##############################
# ===== Standard Imports =====
##############################
import os
import sys
import time
import random
import json
from math import floor
from typing import Any, Dict, List, Optional, Union

# Local import for default LoRA list (if available)
try:
    from flux_app.lora import loras
except ImportError:
    loras = [
        {"image": "placeholder.jpg", "title": "Placeholder LoRA", "repo": "placeholder/repo", "weights": None, "trigger_word": ""}
    ]

import torch
import numpy as np
import requests
from PIL import Image
import spaces

# Diffusers imports
from diffusers import (
    DiffusionPipeline,
    AutoencoderTiny,
    AutoencoderKL,
    AutoPipelineForImage2Image,
)
from diffusers.utils import load_image

# Hugging Face Hub
from huggingface_hub import ModelCard, HfFileSystem

# Gradio (UI)
import gradio as gr

##############################
# ===== config.py =====
##############################
DTYPE = torch.bfloat16
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
BASE_MODEL = "black-forest-labs/FLUX.1-dev"
TAEF1_MODEL = "madebyollin/taef1"
MAX_SEED = 2**32 - 1

##############################
# ===== utilities.py =====
##############################
def calculate_shift(
    image_seq_len,
    base_seq_len: int = 256,
    max_seq_len: int = 4096,
    base_shift: float = 0.5,
    max_shift: float = 1.16,
):
    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
    b = base_shift - m * base_seq_len
    mu = image_seq_len * m + b
    return mu

def retrieve_timesteps(
    scheduler,
    num_inference_steps: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    timesteps: Optional[List[int]] = None,
    sigmas: Optional[List[float]] = None,
    **kwargs,
):
    if timesteps is not None and sigmas is not None:
        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
    if timesteps is not None:
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    elif sigmas is not None:
        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps

def load_image_from_path(image_path: str):
    """Loads an image from a given file path."""
    return load_image(image_path)

def randomize_seed_if_needed(randomize_seed: bool, seed: int, max_seed: int) -> int:
    """Randomizes the seed if requested."""
    if randomize_seed:
        return random.randint(0, max_seed)
    return seed

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")

##############################
# ===== enhance.py =====
##############################
def generate(message, max_new_tokens=256, temperature=0.9, top_p=0.95, repetition_penalty=1.0):
    """
    Generates an enhanced prompt using a streaming Hugging Face API.
    Enhances the given prompt under 100 words without changing its essence.
    """
    SYSTEM_PROMPT = (
        "You are a prompt enhancer and your work is to enhance the given prompt under 100 words "
        "without changing the essence, only write the enhanced prompt and nothing else."
    )
    timestamp = time.time()
    formatted_prompt = (
        f"<s>[INST] SYSTEM: {SYSTEM_PROMPT} [/INST]"
        f"[INST] {message} {timestamp} [/INST]"
    )
    
    api_url = "https://ruslanmv-hf-llm-api.hf.space/api/v1/chat/completions"
    headers = {"Content-Type": "application/json"}
    
    payload = {
        "model": "mixtral-8x7b",
        "messages": [{"role": "user", "content": formatted_prompt}],
        "temperature": temperature,
        "top_p": top_p,
        "max_tokens": max_new_tokens,
        "use_cache": False,
        "stream": True
    }
    
    try:
        response = requests.post(api_url, headers=headers, json=payload, stream=True)
        response.raise_for_status()
        full_output = ""
        
        for line in response.iter_lines():
            if not line:
                continue
            decoded_line = line.decode("utf-8").strip()
            if decoded_line.startswith("data:"):
                decoded_line = decoded_line[len("data:"):].strip()
            if decoded_line == "[DONE]":
                break
            try:
                json_data = json.loads(decoded_line)
                for choice in json_data.get("choices", []):
                    delta = choice.get("delta", {})
                    content = delta.get("content", "")
                    full_output += content
                    yield full_output
                    if choice.get("finish_reason") == "stop":
                        return
            except json.JSONDecodeError:
                continue
    except requests.exceptions.RequestException as e:
        yield f"Error during generation: {str(e)}"

##############################
# ===== lora_handling.py =====
##############################
# A default list of LoRAs for the UI
loras = [
    {"image": "placeholder.jpg", "title": "Placeholder LoRA", "repo": "placeholder/repo", "weights": None, "trigger_word": ""}
]

@torch.inference_mode()
def flux_pipe_call_that_returns_an_iterable_of_images(
    self,
    prompt: Union[str, List[str]] = None,
    prompt_2: Optional[Union[str, List[str]]] = None,
    height: Optional[int] = None,
    width: Optional[int] = None,
    num_inference_steps: int = 28,
    timesteps: List[int] = None,
    guidance_scale: float = 3.5,
    num_images_per_prompt: Optional[int] = 1,
    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
    latents: Optional[torch.FloatTensor] = None,
    prompt_embeds: Optional[torch.FloatTensor] = None,
    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = True,
    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    max_sequence_length: int = 512,
    good_vae: Optional[Any] = None,
):
    height = height or self.default_sample_size * self.vae_scale_factor
    width = width or self.default_sample_size * self.vae_scale_factor
    
    self.check_inputs(
        prompt,
        prompt_2,
        height,
        width,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        max_sequence_length=max_sequence_length,
    )

    self._guidance_scale = guidance_scale
    self._joint_attention_kwargs = joint_attention_kwargs
    self._interrupt = False

    batch_size = 1 if isinstance(prompt, str) else len(prompt)
    device = self._execution_device

    lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
        prompt=prompt,
        prompt_2=prompt_2,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        device=device,
        num_images_per_prompt=num_images_per_prompt,
        max_sequence_length=max_sequence_length,
        lora_scale=lora_scale,
    )
    
    num_channels_latents = self.transformer.config.in_channels // 4
    latents, latent_image_ids = self.prepare_latents(
        batch_size * num_images_per_prompt,
        num_channels_latents,
        height,
        width,
        prompt_embeds.dtype,
        device,
        generator,
        latents,
    )
    
    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
    image_seq_len = latents.shape[1]
    mu = calculate_shift(
        image_seq_len,
        self.scheduler.config.base_image_seq_len,
        self.scheduler.config.max_image_seq_len,
        self.scheduler.config.base_shift,
        self.scheduler.config.max_shift,
    )
    timesteps, num_inference_steps = retrieve_timesteps(
        self.scheduler,
        num_inference_steps,
        device,
        timesteps,
        sigmas,
        mu=mu,
    )
    self._num_timesteps = len(timesteps)

    guidance = (torch.full([1], guidance_scale, device=device, dtype=torch.float32)
                .expand(latents.shape[0])
                if self.transformer.config.guidance_embeds else None)

    for i, t in enumerate(timesteps):
        if self.interrupt:
            continue

        timestep = t.expand(latents.shape[0]).to(latents.dtype)

        noise_pred = self.transformer(
            hidden_states=latents,
            timestep=timestep / 1000,
            guidance=guidance,
            pooled_projections=pooled_prompt_embeds,
            encoder_hidden_states=prompt_embeds,
            txt_ids=text_ids,
            img_ids=latent_image_ids,
            joint_attention_kwargs=self.joint_attention_kwargs,
            return_dict=False,
        )[0]

        latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
        latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
        image = self.vae.decode(latents_for_image, return_dict=False)[0]
        yield self.image_processor.postprocess(image, output_type=output_type)[0]
        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
        torch.cuda.empty_cache()
        
    latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
    latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
    image = good_vae.decode(latents, return_dict=False)[0]
    self.maybe_free_model_hooks()
    torch.cuda.empty_cache()
    yield self.image_processor.postprocess(image, output_type=output_type)[0]

def get_huggingface_safetensors(link: str) -> tuple:
    split_link = link.split("/")
    if len(split_link) == 2:
        model_card = ModelCard.load(link)
        base_model = model_card.data.get("base_model")
        print(base_model)

        if base_model not in ("black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"):
            raise Exception("Flux LoRA Not Found!")

        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()
        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]}"
            return split_link[1], link, safetensors_name, trigger_word, image_url
        except Exception as e:
            print(e)
            raise Exception("You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
    else:
        raise Exception("You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")

def check_custom_model(link: str) -> tuple:
    if link.startswith("https://"):
        if link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co"):
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
    return get_huggingface_safetensors(link)

def create_lora_card(title: str, repo: str, trigger_word: str, image: str) -> str:
    trigger_word_info = (
        f"Using: <code><b>{trigger_word}</b></code> as the trigger word"
        if trigger_word
        else "No trigger word found. If there's a trigger word, include it in your prompt"
    )
    return f'''
    <div class="custom_lora_card">
        <span>Loaded custom LoRA:</span>
        <div class="card_internal">
            <img src="{image}" />
            <div>
                <h3>{title}</h3>
                <small>{trigger_word_info}<br></small>
            </div>
        </div>
    </div>
    '''

def add_custom_lora(custom_lora: str, loras_list: list) -> tuple:
    if custom_lora:
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            card = create_lora_card(title, repo, trigger_word, image)

            existing_item_index = next((index for (index, item) in enumerate(loras_list) if item['repo'] == repo), None)
            if existing_item_index is None:
                new_item = {
                    "image": image,
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(new_item)
                loras_list.append(new_item)
                existing_item_index = len(loras_list) - 1

            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word

        except Exception as e:
            print(f"Error loading LoRA: {e}")
            return gr.update(visible=True, value="Invalid LoRA"), gr.update(visible=False), gr.update(), "", None, ""
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

def remove_custom_lora() -> tuple:
    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

def prepare_prompt(prompt: str, selected_index: Optional[int], loras_list: list) -> str:
    if selected_index is None:
        raise gr.Error("You must select a LoRA before proceeding.🧨")

    selected_lora = loras_list[selected_index]
    trigger_word = selected_lora.get("trigger_word")
    if trigger_word:
        trigger_position = selected_lora.get("trigger_position", "append")
        if trigger_position == "prepend":
            prompt_mash = f"{trigger_word} {prompt}"
        else:
            prompt_mash = f"{prompt} {trigger_word}"
    else:
        prompt_mash = prompt
    return prompt_mash

def unload_lora_weights(pipe, pipe_i2i):
    if pipe is not None:
        pipe.unload_lora_weights()
    if pipe_i2i is not None:
        pipe_i2i.unload_lora_weights()

def load_lora_weights_into_pipeline(pipe_to_use, lora_path: str, weight_name: Optional[str]):
    pipe_to_use.load_lora_weights(
        lora_path,
        weight_name=weight_name,
        low_cpu_mem_usage=True
    )

def update_selection(evt: gr.SelectData, width, height, loras_list):
    selected_lora = loras_list[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✅"
    if "aspect" in selected_lora:
        if selected_lora["aspect"] == "portrait":
            width = 768
            height = 1024
        elif selected_lora["aspect"] == "landscape":
            width = 1024
            height = 768
        else:
            width = 1024
            height = 1024
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
    )

##############################
# ===== backend.py =====
##############################
class ModelManager:
    def __init__(self, hf_token=None):
        self.hf_token = hf_token
        self.pipe = None
        self.pipe_i2i = None
        self.good_vae = None
        self.taef1 = None
        self.initialize_models()

    def initialize_models(self):
        """Initializes the diffusion pipelines and autoencoders."""
        self.taef1 = AutoencoderTiny.from_pretrained(TAEF1_MODEL, torch_dtype=DTYPE).to(DEVICE)
        self.good_vae = AutoencoderKL.from_pretrained(BASE_MODEL, subfolder="vae", torch_dtype=DTYPE).to(DEVICE)
        # Optionally, pass use_auth_token=self.hf_token if needed.
        self.pipe = DiffusionPipeline.from_pretrained(BASE_MODEL, torch_dtype=DTYPE, vae=self.taef1)
        self.pipe = self.pipe.to(DEVICE)
        self.pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
            BASE_MODEL,
            vae=self.good_vae,
            transformer=self.pipe.transformer,
            text_encoder=self.pipe.text_encoder,
            tokenizer=self.pipe.tokenizer,
            text_encoder_2=self.pipe.text_encoder_2,
            tokenizer_2=self.pipe.tokenizer_2,
            torch_dtype=DTYPE,
        ).to(DEVICE)
        # Instead of binding to the instance (which fails due to __slots__),
        # bind the custom method to the pipeline’s class.
        self.pipe.__class__.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images
    
    @spaces.GPU(duration=100)
    def generate_image(self, prompt_mash, steps, seed, cfg_scale, width, height, lora_scale):
        """Generates an image using the text-to-image pipeline."""
        self.pipe.to(DEVICE)
        generator = torch.Generator(device=DEVICE).manual_seed(seed)
        with calculateDuration("Generating image"):
            for img in self.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": lora_scale},
                output_type="pil",
                good_vae=self.good_vae,
            ):
                yield img

    def generate_image_to_image(self, prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
        """Generates an image using the image-to-image pipeline."""
        generator = torch.Generator(device=DEVICE).manual_seed(seed)
        self.pipe_i2i.to(DEVICE)
        image_input = load_image_from_path(image_input_path)
        with calculateDuration("Generating image to image"):
            final_image = self.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": lora_scale},
                output_type="pil",
            ).images[0]
            return final_image

##############################
# ===== frontend.py =====
##############################
class Frontend:
    def __init__(self, model_manager: ModelManager):
        self.model_manager = model_manager
        self.loras = loras  # Use the default LoRA list defined above.
        self.load_initial_loras()
        self.css = self.define_css()

    def define_css(self):
        # Clean and professional CSS styling.
        return '''
        /* Title Styling */
        #title {
            text-align: center;
            margin-bottom: 20px;
        }
        #title h1 {
            font-size: 2.5rem;
            margin: 0;
            color: #333;
        }
        /* Button and Column Styling */
        #gen_btn {
            width: 100%;
            padding: 12px;
            font-weight: bold;
            border-radius: 5px;
        }
        #gen_column {
            display: flex;
            align-items: center;
            justify-content: center;
        }
        /* Gallery and List Styling */
        #gallery .grid-wrap {
            margin-top: 15px;
        }
        #lora_list {
            background-color: #f5f5f5;
            padding: 10px;
            border-radius: 4px;
            font-size: 0.9rem;
        }
        .card_internal {
            display: flex;
            align-items: center;
            height: 100px;
            margin-top: 10px;
        }
        .card_internal img {
            margin-right: 10px;
        }
        .styler {
            --form-gap-width: 0px !important;
        }
        /* Progress Bar Styling */
        .progress-container {
            width: 100%;
            height: 20px;
            background-color: #e0e0e0;
            border-radius: 10px;
            overflow: hidden;
            margin-bottom: 20px;
        }
        .progress-bar {
            height: 100%;
            background-color: #4f46e5;
            transition: width 0.3s ease-in-out;
            width: calc(var(--current) / var(--total) * 100%);
        }
        '''

    def load_initial_loras(self):
        try:
            from lora import loras as loras_list
            self.loras = loras_list
        except ImportError:
            print("Warning: lora.py not found, using placeholder LoRAs.")
            pass

    @spaces.GPU(duration=100)
    def run_lora(self, prompt, image_input, image_strength, cfg_scale, steps, selected_index,
                 randomize_seed, seed, width, height, lora_scale, use_enhancer,
                 progress=gr.Progress(track_tqdm=True)):
        seed = randomize_seed_if_needed(randomize_seed, seed, MAX_SEED)
        # Prepare the prompt using the selected LoRA trigger word.
        prompt_mash = prepare_prompt(prompt, selected_index, self.loras)
        enhanced_text = ""
        
        # Optionally enhance the prompt.
        if use_enhancer:
            for enhanced_chunk in generate(prompt_mash):
                enhanced_text = enhanced_chunk
                yield None, seed, gr.update(visible=False), enhanced_text
            prompt_mash = enhanced_text
        else:
            enhanced_text = ""
        
        selected_lora = self.loras[selected_index]
        unload_lora_weights(self.model_manager.pipe, self.model_manager.pipe_i2i)
        pipe_to_use = self.model_manager.pipe_i2i if image_input is not None else self.model_manager.pipe
        load_lora_weights_into_pipeline(pipe_to_use, selected_lora["repo"], selected_lora.get("weights"))

        if image_input is not None:
            final_image = self.model_manager.generate_image_to_image(
                prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed
            )
            yield final_image, seed, gr.update(visible=False), enhanced_text
        else:
            image_generator = self.model_manager.generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale)
            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), enhanced_text
            yield final_image, seed, gr.update(value=progress_bar, visible=False), enhanced_text

    def create_ui(self):
        with gr.Blocks(theme=gr.themes.Base(), css=self.css, title="Flux LoRA Generation") as app:
            title = gr.HTML(
                """<h1>Flux LoRA Generation</h1>""",
                elem_id="title",
            )
            selected_index = gr.State(None)

            with gr.Row():
                with gr.Column(scale=3):
                    prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Choose the LoRA and type the prompt")
                with gr.Column(scale=1, elem_id="gen_column"):
                    generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
            with gr.Row():
                with gr.Column():
                    selected_info = gr.Markdown("")
                    gallery = gr.Gallery(
                        [(item["image"], item["title"]) for item in self.loras],
                        label="LoRA Collection",
                        allow_preview=False,
                        columns=3,
                        elem_id="gallery",
                        show_share_button=False
                    )
                    with gr.Group():
                        custom_lora = gr.Textbox(label="Enter Custom LoRA", placeholder="prithivMLmods/Canopus-LoRA-Flux-Anime")
                        gr.Markdown("[Check the list of FLUX LoRA's](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
                    custom_lora_info = gr.HTML(visible=False)
                    custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
                with gr.Column():
                    progress_bar = gr.Markdown(elem_id="progress", visible=False)
                    result = gr.Image(label="Generated Image")

            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)
                            lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95)
                        with gr.Row():
                            use_enhancer = gr.Checkbox(value=False, label="Use Prompt Enhancer")
                            show_enhanced_prompt = gr.Checkbox(value=False, label="Display Enhanced Prompt")
                    enhanced_prompt_box = gr.Textbox(label="Enhanced Prompt", visible=False)

            gallery.select(
                update_selection,
                inputs=[width, height, gr.State(self.loras)],
                outputs=[prompt, selected_info, selected_index, width, height]
            )
            custom_lora.input(
                add_custom_lora,
                inputs=[custom_lora, gr.State(self.loras)],
                outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
            )
            custom_lora_button.click(
                remove_custom_lora,
                outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
            )

            show_enhanced_prompt.change(fn=lambda show: gr.update(visible=show),
                                        inputs=show_enhanced_prompt,
                                        outputs=enhanced_prompt_box)

            gr.on(
                triggers=[generate_button.click, prompt.submit],
                fn=self.run_lora,
                inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index,
                        randomize_seed, seed, width, height, lora_scale, use_enhancer],
                outputs=[result, seed, progress_bar, enhanced_prompt_box]
            )

            with gr.Row():
                gr.HTML("<div style='text-align:center; font-size:0.9em; margin-top:20px;'>Credits: <a href='https://ruslanmv.com' target='_blank'>ruslanmv.com</a></div>")
            
            return app

##############################
# ===== Main app.py =====
##############################
if __name__ == "__main__":
    # Get the Hugging Face token from the environment.
    hf_token = os.environ.get("HF_TOKEN")
    if not hf_token:
        raise ValueError("Hugging Face token (HF_TOKEN) not found in environment variables. Please set it.")
    model_manager = ModelManager(hf_token=hf_token)
    frontend = Frontend(model_manager)
    app = frontend.create_ui()
    app.queue()
    # Set share=True to create a public link if desired.
    app.launch(share=False, debug=True)