import spaces
import os
import torch
import random
from PIL import Image
import io
import base64
from huggingface_hub import snapshot_download
from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256 import StableDiffusionXLPipeline
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from diffusers import UNet2DConditionModel, AutoencoderKL
from diffusers import EulerDiscreteScheduler
import gradio as gr
import requests

# Download the model files
ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors")

# Load the models
text_encoder = ChatGLMModel.from_pretrained(
    os.path.join(ckpt_dir, 'text_encoder'),
    torch_dtype=torch.float16).half()
tokenizer = ChatGLMTokenizer.from_pretrained(os.path.join(ckpt_dir, 'text_encoder'))
vae = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir, "vae"), revision=None).half()
scheduler = EulerDiscreteScheduler.from_pretrained(os.path.join(ckpt_dir, "scheduler"))
unet = UNet2DConditionModel.from_pretrained(os.path.join(ckpt_dir, "unet"), revision=None).half()

pipe = StableDiffusionXLPipeline(
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=unet,
        scheduler=scheduler,
        force_zeros_for_empty_prompt=False)
pipe = pipe.to("cuda")


API_URL = "https://bots.spaceship.im"  # Replace with your actual API endpoint

url_params = gr.JSON({}, visible=True, label="URL Params")
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(label="Negative Prompt")
gallery = gr.Gallery(label="Result", elem_id="gallery", show_label=False)
height = gr.Slider(512, 2048, 1024, step=64, label="Height")
width = gr.Slider(512, 2048, 1024, step=64, label="Width")
steps = gr.Slider(1, 50, 25, step=1, label="Steps")
number_of_images = gr.Slider(
    1, 4, 1, step=1, label="Number of images per prompt")
random_seed = gr.Checkbox(label="Use Random Seed", value=True)
seed = gr.Number(label="Seed", value=0, precision=0)
seed_used = gr.Number(label="Seed Used")


def test_func(request: gr.Request):
    data = request.query_params
    if "uuid" in data:
        msg_id = data["uuid"]
        response = requests.get(f"{API_URL}/check_data/{msg_id}")
        if response.status_code == 200:
            api_data = response.json().get("data")
            return [value for value in api_data.values()]
    return prompt, data, height, width, steps, number_of_images, random_seed, seed, gallery, seed_used

@spaces.GPU(duration=200)
def generate_image(request: gr.Request,prompt, negative_prompt, height, width, num_inference_steps, guidance_scale, num_images_per_prompt, use_random_seed, seed,progress=gr.Progress(track_tqdm=True)):
    if use_random_seed:
        seed = random.randint(0, 2**32 - 1)
    else:
        seed = int(seed)  # Ensure seed is an integer
        height = int(height)
        width = int(width)
        print(f'Debug: Retrieved height = {height}, width = {width}')

    
    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=1024,
        width=1024,
        num_inference_steps=20,
        guidance_scale=guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
        generator=torch.Generator(pipe.device).manual_seed(seed)
    ).images
    # testing


image = image[0]  # Since it's in a list

# Convert the image to an in-memory byte stream
byte_stream = io.BytesIO()
image.save(byte_stream, format='PNG')  # You can choose other formats like 'JPEG' if needed
byte_data = byte_stream.getvalue()

# Encode the byte stream to a base64 string
base64_str = base64.b64encode(byte_data).decode('utf-8')

# For demonstration, print the base64 string
print(base64_str)

    query_data: dict = request.query_params
    save_data = {
            "prompt": prompt,
            "url_params": url_params,
            "height": height,
            "width": width,
            "steps": steps,
            "num_images_per_prompt": num_images_per_prompt,
            "use_random_seed": use_random_seed,
            "seed": seed,
            "output": base64_str,
            "seed_used" : seed
        }

    print(save_data)
    print(query_data)
    
    # url = f"https://bots.spaceship.im/save_data/{query_data['uuid']}"
    # res = requests.post(url, json=save_data)
    
    
    return image, seed

description = """
<p align="center">Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis</p>
<p><center>
<a href="https://kolors.kuaishou.com/" target="_blank">[Official Website]</a>
<a href="https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf" target="_blank">[Tech Report]</a>
<a href="https://huggingface.co/Kwai-Kolors/Kolors" target="_blank">[Model Page]</a>
<a href="https://github.com/Kwai-Kolors/Kolors" target="_blank">[Github]</a>
</center></p>
"""

# Gradio interface
with gr.Blocks() as demo:
    iface = gr.Interface(
        fn=generate_image,
        inputs=[
            prompt,
            negative_prompt,
            url_params
        ],
        additional_inputs=[
            height,
            width,
            steps,
            number_of_images,
            random_seed,
            seed
        ],
        additional_inputs_accordion=gr.Accordion(
            label="Advanced settings", open=False),
        outputs=[
            gallery,
            seed_used
        ],
        title="Kolors",
        description=description,
        theme='bethecloud/storj_theme',
    )
    demo.load(fn=test_func, outputs=[
              prompt, url_params, height, width, steps, number_of_images, random_seed, seed, gallery, seed_used])

demo.launch(debug=True)