app.py

import os
import time
import random
import logging
from gradio.blocks import postprocess_update_dict
import numpy as np
from typing import Any, Dict, List, Optional, Union

import torch
from PIL import Image
import gradio as gr
from tempfile import NamedTemporaryFile

from diffusers import (
    DiffusionPipeline,
    AutoencoderTiny,
    AutoencoderKL,
    AutoPipelineForImage2Image,
    FluxPipeline,
    FlowMatchEulerDiscreteScheduler,
    DPMSolverMultistepScheduler)

from huggingface_hub import (
    hf_hub_download,
    HfFileSystem,
    ModelCard,
    snapshot_download)

from diffusers.utils import load_image

from modules.version_info import (
    versions_html,
    #initialize_cuda,
    #release_torch_resources,
    #get_torch_info
)
from modules.image_utils import (
    change_color,
    open_image,
    build_prerendered_images_by_quality,
    upscale_image,
    # lerp_imagemath,
    # shrink_and_paste_on_blank,
    show_lut,
    apply_lut_to_image_path,
    multiply_and_blend_images,
    alpha_composite_with_control,
    resize_and_crop_image,
    convert_to_rgba_png,
    get_image_from_dict
)
from modules.constants import (
    LORA_DETAILS, LORAS as loras, MODELS,
    default_lut_example_img, 
    lut_files, 
    MAX_SEED, 
    # lut_folder,cards, 
    # cards_alternating, 
    # card_colors, 
    # card_colors_alternating,
    pre_rendered_maps_paths,
    PROMPTS,
    NEGATIVE_PROMPTS,
    TARGET_SIZE,
    temp_files,
    load_env_vars,
    dotenv_path
)
# from modules.excluded_colors import (
#     add_color,
#     delete_color,
#     build_dataframe,
#     on_input,
#     excluded_color_list,
#     on_color_display_select
# )
from modules.misc import (
    get_filename,
    convert_ratio_to_dimensions,
    update_dimensions_on_ratio
)
from modules.lora_details import (
    approximate_token_count,
    split_prompt_precisely,
    upd_prompt_notes_by_index,
    get_trigger_words_by_index
)

import spaces

input_image_palette = []
current_prerendered_image = gr.State("./images/Beeuty-1.png")
user_info = {
    "username": "guest",
    "session_hash": None,
    "headers": None,
    "client": None,
    "query_params": None,
    "path_params": None,
    "level" : 0
}
# Define a function to handle the login button click and retrieve user information.
def handle_login(request: gr.Request):
    # Extract user information from the request
    user_info = {
        "username": request.username,
        "session_hash": request.session_hash,
        "headers": dict(request.headers),
        "client": request.client,
        "query_params": dict(request.query_params),
        "path_params": dict(request.path_params),
        "level" : (0 if request.username == "guest" else 2)
    }
    return user_info, gr.update(logout_value=f"Logout {user_info['username']} ({user_info['level']})", value=f"Login {user_info['username']} ({user_info['level']})")
#---if workspace = local or colab---

# Authenticate with Hugging Face
# from huggingface_hub import login

# Log in to Hugging Face using the provided token
# hf_token = 'hf-token-authentication'
# login(hf_token)

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

# FLUX pipeline
@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,
    negative_prompt: 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)
        print(f"Step {i + 1}/{num_inference_steps} - Timestep: {timestep.item()}\n")

        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]

#--------------------------------------------------Model Initialization-----------------------------------------------------------------------------------------#

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "black-forest-labs/FLUX.1-dev"

#TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.#
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)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
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
                                                     )

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 update_selection(evt: gr.SelectData, width, height, aspect_ratio):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    new_aspect_ratio = aspect_ratio
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✅"
    # aspect will now use ratios if implemented, like 16:9, 4:3, 1:1, etc.
    if "aspect" in selected_lora:
        try:
            new_aspect_ratio = selected_lora["aspect"]
            width, height = update_dimensions_on_ratio(new_aspect_ratio, height)
        except Exception as e:
            print(f"\nError in update selection aspect ratios:{e}\nSkipping")
            new_aspect_ratio = aspect_ratio
            width = width
            height = height
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
        new_aspect_ratio,
        upd_prompt_notes_by_index(evt.index)
    )

@spaces.GPU(duration=120,progress=gr.Progress(track_tqdm=True))
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    flash_attention_enabled = torch.backends.cuda.flash_sdp_enabled()
    if flash_attention_enabled:
        pipe.attn_implementation="flash_attention_2"
    # Compile UNet
    #pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead")
    pipe.vae.enable_tiling()  # For larger resolutions if needed

    # Disable unnecessary features
    pipe.safety_checker = None
    print(f"\nGenerating image with prompt: {prompt_mash}\n")
    approx_tokens= approximate_token_count(prompt_mash)
    if approx_tokens > 76:
        print(f"\nSplitting prompt due to length: {approx_tokens}\n")
        prompt, prompt2 = split_prompt_precisely(prompt_mash)
    else:
        prompt = prompt_mash
        prompt2 = None
    with calculateDuration("Generating image"):
        # Generate image
        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt,
            prompt_2=prompt2,
            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=good_vae,
        ):
            yield img

def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed, progress):
    generator = torch.Generator(device="cuda").manual_seed(seed)
    pipe_i2i.to("cuda")
    flash_attention_enabled = torch.backends.cuda.flash_sdp_enabled()
    if flash_attention_enabled:
        pipe_i2i.attn_implementation="flash_attention_2"
    # Compile UNet
    #pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead")
    pipe.vae.enable_tiling()  # For larger resolutions if needed

    # Disable unnecessary features
    pipe.safety_checker = None
    image_input = open_image(image_input_path)
    print(f"\nGenerating image with prompt: {prompt_mash} and {image_input_path}\n")
    approx_tokens= approximate_token_count(prompt_mash)
    if approx_tokens > 76:
        print(f"\nSplitting prompt due to length: {approx_tokens}\n")
        prompt, prompt2 = split_prompt_precisely(prompt_mash)
    else:
        prompt = prompt_mash
        prompt2 = None
    final_image = pipe_i2i(
        prompt=prompt,
        prompt_2=prompt2,
        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

@spaces.GPU(duration=140)
def run_lora(prompt, map_option, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, enlarge, use_conditioned_image=False, progress=gr.Progress(track_tqdm=True)):
    if selected_index is None:
        raise gr.Error("You must select a LoRA before proceeding.🧨")
    print(f"input Image: {image_input}\n")
    # handle selecting a conditioned image from the gallery
    global current_prerendered_image
    conditioned_image=None
    if use_conditioned_image:
        print(f"Conditioned path: {current_prerendered_image.value}.. converting to RGB\n")
        # ensure the conditioned image is an image and not a string, cannot use RGBA
        if isinstance(current_prerendered_image.value, str):
            conditioned_image = open_image(current_prerendered_image.value).convert("RGB")
            image_input = resize_and_crop_image(conditioned_image, width, height)
            print(f"Conditioned Image: {image_input.size}.. converted to RGB and resized\n")
    if map_option != "Prompt":
        prompt = PROMPTS[map_option]
        # negative_prompt = NEGATIVE_PROMPTS.get(map_option, "")

    selected_lora = loras[selected_index]
    lora_path = selected_lora["repo"]
    trigger_word = selected_lora["trigger_word"]
    if(trigger_word):
        if "trigger_position" in selected_lora:
            if selected_lora["trigger_position"] == "prepend":
                prompt_mash = f"{trigger_word} {prompt}"
            else:
                prompt_mash = f"{prompt} {trigger_word}"
        else:
            prompt_mash = f"{trigger_word} {prompt}"
    else:
        prompt_mash = prompt

    with calculateDuration("Unloading LoRA"):
        pipe.unload_lora_weights()
        pipe_i2i.unload_lora_weights()
        
    #LoRA weights flow
    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
        pipe_to_use = pipe_i2i if image_input is not None else pipe
        weight_name = selected_lora.get("weights", None)
        
        pipe_to_use.load_lora_weights(
            lora_path, 
            weight_name=weight_name, 
            low_cpu_mem_usage=True
        )
            
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
            
    if(image_input is not None):
        print(f"\nGenerating image to image with seed: {seed}\n")
        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed, progress)
        if enlarge:
            upscaled_image = upscale_image(final_image, max(1.0,min((TARGET_SIZE[0]/width),(TARGET_SIZE[1]/height))))
            # Save the upscaled image to a temporary file
            with NamedTemporaryFile(delete=False, suffix=".png") as tmp_upscaled:
                upscaled_image.save(tmp_upscaled.name, format="PNG")
                temp_files.append(tmp_upscaled.name)
                print(f"Upscaled image saved to {tmp_upscaled.name}")
                final_image = tmp_upscaled.name
        yield final_image, seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, 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 enlarge:
            upscaled_image = upscale_image(final_image, max(1.0,min((TARGET_SIZE[0]/width),(TARGET_SIZE[1]/height))))
            # Save the upscaled image to a temporary file
            with NamedTemporaryFile(delete=False, suffix=".png") as tmp_upscaled:
                upscaled_image.save(tmp_upscaled.name, format="PNG")
                temp_files.append(tmp_upscaled.name)
                print(f"Upscaled image saved to {tmp_upscaled.name}")
                final_image = tmp_upscaled.name
        yield final_image, seed, gr.update(value=progress_bar, visible=False)
        
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(base_model)
      
            #Allows Both
            if base_model not in MODELS:
            #if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
                raise Exception("Flux LoRA Not Found!")
                
            # Only allow "black-forest-labs/FLUX.1-dev"
            #if base_model != "black-forest-labs/FLUX.1-dev":
                #raise Exception("Only FLUX.1-dev is supported, other LoRA models are not allowed!")
                
            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]}"
            except Exception as e:
              print(e)
              gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
              raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
            return split_link[1], link, safetensors_name, trigger_word, image_url

def check_custom_model(link):
    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])
    else: 
        return get_huggingface_safetensors(link)

def add_custom_lora(custom_lora):
    global loras
    if(custom_lora):
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            card = f'''
            <div class="custom_lora_card">
              <span>Loaded custom LoRA:</span>
              <div class="card_internal">
                <img src="{image}" />
                <div>
                    <h3>{title}</h3>
                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
                </div>
              </div>
            </div>
            '''
            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
            if(not existing_item_index):
                new_item = {
                    "image": image,
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(new_item)
                existing_item_index = len(loras)
                loras.append(new_item)
        
            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:
            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX 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():
    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

def on_prerendered_gallery_selection(event_data: gr.SelectData):
    global current_prerendered_image
    selected_index = event_data.index
    selected_image = pre_rendered_maps_paths[selected_index]
    print(f"Gallery Image Selected: {selected_image}\n")
    current_prerendered_image.value = selected_image
    return current_prerendered_image

def update_prompt_visibility(map_option):
      is_visible = (map_option == "Prompt")
      return (
          gr.update(visible=is_visible),
          gr.update(visible=is_visible),
          gr.update(visible=is_visible)
      )
def composite_with_control_sync(input_image, sketch_image, slider_value):
    # Load the images using open_image() if they are provided as file paths.
    in_img = open_image(input_image) if isinstance(input_image, str) else input_image
    sk_img_path, _ = get_image_from_dict(sketch_image)
    sk_img = open_image(sk_img_path)

    # Resize sketch image if dimensions don't match input image.
    if in_img.size != sk_img.size:
        sk_img = sk_img.resize(in_img.size, Image.LANCZOS)

    # Now composite using the original alpha_composite_with_control function.
    result_img = alpha_composite_with_control(in_img, sk_img, slider_value)
    return result_img

def replace_input_with_sketch_image(sketch_image):
    print(f"Sketch Image: {sketch_image}\n")
    sketch, is_dict = get_image_from_dict(sketch_image)
    return sketch

def on_input_image_change(image_path):
    if image_path is None:
        gr.Warning("Please upload an Input Image to get started.")
        return None, gr.update()
    img, img_path = convert_to_rgba_png(image_path)
    with Image.open(img_path) as pil_img:
        width, height = pil_img.size
    return [img_path, gr.update(width=width, height=height)]

def update_sketch_dimensions(input_image, sketch_image):
    # Load the images using open_image() if they are provided as file paths.
    in_img = open_image(input_image) if isinstance(input_image, str) else input_image
    sk_img_path, _ = get_image_from_dict(sketch_image)
    sk_img = open_image(sk_img_path)
    # Resize sketch image if dimensions don't match input image.
    if in_img.size != sk_img.size:
        sk_img = sk_img.resize(in_img.size, Image.LANCZOS)
    return sk_img

@spaces.GPU()
def getVersions():
    return versions_html()
run_lora.zerogpu = True
gr.set_static_paths(paths=["images/","images/images","images/prerendered","LUT/","fonts/", "assets/"])
title = "Hex Game Maker"
with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty', delete_cache=(43200, 43200), head_paths="head.htm") as app:
    with gr.Row():
        gr.Markdown("""
        # Hex Game Maker Development Features
        ## This project includes features that did not make it into the main project! ⬢""", elem_classes="intro")
    with gr.Row():
        with gr.Accordion("Welcome to Hex Game Maker, the ultimate tool for transforming your images into stunning hexagon grid artworks. Whether you're a tabletop game enthusiast, a digital artist, or someone who loves unique patterns, Hex Game Maker has something for you.", open=False, elem_classes="intro"):
            gr.Markdown ("""

            ## Drop an image into the Input Image and get started!

        

            ## What is Hex Game Maker?
            Hex Game Maker is a web-based application that allows you to apply a hexagon grid overlay to any image. You can customize the size, color, and opacity of the hexagons, as well as the background and border colors. The result is a visually striking image that looks like it was made from hexagonal tiles!

            ### What Can You Do?
            - **Generate Hexagon Grids:** Create beautiful hexagon grid overlays on any image with fully customizable parameters.
            - **AI-Powered Image Generation:** Use advanced AI models to generate images based on your prompts and apply hexagon grids to them.
            - **Color Exclusion:** Select and exclude specific colors from your hexagon grid for a cleaner and more refined look.
            - **Interactive Customization:** Adjust hexagon size, border size, rotation, background color, and more in real-time.
            - **Depth and 3D Model Generation:** Generate depth maps and 3D models from your images for enhanced visualization.
            - **Image Filter [Look-Up Table (LUT)] Application:** Apply filters (LUTs) to your images for color grading and enhancement.
            - **Pre-rendered Maps:** Access a library of pre-rendered hexagon maps for quick and easy customization.
            - **Add Margins:** Add customizable margins around your images for a polished finish.

            ### Why You'll Love It
            - **Fun and Easy to Use:** With an intuitive interface and real-time previews, creating hexagon grids has never been this fun!
            - **Endless Creativity:** Unleash your creativity with endless customization options and see your images transform in unique ways.
            - **Hexagon-Inspired Theme:** Enjoy a delightful yellow and purple theme inspired by hexagons! ⬢
            - **Advanced AI Models:** Leverage advanced AI models and LoRA weights for high-quality image generation and customization.

            ### Get Started
            1. **Upload or Generate an Image:** Start by uploading your own image or generate one using our AI-powered tool.
            2. **Customize Your Grid:** Play around with the settings to create the perfect hexagon grid overlay.
            3. **Download and Share:** Once you're happy with your creation, download it and share it with the world!

            ### Advanced Features
            - **Generative AI Integration:** Utilize models like `black-forest-labs/FLUX.1-dev` and various LoRA weights for generating unique images.
            - **Pre-rendered Maps:** Access a library of pre-rendered hexagon maps for quick and easy customization.
            - **Image Filter [Look-Up Table (LUT)] Application:** Apply filters (LUTs) to your images for color grading and enhancement.
            - **Depth and 3D Model Generation:** Create depth maps and 3D models from your images for enhanced visualization.
            - **Add Margins:** Customize margins around your images for a polished finish.

            Join the hive and start creating with Hex Game Maker today!
        
            """, elem_classes="intro")
    selected_index = gr.State(None)
    with gr.Row():
        with gr.Column(scale=2):
            progress_bar = gr.Markdown(elem_id="progress",visible=False)
            input_image = gr.Image(
                label="Input Image",
                type="filepath",
                interactive=True,
                elem_classes="centered solid imgcontainer",
                key="imgInput",
                image_mode="RGB",
                format="PNG"
            )

        with gr.Column(scale=0):
            with gr.Accordion("Sketch Pad", open = False):
                with gr.Row():
                    sketch_image = gr.Sketchpad(
                        label="Sketch Image",
                        type="filepath",
                        #invert_colors=True,
                        #sources=['upload','canvas'],
                        #tool=['editor','select','color-sketch'],
                        placeholder="Draw a sketch or upload an image. Currently broken in gradio 5.17.1",
                        interactive=True,
                        elem_classes="centered solid imgcontainer",
                        key="imgSketch",
                        image_mode="RGBA",
                        format="PNG",
                        brush=gr.Brush()
                    )
                with gr.Row():
                    with gr.Column(scale=1):
                        sketch_replace_input_image_button = gr.Button(
                            "Replace Input Image with sketch",
                            elem_id="sketch_replace_input_image_button",
                            elem_classes="solid"
                        )
                    with gr.Column(scale=2):
                        alpha_composite_slider = gr.Slider(0,100,50,0.5, label="Alpha Composite Sketch to Input Image", elem_id="alpha_composite_slider")
                    
            with gr.Accordion("Image Filters", open = False):
                with gr.Row():
                    with gr.Column():
                        composite_color = gr.ColorPicker(label="Color", value="#ede9ac44")
                        composite_opacity = gr.Slider(label="Opacity %", minimum=0, maximum=100, value=50, interactive=True)
                with gr.Row():
                    composite_button = gr.Button("Composite", elem_classes="solid")
                with gr.Row():
                    with gr.Column():
                        lut_filename = gr.Textbox(
                            value="", 
                            label="Look Up Table (LUT) File Name",
                            elem_id="lutFileName")
                    with gr.Column():
                        lut_file = gr.File(
                            value=None,
                            file_count="single",
                            file_types=[".cube"],
                            type="filepath",
                            label="LUT cube File")
                with gr.Row():
                    lut_example_image = gr.Image(type="pil", label="Filter (LUT) Example Image", value=default_lut_example_img)
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("""
                        ### Included Filters (LUTs)
                        There are several included Filters:

                        Try them on the example image before applying to your Input Image.
                        """, elem_id="lut_markdown")
                    with gr.Column():
                        gr.Examples(elem_id="lut_examples",
                            examples=[[f] for f in lut_files],
                            inputs=[lut_filename],
                            outputs=[lut_filename],
                            label="Select a Filter (LUT) file. Populate the LUT File Name field",
                            examples_per_page=15
                        )
                    
                with gr.Row():
                    apply_lut_button = gr.Button("Apply Filter (LUT)", elem_classes="solid", elem_id="apply_lut_button")
                    
                lut_file.change(get_filename, inputs=[lut_file], outputs=[lut_filename])
                lut_filename.change(show_lut, inputs=[lut_filename, lut_example_image], outputs=[lut_example_image])
                apply_lut_button.click(
                    lambda lut_filename, input_image: gr.Warning("Please upload an Input Image to get started.") if input_image is None else apply_lut_to_image_path(lut_filename, input_image)[0], 
                    inputs=[lut_filename, input_image], 
                    outputs=[input_image], 
                    scroll_to_output=True
                )
    with gr.Row():
        with gr.Accordion("Generative AI", open = True ):
            with gr.Column():
                map_options = gr.Dropdown(
                    label="Map Options*",
                    choices=list(PROMPTS.keys()),
                    value="Alien Landscape",
                    elem_classes="solid",
                    scale=0
                )
                prompt = gr.Textbox(
                    label="Prompt",
                    visible=False,
                    elem_classes="solid",
                    value="top-down, (rectangular tabletop_map) alien planet map, Battletech_boardgame scifi world with forests, lakes, oceans, continents and snow at the top and bottom, (middle is dark, no_reflections, no_shadows), from directly above. From 100,000 feet looking straight down",
                    lines=4
                )
                negative_prompt_textbox = gr.Textbox(
                    label="Negative Prompt",
                    visible=False,
                    elem_classes="solid",
                    value="Earth, low quality, bad anatomy, blurry, cropped, worst quality, shadows, people, humans, reflections, shadows, realistic map of the Earth, isometric, text"
                )
                prompt_notes_label = gr.Label(
                    "Choose a LoRa style or add an image. YOU MUST CLEAR THE IMAGE TO START OVER ",
                    elem_classes="solid centered small",
                    show_label=False,
                    visible=False
                )
                # Keep the change event to maintain functionality
                map_options.change(
                    fn=update_prompt_visibility,
                    inputs=[map_options],
                    outputs=[prompt, negative_prompt_textbox, prompt_notes_label]
                )
            with gr.Row():
                with gr.Column(scale=1):
                    generate_button = gr.Button("Generate From Map Options, Input Image and LoRa Style", variant="primary", elem_id="gen_btn")
                    with gr.Accordion("LoRA Styles*", open=False):
                        selected_info = gr.Markdown("")
                        lora_gallery = gr.Gallery(
                            [(item["image"], item["title"]) for item in loras],
                            label="LoRA Styles",
                            allow_preview=False,
                            columns=3,
                            elem_id="lora_gallery",
                            show_share_button=False
                        )
                    with gr.Accordion("Custom LoRA", open=False):
                        with gr.Group():
                            custom_lora = gr.Textbox(label="Enter Custom LoRA. **NOT TESTED**", 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(scale=2):
                    generate_input_image_from_gallery = gr.Button(
                        "Generate AI Image from Template Image",
                        elem_id="generate_input_image_from_gallery",
                        elem_classes="solid",
                        variant="primary"
                    )
                    with gr.Accordion("Template Images", open = False):
                        with gr.Row():
                            with gr.Column(scale=1):
                                # Gallery from PRE_RENDERED_IMAGES GOES HERE
                                prerendered_image_gallery = gr.Gallery(label="Template Gallery", show_label=True, value=build_prerendered_images_by_quality(3,'thumbnail'), elem_id="gallery", elem_classes="solid", type="filepath", columns=[3], rows=[3], preview=False ,object_fit="contain", height="auto", format="png",allow_preview=False)                            
                            with gr.Column(scale=1):
                                # def handle_login(request: gr.Request):
                                #     # Extract user information from the request
                                #     user_info = {
                                #         "username": request.username,
                                #         "session_hash": request.session_hash,
                                #         "headers": dict(request.headers),
                                #         "client": request.client,
                                #         "query_params": dict(request.query_params),
                                #         "path_params": dict(request.path_params)
                                #     }
                                #     print(f"\n{user_info}\n")
                                #     return user_info
                                replace_input_image_button = gr.Button(
                                    "Replace Input Image",
                                    elem_id="prerendered_replace_input_image_button",
                                    elem_classes="solid"
                                )
                                # login_button = gr.LoginButton()
                                # user_info_output = gr.JSON(label="User Information")
                                # login_button.click(fn=handle_login, inputs=[], outputs=user_info_output)

            with gr.Row():
                with gr.Accordion("Advanced Settings", open=False):
                        with gr.Row():
                            image_strength = gr.Slider(label="Image Guidance Strength (prompt percentage)", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.85)
                        with gr.Column():
                            with gr.Row():
                                cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=5.0)
                                steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=30)
                
                            with gr.Row():
                                negative_prompt_textbox = gr.Textbox(
                                        label="Negative Prompt",
                                        visible=False,
                                        elem_classes="solid",
                                        value="Earth, low quality, bad anatomy, blurry, cropped, worst quality, shadows, people, humans, reflections, shadows, realistic map of the Earth, isometric, text"
                                )
                                # Add Dropdown for sizing of Images, height and width based on selection. Options are 16x9, 16x10, 4x5, 1x1
                                # The values of height and width are based on common resolutions for each aspect ratio
                                # Default to 16x9, 1024x576
                                image_size_ratio = gr.Dropdown(label="Image Aspect Ratio", choices=["16:9", "16:10", "4:5", "4:3", "2:1","3:2","1:1", "9:16", "10:16", "5:4", "3:4","1:2", "2:3"], value="16:9", elem_classes="solid", type="value", scale=0, interactive=True)
                                width = gr.Slider(label="Width", minimum=256, maximum=2560, step=16, value=1024, interactive=False)
                                height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=512)
                                enlarge_to_default = gr.Checkbox(label="Auto Enlarge to Default Size", value=False)
                                image_size_ratio.change(
                                    fn=update_dimensions_on_ratio,
                                    inputs=[image_size_ratio, height],
                                    outputs=[width, height]
                                )
                                height.change(
                                    fn=lambda *args: update_dimensions_on_ratio(*args)[0],
                                    inputs=[image_size_ratio, height],
                                    outputs=[width]
                                )
                            with gr.Row():
                                randomize_seed = gr.Checkbox(False, label="Randomize seed",elem_id="rnd_seed_chk")
                                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True, elem_id="rnd_seed")
                                lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=1.01)
    with gr.Row():
        login_button = gr.LoginButton(logout_value=f"Logout({user_info['username']} ({user_info['level']}))", size="md", elem_classes="solid centered", elem_id="hf_login_btn", icon="./assets/favicon.ico")
        # Create a JSON component to display the user information
        user_info_output = gr.JSON(label="User Information:")
    
        # Set up the event listener for the login button click
        login_button.click(fn=handle_login, inputs=[], outputs=[user_info_output, login_button])
    with gr.Row():
        gr.HTML(value=getVersions(), visible=True, elem_id="versions")

    # Event Handlers
    composite_button.click(
        fn=lambda input_image, composite_color, composite_opacity: gr.Warning("Please upload an Input Image to get started.") if input_image is None else change_color(input_image, composite_color, composite_opacity),
        inputs=[input_image, composite_color, composite_opacity],
        outputs=[input_image]
    )
    input_image.input(
        fn=on_input_image_change,
        inputs=[input_image],
        outputs=[input_image,sketch_image], scroll_to_output=True,
    )
    #use conditioned_image as the input_image for generate_input_image_click
    generate_input_image_from_gallery.click(
        fn=run_lora,
        inputs=[prompt, map_options, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, enlarge_to_default, gr.State(True)],
        outputs=[input_image, seed, progress_bar], scroll_to_output=True
    ).then(
        fn=update_sketch_dimensions,
        inputs=[input_image, sketch_image],
        outputs=[sketch_image]
    )
    prerendered_image_gallery.select(
        fn=on_prerendered_gallery_selection, 
        inputs=None, 
        outputs=gr.State(current_prerendered_image),  # Update the state with the selected image
        show_api=False, scroll_to_output=True
    )
    alpha_composite_slider.change(
        fn=composite_with_control_sync,
        inputs=[input_image, sketch_image, alpha_composite_slider],
        outputs=[input_image],
        scroll_to_output=True
    )
    sketch_replace_input_image_button.click(
        lambda sketch_image: replace_input_with_sketch_image(sketch_image),
        inputs=[sketch_image],
        outputs=[input_image], scroll_to_output=True
    )
    # replace input image with selected prerendered image gallery selection
    replace_input_image_button.click(
        lambda: current_prerendered_image.value,
        inputs=None,
        outputs=[input_image], scroll_to_output=True
    ).then(
        fn=update_sketch_dimensions,
        inputs=[input_image, sketch_image],
        outputs=[sketch_image]
    )
    lora_gallery.select(
        update_selection,
        inputs=[width, height, image_size_ratio],
        outputs=[prompt, selected_info, selected_index, width, height, image_size_ratio, prompt_notes_label]
    )
    custom_lora.input(
        add_custom_lora,
        inputs=[custom_lora],
        outputs=[custom_lora_info, custom_lora_button, lora_gallery, selected_info, selected_index, prompt]
    )
    custom_lora_button.click(
        remove_custom_lora,
        outputs=[custom_lora_info, custom_lora_button, lora_gallery, selected_info, selected_index, custom_lora]
    )
    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=run_lora,
        inputs=[prompt, map_options, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, enlarge_to_default, gr.State(False)],
        outputs=[input_image, seed, progress_bar]
    ).then(
        fn=update_sketch_dimensions,
        inputs=[input_image, sketch_image],
        outputs=[sketch_image]
    )

load_env_vars(dotenv_path)
logging.basicConfig(
    format="[%(levelname)s] %(asctime)s %(message)s", level=logging.INFO
)
logging.info("Environment Variables: %s" % os.environ)

app.queue()
app.launch(allowed_paths=["assets","/","./assets","images","./images", "./images/prerendered"], favicon_path="./assets/favicon.ico", max_file_size="10mb")