import gradio as gr
import numpy as np
import random
from PIL import Image
import os

import spaces
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, StableDiffusionImg2ImgPipeline
import torch
from huggingface_hub import login

# Get token from Hugging Face Spaces secrets
# This will use the environment variable HF_ACCESS_TOKEN which is the standard in HF Spaces
hf_token = os.environ.get("HF_ACCESS_TOKEN")
if hf_token:
    login(hf_token)
else:
    print("Warning: HF_ACCESS_TOKEN not found in environment. Authentication may fail.")

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/stable-diffusion-3.5-medium"

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

# For text-to-image
pipe = DiffusionPipeline.from_pretrained(
    model_repo_id, 
    torch_dtype=torch_dtype,
    use_auth_token=True  # This will use the token from login()
)
pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
    model_repo_id, 
    subfolder="scheduler", 
    shift=5,
    use_auth_token=True
)
pipe = pipe.to(device)

# For image-to-image
img2img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    model_repo_id,
    torch_dtype=torch_dtype,
    use_auth_token=True
)
img2img_pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
    model_repo_id, 
    subfolder="scheduler", 
    shift=5,
    use_auth_token=True
)
img2img_pipe = img2img_pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


@spaces.GPU(duration=65)
def infer(
        prompt,
        negative_prompt="",
        seed=42,
        randomize_seed=False,
        width=1024,
        height=1024,
        guidance_scale=1.5,
        num_inference_steps=8,
        input_image=None,
        strength=0.8,
        progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    # Text-to-image if no input image is provided
    if input_image is None:
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
        ).images[0]
    # Image-to-image if an input image is provided
    else:
        # Convert to PIL Image if it's a numpy array
        if isinstance(input_image, np.ndarray):
            input_image = Image.fromarray(input_image)

        # Resize image to match requested dimensions
        input_image = input_image.resize((width, height), Image.LANCZOS)

        image = img2img_pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            image=input_image,
            strength=strength,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            generator=generator,
        ).images[0]

    return image, seed


examples = [
    "A capybara wearing a suit holding a sign that reads Hello World",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # TensorArt Stable Diffusion 3.5 Large TurboX")
        gr.Markdown(
            "[8-step distilled turbo model](https://huggingface.co/tensorart/stable-diffusion-3.5-large-TurboX)")
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        # Add image upload component
        input_image = gr.Image(
            label="Input Image (Optional)",
            type="pil",
            sources=["upload", "clipboard"],
        )

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

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
            )

            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():
                width = gr.Slider(
                    label="Width",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=7.5,
                    step=0.1,
                    value=1.5,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=8,
                )

            # Add strength parameter for image-to-image
            strength = gr.Slider(
                label="Strength (for image-to-image)",
                minimum=0.0,
                maximum=1.0,
                step=0.01,
                value=0.8,
                info="How much to transform the reference image. 1.0 means complete transformation."
            )

        gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True,
                    cache_mode="lazy")
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            input_image,
            strength,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()