import gradio as gr
import numpy as np
import os
import spaces
import random
import json
# from image_gen_aux import DepthPreprocessor
from PIL import Image
import torch
from torchvision import transforms

from diffusers import FluxFillPipeline, AutoencoderKL
from PIL import Image


MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
# pipe.load_lora_weights("Himanshu806/testLora")
# pipe.enable_lora()

with open("lora_models.json", "r") as f:
    lora_models = json.load(f)

def download_model(model_name, model_path):
    print(f"Downloading model: {model_name} from {model_path}")
    try:
        pipe.load_lora_weights(model_path)
        print(f"Successfully downloaded model: {model_name}")
    except Exception as e:
        print(f"Failed to download model: {model_name}. Error: {e}")

# Iterate through the models and download each one
for model_name, model_path in lora_models.items():
    download_model(model_name, model_path)

lora_models["None"] = None

@spaces.GPU(durations=300)
def infer(edit_images, prompt, width, height, lora_model, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    # pipe.enable_xformers_memory_efficient_attention()

    if lora_model != "None":
        pipe.load_lora_weights(lora_models[lora_model])
        pipe.enable_lora()

    image = edit_images["background"]
    # width, height = calculate_optimal_dimensions(image)
    mask = edit_images["layers"][0]
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    # controlImage = processor(image)
    image = pipe(
        # mask_image_latent=vae.encode(controlImage),
        prompt=prompt,
        prompt_2=prompt,
        image=image,
        mask_image=mask,
        height=height,
        width=width,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=torch.Generator(device='cuda').manual_seed(seed),
        # lora_scale=0.75 // not supported in this version
    ).images[0]

    output_image_jpg = image.convert("RGB")
    output_image_jpg.save("output.jpg", "JPEG")

    return output_image_jpg, seed
    # return image, seed

examples = [
    "photography of a young woman,  accent lighting,  (front view:1.4),  "
    # "a tiny astronaut hatching from an egg on the moon",
    # "a cat holding a sign that says hello world",
    # "an anime illustration of a wiener schnitzel",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 1000px;
}
"""

with gr.Blocks(css=css) as demo:

    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 [dev]
        """)
        with gr.Row():
            with gr.Column():
                edit_image = gr.ImageEditor(
                    label='Upload and draw mask for inpainting',
                    type='pil',
                    sources=["upload", "webcam"],
                    image_mode='RGB',
                    layers=False,
                    brush=gr.Brush(colors=["#FFFFFF"]),
                    # height=600
                )
                prompt = gr.Text(
                    label="Prompt",
                    show_label=False,
                    max_lines=2,
                    placeholder="Enter your prompt",
                    container=False,
                )

                lora_model = gr.Dropdown(
                    label="Select LoRA Model",
                    choices=list(lora_models.keys()),
                    value="None",
                )

                run_button = gr.Button("Run")

            result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():

                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=30,
                    step=0.5,
                    value=50,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )

            with gr.Row():

                width = gr.Slider(
                    label="width",
                    minimum=512,
                    maximum=3072,
                    step=1,
                    value=1024,
                )

                height = gr.Slider(
                    label="height",
                    minimum=512,
                    maximum=3072,
                    step=1,
                    value=1024,
                )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [edit_image, prompt, width, height, lora_model, seed, randomize_seed, guidance_scale, num_inference_steps],
        outputs = [result, seed]
    )

# demo.launch()
PASSWORD = os.getenv("GRADIO_PASSWORD")
USERNAME = os.getenv("GRADIO_USERNAME")
# Create an authentication object
def authenticate(username, password):
    if username == USERNAME and password == PASSWORD:
        return True

    else:
        return False
# Launch the app with authentication

demo.launch(auth=authenticate)