import gradio as gr
import torch, 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 = 'johnowhitaker/sd-class-wikiart-from-bedrooms'
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

def monochromatic_loss(images, threshold=0.5, target_value=0.01):
    # Convert images to grayscale (simple average of channels)
    # We assume images are [N, C, H, W] where C=3 (RGB)
    grayscale_images = (images[:,0,:,:] + images[:,1,:,:] + images[:,2,:,:]) / 3.0

    # Penalize pixels that are not close to black or white
    # Encourage values close to target_value (e.g., 0.01 for black) or 1.0 (for white)
    # This creates a strong push towards high contrast
    loss_black = torch.abs(grayscale_images - target_value)
    loss_white = torch.abs(grayscale_images - (1.0 - target_value))

    # For each pixel, take the minimum deviation from either black or white
    min_deviation = torch.min(loss_black, loss_white)

    # We want to minimize this deviation across the image
    loss = min_deviation.mean()
    return loss

# 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
    loss  = monochromatic_loss(x0)
    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 = Image.fromarray(np.array(im*255).astype(np.uint8))
  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],["#44CCAA", 5] # You can provide some example inputs to get people started
    ],
)

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