import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time
from transformers import pipeline
import sqlite3
from datetime import datetime

# 데이터베이스 초기화
def init_db():
    conn = sqlite3.connect('gallery.db')
    c = conn.cursor()
    c.execute('''CREATE TABLE IF NOT EXISTS images
                 (id INTEGER PRIMARY KEY AUTOINCREMENT,
                  model_name TEXT,
                  prompt TEXT,
                  image_path TEXT,
                  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP)''')
    conn.commit()
    conn.close()

# 이미지 저장 함수
def save_image(image, model_name, prompt):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"gallery_{timestamp}.png"
    image.save(os.path.join("gallery", filename))
    
    conn = sqlite3.connect('gallery.db')
    c = conn.cursor()
    c.execute("INSERT INTO images (model_name, prompt, image_path) VALUES (?, ?, ?)",
              (model_name, prompt, filename))
    conn.commit()
    conn.close()

# 갤러리 이미지 로드 함수
def load_gallery_images():
    conn = sqlite3.connect('gallery.db')
    c = conn.cursor()
    c.execute("SELECT model_name, prompt, image_path FROM images ORDER BY created_at DESC")
    rows = c.fetchall()
    conn.close()
    
    return [(os.path.join("gallery", row[2]), f"{row[0]}: {row[1]}") for row in rows]

# 번역 모델 초기화
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")

# 프롬프트 처리 함수 추가
def process_prompt(prompt):
    if any('\u3131' <= char <= '\u3163' or '\uac00' <= char <= '\ud7a3' for char in prompt):
        translated = translator(prompt)[0]['translation_text']
        return prompt, translated
    return prompt, prompt

KEY_JSON = os.getenv("KEY_JSON")
with open(KEY_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)

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_lora = loras[evt.index]
    new_placeholder = f"{selected_lora['title']}를 위한 프롬프트를 입력하세요"
    lora_repo = selected_lora["repo"]
    updated_text = f"### 선택됨: [{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,
    )

@spaces.GPU(duration=70)
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)
    with calculateDuration("이미지 생성"):
        # 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,
            joint_attention_kwargs={"scale": lora_scale},
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img

def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
    if selected_index is None:
        raise gr.Error("진행하기 전에 LoRA를 선택해야 합니다.")
    
    original_prompt, english_prompt = process_prompt(prompt)
    
    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} {english_prompt}"
            else:
                prompt_mash = f"{english_prompt} {trigger_word}"
        else:
            prompt_mash = f"{trigger_word} {english_prompt}"
    else:
        prompt_mash = english_prompt

    with calculateDuration("LoRA 언로드"):
        pipe.unload_lora_weights()
        
    # Load LoRA weights
    with calculateDuration(f"{selected_lora['title']}의 LoRA 가중치 로드"):
        if "weights" in selected_lora:
            pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
        else:
            pipe.load_lora_weights(lora_path)

    # Set random seed for reproducibility
    with calculateDuration("시드 무작위화"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)

    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, 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), original_prompt, english_prompt
    
    # 갤러리에 이미지 저장
    save_image(final_image, selected_lora['title'], original_prompt)
    
    yield final_image, seed, gr.update(value=progress_bar, visible=False), original_prompt, english_prompt


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 = """
footer {
    visibility: hidden;
}
"""


def create_snow_effect():
    # CSS 스타일 정의
    snow_css = """
    @keyframes snowfall {
        0% {
            transform: translateY(-10vh) translateX(0);
            opacity: 1;
        }
        100% {
            transform: translateY(100vh) translateX(100px);
            opacity: 0.3;
        }
    }
    .snowflake {
        position: fixed;
        color: white;
        font-size: 1.5em;
        user-select: none;
        z-index: 1000;
        pointer-events: none;
        animation: snowfall linear infinite;
    }
    """

    # JavaScript 코드 정의
    snow_js = """
    function createSnowflake() {
        const snowflake = document.createElement('div');
        snowflake.innerHTML = '❄';
        snowflake.className = 'snowflake';
        snowflake.style.left = Math.random() * 100 + 'vw';
        snowflake.style.animationDuration = Math.random() * 3 + 2 + 's';
        snowflake.style.opacity = Math.random();
        document.body.appendChild(snowflake);
        
        setTimeout(() => {
            snowflake.remove();
        }, 5000);
    }
    setInterval(createSnowflake, 200);
    """

    # CSS와 JavaScript를 결합한 HTML 
    snow_html = f"""
    <style>
        {snow_css}
    </style>
    <script>
        {snow_js}
    </script>
    """
    
    return gr.HTML(snow_html)


# 갤러리 디렉토리 생성
if not os.path.exists('gallery'):
    os.makedirs('gallery')

# 데이터베이스 초기화
init_db()

with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as app:
    create_snow_effect()    
    selected_index = gr.State(None)
    
    with gr.Tabs():
        with gr.TabItem("생성"):
            with gr.Row():
                with gr.Column(scale=3):
                    prompt = gr.Textbox(label="프롬프트", lines=1, placeholder="LoRA를 선택한 후 프롬프트를 입력하세요 (한글 또는 영어)")
                with gr.Column(scale=1, elem_id="gen_column"):
                    generate_button = gr.Button("생성", 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 loras],
                        label="LoRA 갤러리",
                        allow_preview=False,
                        columns=3,
                        elem_id="gallery"
                    )
                    with gr.Group():
                        custom_lora = gr.Textbox(label="커스텀 LoRA", info="LoRA Hugging Face 경로", placeholder="multimodalart/vintage-ads-flux")
                        gr.Markdown("[FLUX LoRA 목록 확인](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("커스텀 LoRA 제거", visible=False)
                with gr.Column():
                    progress_bar = gr.Markdown(elem_id="progress",visible=False)
                    result = gr.Image(label="생성된 이미지")
                    original_prompt_display = gr.Textbox(label="원본 프롬프트")
                    english_prompt_display = gr.Textbox(label="영어 프롬프트")

            with gr.Row():
                with gr.Accordion("고급 설정", open=False):
                    with gr.Column():
                        with gr.Row():
                            cfg_scale = gr.Slider(label="CFG 스케일", minimum=1, maximum=20, step=0.5, value=3.5)
                            steps = gr.Slider(label="스텝", minimum=1, maximum=50, step=1, value=28)
                        
                        with gr.Row():
                            width = gr.Slider(label="너비", minimum=256, maximum=1536, step=64, value=1024)
                            height = gr.Slider(label="높이", minimum=256, maximum=1536, step=64, value=1024)
                        
                        with gr.Row():
                            randomize_seed = gr.Checkbox(True, label="시드 무작위화")
                            seed = gr.Slider(label="시드", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
                            lora_scale = gr.Slider(label="LoRA 스케일", minimum=0, maximum=3, step=0.01, value=0.95)

        with gr.TabItem("갤러리"):
            gallery_images = gr.Gallery(
                load_gallery_images(),
                label="생성된 이미지 갤러리",
                columns=3,
                rows=3,
                height="auto"
            )
            refresh_button = gr.Button("갤러리 새로고침")

    gallery.select(
        update_selection,
        inputs=[width, height],
        outputs=[prompt, selected_info, selected_index, width, height]
    )
    custom_lora.input(
        add_custom_lora,
        inputs=[custom_lora],
        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]
    )

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

    refresh_button.click(
        lambda: gr.update(value=load_gallery_images()),
        outputs=[gallery_images]
    )

app.queue()
app.launch()