# Thanks: https://huggingface.co/spaces/stabilityai/stable-diffusion-3-medium
import spaces
import os
import gradio as gr
import numpy as np
import random
import torch
from diffusers import StableDiffusion3Pipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

device = "cuda"
dtype = torch.float16

repo = "aipicasso/emi-3"
t2i = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.bfloat16, token=os.environ["TOKEN"]).to(device)

model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct", 
    device_map="cuda", 
    torch_dtype=torch.bfloat16, 
    trust_remote_code=True, 
    token=os.environ["TOKEN"]
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", token=os.environ["TOKEN"])
upsampler = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 226,
    "return_full_text": False,
    "temperature": 0.7,
    "do_sample": True,
    "top_p": 0.95
}

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1344

@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
    messages = [
        {"role": "user", "content": "次のプロンプトを想像を膨らませて英語に翻訳してください。「クールなアニメ風の女の子」"},
        {"role": "assistant", "content": "An anime style illustration of a cool-looking teenage girl with an edgy, confident expression. She has piercing eyes, a slight smirk, and colorful hair that flows in the wind. "},
        {"role": "user", "content": "次のプロンプトを想像を膨らませて英語に翻訳してください。「実写風の女子高生」"},
        {"role": "assistant", "content": "A photorealistic image of a female high school student standing on a city street. She is wearing a traditional Japanese school uniform, consisting of a navy blue blazer, a white blouse, and a knee-length plaid skirt. "},
        {"role": "user", "content": f"次のプロンプトを想像を膨らませて英語に翻訳してください。「{prompt}」" },
    ]
    output = upsampler(messages, **generation_args)
    upsampled_prompt=output[0]['generated_text']
    print(upsampled_prompt)
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    image = t2i(
        prompt = upsampled_prompt, 
        negative_prompt = negative_prompt,
        guidance_scale = guidance_scale, 
        num_inference_steps = num_inference_steps, 
        width = width, 
        height = height,
        generator = generator
    ).images[0] 
    
    return image, seed, upsampled_prompt

examples = [
    "美味しい肉",
    "馬に乗った宇宙飛行士",
    "アニメ風の美少女",
    "女子高生の写真",
    "寿司でできた家に入っているコーギー",
    "バナナとアボカドが戦っている様子"
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 580px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # 日本語が入力できる SD3.5 Large
        """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="プロンプト",
                show_label=False,
                max_lines=1,
                placeholder="作りたい画像の特徴を入力してください",
                container=False,
            )
            
            run_button = gr.Button("実行", scale=0)
        
        result = gr.Image(label="結果", show_label=False)
        generated_prompt = gr.Textbox(label="生成に使ったプロンプト", show_label=False, interactive=False)
        
        with gr.Accordion("詳細設定", open=False):
            
            negative_prompt = gr.Text(
                label="ネガティブプロンプト",
                max_lines=1,
                placeholder="画像から排除したい要素を入力してください",
            )
            
            seed = gr.Slider(
                label="乱数のシード",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="ランダム生成", value=True)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="横",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1024,
                )
                
                height = gr.Slider(
                    label="縦",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1024,
                )
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="プロンプトの忠実さ",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=3.5,
                )
                
                num_inference_steps = gr.Slider(
                    label="推論回数",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )
        
        gr.Examples(
            examples = examples,
            inputs = [prompt]
        )
    gr.on(
        triggers=[run_button.click, prompt.submit, negative_prompt.submit],
        fn = infer,
        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result, seed, generated_prompt]
    )

demo.launch()