_name = None
        try:
            list_of_files = fs.ls(link, detail=False)
            for file in list_of_files:
                if file.endswith(".safetensors"):
                    safetensors_name = file.split("/")[-1]
                if not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp")):
                    image_elements = file.split("/")
                    image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
        except Exception as e:
            print(e)
            raise gr.Error("Invalid Hugging Face repository with a *.safetensors LoRA")
        if not safetensors_name:
            raise gr.Error("No *.safetensors file found in the repository")
        return split_link[1], link, safetensors_name, trigger_word, image_url
    else:
        raise gr.Error("Invalid Hugging Face repository link")

def check_custom_model(link):
    if link.endswith(".safetensors"):
        # Treat as direct link to the LoRA weights
        title = os.path.basename(link)
        repo = link
        path = None  # No specific weight name
        trigger_word = ""
        image_url = None
        return title, repo, path, trigger_word, image_url
    elif link.startswith("https://"):
        if "huggingface.co" in link:
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
        else:
            raise Exception("Unsupported URL")
    else:
        # Assume it's a Hugging Face model path
        return get_huggingface_safetensors(link)

def update_history(new_image, history):
    """Updates the history gallery with the new image."""
    if history is None:
        history = []
    if new_image is not None:
        history.insert(0, new_image)
    return history

# Custom theme configuration
custom_theme = gr.themes.Base(
    primary_hue="blue",
    secondary_hue="purple",
    neutral_hue="slate",
).set(
    button_primary_background_fill="*primary_500",
    button_primary_background_fill_dark="*primary_600",
    button_primary_background_fill_hover="*primary_400",
    button_primary_border_color="*primary_500",
    button_primary_border_color_dark="*primary_600",
    button_primary_text_color="white",
    button_primary_text_color_dark="white",
    button_secondary_background_fill="*neutral_100",
    button_secondary_background_fill_dark="*neutral_700",
    button_secondary_background_fill_hover="*neutral_50",
    button_secondary_text_color="*neutral_800",
    button_secondary_text_color_dark="white",
    background_fill_primary="*neutral_50",
    background_fill_primary_dark="*neutral_900",
    block_background_fill="white",
    block_background_fill_dark="*neutral_800",
    block_label_background_fill="*primary_500",
    block_label_background_fill_dark="*primary_600",
    block_label_text_color="white",
    block_label_text_color_dark="white",
    block_title_text_color="*neutral_800",
    block_title_text_color_dark="white",
    input_background_fill="white",
    input_background_fill_dark="*neutral_800",
    input_border_color="*neutral_200",
    input_border_color_dark="*neutral_700",
    input_placeholder_color="*neutral_400",
    input_placeholder_color_dark="*neutral_400",
    shadow_spread="8px",
    shadow_inset="0px 2px 4px 0px rgba(0,0,0,0.05)"
)

css = '''
/* Basic button and component styles */
#gen_btn {
    height: 100%
}

#title {
    text-align: center
}

#title h1 {
    font-size: 3em; 
    display: inline-flex; 
    align-items: center
}

#title img {
    width: 100px; 
    margin-right: 0.25em
}

#lora_list {
    background: var(--block-background-fill);
    padding: 0 1em 0.3em; 
    font-size: 90%
}

/* Custom LoRA card styles */
.custom_lora_card {
    margin-bottom: 1em
}

.card_internal {
    display: flex;
    height: 100px;
    margin-top: 0.5em
}

.card_internal img {
    margin-right: 1em
}

/* Utility classes */
.styler {
    --form-gap-width: 0px !important
}

/* Progress bar styles */
#progress {
    height: 30px;
    width: 90% !important;
    margin: 0 auto !important;
}

#progress .generating {
    display: none
}

.progress-container {
    width: 100%;
    height: 30px;
    background-color: #f0f0f0;
    border-radius: 15px;
    overflow: hidden;
    margin-bottom: 20px
}

.progress-bar {
    height: 100%;
    background-color: #4f46e5;
    width: calc(var(--current) / var(--total) * 100%);
    transition: width 0.5s ease-in-out
}

/* Component-specific styles */
#component-8, .button_total {
    height: 100%; 
    align-self: stretch;
}

#loaded_loras [data-testid="block-info"] {
    font-size: 80%
}

#custom_lora_structure {
    background: var(--block-background-fill)
}

#custom_lora_btn {
    margin-top: auto;
    margin-bottom: 11px
}

#random_btn {
    font-size: 300%
}

#component-11 {
    align-self: stretch;
}

/* Gallery main styles */
#lora_gallery {
    margin: 20px 0;
    padding: 10px;
    border: 1px solid #ddd;
    border-radius: 12px;
    background: linear-gradient(to bottom right, #ffffff, #f8f9fa);
    width: 100% !important;
    height: 800px !important;
    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
    display: block !important;
}

/* Gallery grid styles */
#gallery {
    display: grid !important;
    grid-template-columns: repeat(10, 1fr) !important;
    gap: 10px !important;
    padding: 10px !important;
    width: 100% !important;
    height: 100% !important;
    overflow-y: auto !important;
    max-width: 100% !important;
}

/* Gallery item styles */
.gallery-item {
    position: relative !important;
    width: 100% !important;
    aspect-ratio: 1 !important;
    margin: 0 !important;
    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
    transition: transform 0.3s ease, box-shadow 0.3s ease;
    border-radius: 12px;
    overflow: hidden;
}

.gallery-item img {
    width: 100% !important;
    height: 100% !important;
    object-fit: cover !important;
    border-radius: 12px !important;
}

/* Gallery grid wrapper */
.wrap, .svelte-w6dy5e {
    display: grid !important;
    grid-template-columns: repeat(10, 1fr) !important;
    gap: 10px !important;
    width: 100% !important;
    max-width: 100% !important;
}

/* Common container styles */
.container, .content, .block, .contain {
    width: 100% !important;
    max-width: 100% !important;
    margin: 0 !important;
    padding: 0 !important;
}

.row {
    width: 100% !important;
    margin: 0 !important;
    padding: 0 !important;
}

/* Button styles */
.button_total {
    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
    transition: all 0.3s ease;
}

.button_total:hover {
    transform: translateY(-2px);
    box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05);
}

/* Input field styles */
input, textarea {
    box-shadow: inset 0 2px 4px 0 rgba(0, 0, 0, 0.06);
    transition: all 0.3s ease;
}

input:focus, textarea:focus {
    box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.5);
}

/* Component border-radius */
.gradio-container .input, 
.gradio-container .button,
.gradio-container .block {
    border-radius: 12px;
}

/* Scrollbar styles */
#gallery::-webkit-scrollbar {
    width: 8px;
}

#gallery::-webkit-scrollbar-track {
    background: #f1f1f1;
    border-radius: 4px;
}

#gallery::-webkit-scrollbar-thumb {
    background: #888;
    border-radius: 4px;
}

#gallery::-webkit-scrollbar-thumb:hover {
    background: #555;
}

/* Flex container */
.flex {
    width: 100% !important;
    max-width: 100% !important;
    display: flex !important;
}

/* Svelte specific classes */
.svelte-1p9xokt {
    width: 100% !important;
    max-width: 100% !important;
}

/* Hide Footer */
#footer {
    visibility: hidden;
}

/* Generated image and container styles */
#result_column, #result_column > div {
    display: flex !important;
    flex-direction: column !important;
    align-items: flex-start !important;
    width: 100% !important;
    margin: 0 !important;
}

.generated-image, .generated-image > div {
    display: flex !important;
    justify-content: flex-start !important;
    align-items: flex-start !important;
    width: 90% !important;
    max-width: 768px !important;
    margin: 0 !important;
    margin-left: 20px !important;
}

.generated-image img {
    margin: 0 !important;
    display: block !important;
    max-width: 100% !important;
}

/* History gallery left alignment */
.history-gallery {
    display: flex !important;
    justify-content: flex-start !important;
    width: 90% !important;
    max-width: 90% !important;
    margin: 0 !important;
    margin-left: 20px !important;
}
'''

with gr.Blocks(theme=custom_theme, css=css, delete_cache=(60, 3600)) as app:
    loras_state = gr.State(loras)
    selected_indices = gr.State([])

    gr.Markdown(
        """
        # GiniGen: Multi-LoRA (Image Training) Integrated Generation Model
        ### Instructions:
        Select a model from the gallery (up to 3 models) &nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;
        Enter your prompt in Korean or English &nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;
        Click the **Generate** button
        """
    )

    with gr.Row(elem_id="lora_gallery", equal_height=True):
        gallery = gr.Gallery(
            value=[(item["image"], item["title"]) for item in loras],
            label="LoRA Explorer Gallery",
            columns=11,
            elem_id="gallery",
            height=800,
            object_fit="cover",
            show_label=True,
            allow_preview=False,
            show_share_button=False,
            container=True,
            preview=False
        )

    with gr.Tab(label="Generate"):
        # Prompt and Generate Button
        with gr.Row():
            with gr.Column(scale=3):
                prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
            with gr.Column(scale=1):
                generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])

        # LoRA Selection Area
        with gr.Row(elem_id="loaded_loras"):
            # Randomize Button
            with gr.Column(scale=1, min_width=25):
                randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
            
            # LoRA 1
            with gr.Column(scale=8):
                with gr.Row():
                    with gr.Column(scale=0, min_width=50):
                        lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
                    with gr.Column(scale=3, min_width=100):
                        selected_info_1 = gr.Markdown("Select a LoRA 1")
                    with gr.Column(scale=5, min_width=50):
                        lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
                with gr.Row():
                    remove_button_1 = gr.Button("Remove", size="sm")

            # LoRA 2
            with gr.Column(scale=8):
                with gr.Row():
                    with gr.Column(scale=0, min_width=50):
                        lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
                    with gr.Column(scale=3, min_width=100):
                        selected_info_2 = gr.Markdown("Select a LoRA 2")
                    with gr.Column(scale=5, min_width=50):
                        lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
                with gr.Row():
                    remove_button_2 = gr.Button("Remove", size="sm")

            # LoRA 3
            with gr.Column(scale=8):
                with gr.Row():
                    with gr.Column(scale=0, min_width=50):
                        lora_image_3 = gr.Image(label="LoRA 3 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
                    with gr.Column(scale=3, min_width=100):
                        selected_info_3 = gr.Markdown("Select a LoRA 3")
                    with gr.Column(scale=5, min_width=50):
                        lora_scale_3 = gr.Slider(label="LoRA 3 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
                with gr.Row():
                    remove_button_3 = gr.Button("Remove", size="sm")

        # Result and Progress Area
        with gr.Column(elem_id="result_column"):
            progress_bar = gr.Markdown(elem_id="progress", visible=False)
            with gr.Column(elem_id="result_box"):
                result = gr.Image(
                    label="Generated Image", 
                    interactive=False,
                    elem_classes=["generated-image"],
                    container=True,
                    elem_id="result_image",
                    width="100%"
                )
            with gr.Accordion("History", open=False):
                history_gallery = gr.Gallery(
                    label="History", 
                    columns=6, 
                    object_fit="contain", 
                    interactive=False,
                    elem_classes=["history-gallery"]
                )

        # Advanced Settings
        with gr.Row():
            with gr.Accordion("Advanced Settings", open=False):
                with gr.Row():
                    input_image = gr.Image(label="Input Image", type="filepath")
                    image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
                with gr.Column():
                    with gr.Row():
                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
                        steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
                    with gr.Row():
                        width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                        height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
                    with gr.Row():
                        randomize_seed = gr.Checkbox(True, label="Randomize Seed")
                        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)

        # Custom LoRA Section
        with gr.Column():
            with gr.Group():
                with gr.Row(elem_id="custom_lora_structure"):
                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3, min_width=150)
                    add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
                remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
                gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")

        # Event Handlers
        gallery.select(
            update_selection,
            inputs=[selected_indices, loras_state, width, height],
            outputs=[prompt, selected_info_1, selected_info_2, selected_info_3, selected_indices, 
                lora_scale_1, lora_scale_2, lora_scale_3, width, height, 
                lora_image_1, lora_image_2, lora_image_3]
        )

        remove_button_1.click(
            remove_lora_1,
            inputs=[selected_indices, loras_state],
            outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, 
                lora_scale_1, lora_scale_2, lora_scale_3, 
                lora_image_1, lora_image_2, lora_image_3]
        )

        remove_button_2.click(
            remove_lora_2,
            inputs=[selected_indices, loras_state],
            outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, 
                lora_scale_1, lora_scale_2, lora_scale_3, 
                lora_image_1, lora_image_2, lora_image_3]
        )

        remove_button_3.click(
            remove_lora_3,
            inputs=[selected_indices, loras_state],
            outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, 
                lora_scale_1, lora_scale_2, lora_scale_3, 
                lora_image_1, lora_image_2, lora_image_3]
        )

        randomize_button.click(
            randomize_loras,
            inputs=[selected_indices, loras_state],
            outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, 
                lora_scale_1, lora_scale_2, lora_scale_3, 
                lora_image_1, lora_image_2, lora_image_3, prompt]
        )

        add_custom_lora_button.click(
            add_custom_lora,
            inputs=[custom_lora, selected_indices, loras_state],
            outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_info_3, 
                selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, 
                lora_image_1, lora_image_2, lora_image_3, remove_custom_lora_button]
        )

        remove_custom_lora_button.click(
            remove_custom_lora,
            inputs=[selected_indices, loras_state],
            outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_info_3, 
                selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, 
                lora_image_1, lora_image_2, lora_image_3, remove_custom_lora_button]
        )

        gr.on(
            triggers=[generate_button.click, prompt.submit],
            fn=run_lora,
            inputs=[prompt, input_image, image_strength, cfg_scale, steps, 
                selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, 
                randomize_seed, seed, width, height, loras_state],
            outputs=[result, seed, progress_bar]
        ).then(
            fn=lambda x, history: update_history(x, history) if x is not None else history,
            inputs=[result, history_gallery],
            outputs=history_gallery
        )

if __name__ == "__main__":
    app.queue(max_size=20)
    app.launch(debug=True)