import os
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

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

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

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
                                                     )

MAX_SEED = 2**32-1

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, selected_lora1, selected_lora2):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"

    # Initialize outputs
    outputs = []

    if selected_lora1 is None:
        selected_lora1 = selected_lora
        selected_lora1_info = f"### LoRA 1 Selected: [{selected_lora1['title']}](https://huggingface.co./{selected_lora1['repo']}) ✨"
        lora_scale1_visible = True
        remove_lora1_visible = True
    elif selected_lora2 is None:
        selected_lora2 = selected_lora
        selected_lora2_info = f"### LoRA 2 Selected: [{selected_lora2['title']}](https://huggingface.co./{selected_lora2['repo']}) ✨"
        lora_scale2_visible = True
        remove_lora2_visible = True
    else:
        raise gr.Error("You can only select up to two LoRAs. Please remove one before selecting another.")

    # Update placeholder
    placeholder_update = gr.update(placeholder=new_placeholder)

    # For width and height adjustment
    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 placeholder_update, selected_lora1, selected_lora2, selected_lora1_info, selected_lora2_info, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), width, height

def remove_selected_lora1(selected_lora1, selected_lora1_info):
    selected_lora1 = None
    selected_lora1_info = ""
    return selected_lora1, selected_lora1_info, gr.update(visible=False), gr.update(visible=False)

def remove_selected_lora2(selected_lora2, selected_lora2_info):
    selected_lora2 = None
    selected_lora2_info = ""
    return selected_lora2, selected_lora2_info, gr.update(visible=False), gr.update(visible=False)

@spaces.GPU(duration=70)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    with calculateDuration("Generating image"):
        # Generate image
        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,
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img

@spaces.GPU(duration=70)
def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
    generator = torch.Generator(device="cuda").manual_seed(seed)
    pipe_i2i.to("cuda")
    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,
        output_type="pil",
    ).images[0]
    return final_image 

def run_lora(prompt, image_input, image_strength, cfg_scale, steps, randomize_seed, seed, width, height, selected_lora1, selected_lora2, lora_scale1, lora_scale2, progress=gr.Progress(track_tqdm=True)):
    if selected_lora1 is None and selected_lora2 is None:
        raise gr.Error("You must select at least one LoRA before proceeding.")
    
    # Build the prompt mash
    prompt_mash = prompt

    # Handle trigger words and positions
    trigger_words = []
    if selected_lora1 is not None:
        trigger_word1 = selected_lora1.get("trigger_word", "")
        if trigger_word1:
            if selected_lora1.get("trigger_position") == "prepend":
                trigger_words.insert(0, trigger_word1)
            else:
                trigger_words.append(trigger_word1)
    if selected_lora2 is not None:
        trigger_word2 = selected_lora2.get("trigger_word", "")
        if trigger_word2:
            if selected_lora2.get("trigger_position") == "prepend":
                trigger_words.insert(0, trigger_word2)
            else:
                trigger_words.append(trigger_word2)
    # Combine trigger words with the prompt
    if trigger_words:
        prompt_mash = f"{' '.join(trigger_words)} {prompt}"

    with calculateDuration("Unloading LoRAs"):
        pipe.unload_lora_weights()
        pipe_i2i.unload_lora_weights()
        
    # Load LoRA weights with respective scales
    with calculateDuration("Loading LoRA weights"):
        if image_input is not None:
            if selected_lora1 is not None:
                pipe_i2i.load_lora_weights(selected_lora1['repo'], weight_name=selected_lora1.get('weights'), scale=lora_scale1)
            if selected_lora2 is not None:
                pipe_i2i.load_lora_weights(selected_lora2['repo'], weight_name=selected_lora2.get('weights'), scale=lora_scale2)
        else:
            if selected_lora1 is not None:
                pipe.load_lora_weights(selected_lora1['repo'], weight_name=selected_lora1.get('weights'), scale=lora_scale1)
            if selected_lora2 is not None:
                pipe.load_lora_weights(selected_lora2['repo'], weight_name=selected_lora2.get('weights'), scale=lora_scale2)
                
    # Set random seed for reproducibility
    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)
        yield final_image, seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
        # Consume the generator to get the final image
        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)
        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)
        if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "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()
        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=True), 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, ""

run_lora.zerogpu = True

css = '''
#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: 0.5em}
#gallery .grid-wrap{height: 10vh}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.custom_lora_card .card_internal{display: flex;height: 100px;margin-top: .5em}
.custom_lora_card .card_internal img{margin-right: 1em}
.styler{--form-gap-width: 0px !important}
#progress{height:30px}
#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}
'''
with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
    title = gr.HTML(
        """<h1><img src="https://huggingface.co./spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> LoRA Lab</h1>""",
        elem_id="title",
    )
    selected_lora1 = gr.State(None)
    selected_lora2 = gr.State(None)
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting LoRAs")
        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():
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA Gallery",
                allow_preview=False,
                columns=3,
                elem_id="gallery"
            )
            with gr.Group():
                custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
                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")
            custom_lora_info = gr.HTML(visible=False)
            custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
            # Selected LoRAs section
            gr.Markdown("### Selected LoRAs")
            with gr.Row():
                with gr.Column():
                    selected_lora1_info = gr.Markdown("", visible=False)
                    lora_scale1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=0.95, visible=False)
                    remove_lora1_button = gr.Button("Remove LoRA 1", visible=False)
                with gr.Column():
                    selected_lora2_info = gr.Markdown("", visible=False)
                    lora_scale2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=0.95, visible=False)
                    remove_lora2_button = gr.Button("Remove LoRA 2", 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)

    gallery.select(
        update_selection,
        inputs=[width, height, selected_lora1, selected_lora2],
        outputs=[prompt, selected_lora1, selected_lora2, selected_lora1_info, selected_lora2_info, lora_scale1, remove_lora1_button, lora_scale2, remove_lora2_button, width, height]
    )
    remove_lora1_button.click(
        remove_selected_lora1,
        inputs=[selected_lora1, selected_lora1_info],
        outputs=[selected_lora1, selected_lora1_info, lora_scale1, remove_lora1_button]
    )
    remove_lora2_button.click(
        remove_selected_lora2,
        inputs=[selected_lora2, selected_lora2_info],
        outputs=[selected_lora2, selected_lora2_info, lora_scale2, remove_lora2_button]
    )
    custom_lora.input(
        add_custom_lora,
        inputs=[custom_lora],
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_lora1_info, selected_lora2_info, prompt]
    )
    custom_lora_button.click(
        remove_custom_lora,
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_lora1_info, selected_lora2_info, custom_lora]
    )
    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=run_lora,
        inputs=[prompt, input_image, image_strength, cfg_scale, steps, randomize_seed, seed, width, height, selected_lora1, selected_lora2, lora_scale1, lora_scale2],
        outputs=[result, seed, progress_bar]
    )

app.queue()
app.launch()