import gradio as gr
import torch
import torchvision
import torch.nn.functional as F
import numpy as np
from PIL import Image, ImageColor
from diffusers import DDPMPipeline
from diffusers import DDIMScheduler

device = 'mps' if torch.backends.mps.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu'

# Load the pretrained pipeline
pipeline_name = 'muneebable/ddpm-celebahq-finetuned-anime-art'
image_pipe = DDPMPipeline.from_pretrained(pipeline_name).to(device)

# Set up the scheduler
scheduler = DDIMScheduler.from_pretrained(pipeline_name)
scheduler.set_timesteps(num_inference_steps=20)

# The guidance function 
def color_loss(images, target_color=(0.1, 0.9, 0.5)):
    """Given a target color (R, G, B) return a loss for how far away on average 
    the images' pixels are from that color. Defaults to a light teal: (0.1, 0.9, 0.5) """
    target = torch.tensor(target_color).to(images.device) * 2 - 1 # Map target color to (-1, 1)
    target = target[None, :, None, None] # Get shape right to work with the images (b, c, h, w)
    error = torch.abs(images - target).mean() # Mean absolute difference between the image pixels and the target color
    return error

# And the core function to generate an image given the relevant inputs
def generate(color, guidance_loss_scale):
    target_color = ImageColor.getcolor(color, "RGB") # Target color as RGB
    target_color = [a/255 for a in target_color] # Rescale from (0, 255) to (0, 1)
    x = torch.randn(1, 3, 256, 256).to(device) 
    for i, t in enumerate(scheduler.timesteps):
        model_input = scheduler.scale_model_input(x, t)
        with torch.no_grad():
            noise_pred = image_pipe.unet(model_input, t)["sample"]
        x = x.detach().requires_grad_()
        x0 = scheduler.step(noise_pred, t, x).pred_original_sample
        loss = color_loss(x0, target_color) * guidance_loss_scale
        cond_grad = -torch.autograd.grad(loss, x)[0]
        x = x.detach() + cond_grad 
        x = scheduler.step(noise_pred, t, x).prev_sample
    grid = torchvision.utils.make_grid(x, nrow=4)
    im = grid.permute(1, 2, 0).cpu().clip(-1, 1)*0.5 + 0.5
    im = (im * 255).byte().numpy()  # Convert to uint8 numpy array
    im = Image.fromarray(im)
    im.save('test.jpeg')
    return im

# See the gradio docs for the types of inputs and outputs available
inputs = [
    gr.ColorPicker(label="color", value='55FFAA'), # Add any inputs you need here
    gr.Slider(label="guidance_scale", minimum=0, maximum=30, value=3)
]
outputs = gr.Image(label="result")

# Setting up a minimal interface to our function:
demo = gr.Interface(
    fn=generate,
    inputs=inputs,
    outputs=outputs,
    examples=[
        ["#BB2266", 3, 'assets/image.webp'],["#44CCAA", 5, 'assets/image (1).webp'] # You can provide some example inputs to get people started
    ],
)

# And launching
if __name__ == "__main__":
    demo.launch()  # Removed enable_queue=True