import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image, ImageFilter
import gradio as gr
import numpy as np
import os
import uuid

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

resize_transform = transforms.Resize((512, 512))

def load_image(image):
    image = Image.fromarray(image).convert('RGB')
    image = transform(image)
    return image.unsqueeze(0).to(device)

def interpolate_vectors(v1, v2, num_steps):
    return [v1 * (1 - alpha) + v2 * alpha for alpha in np.linspace(0, 1, num_steps)]

def infer_and_interpolate(image1, image2, num_interpolations=24):
    image1 = load_image(image1)
    image2 = load_image(image2)

    with torch.no_grad():
        mu1, logvar1 = model.encode(image1)
        mu2, logvar2 = model.encode(image2)
        interpolated_vectors = interpolate_vectors(mu1, mu2, num_interpolations)
        decoded_images = [model.decode(vec).squeeze(0) for vec in interpolated_vectors]

    return decoded_images

def create_gif(decoded_images, duration=200, apply_blur=False):
    reversed_images = decoded_images[::-1]
    all_images = decoded_images + reversed_images

    pil_images = []
    for img in all_images:
        img = (img - img.min()) / (img.max() - img.min())
        img = (img * 255).byte()
        pil_img = transforms.ToPILImage()(img.cpu()).convert("RGB")
        pil_img = resize_transform(pil_img)
        if apply_blur:
            pil_img = pil_img.filter(ImageFilter.GaussianBlur(radius=1))
        pil_images.append(pil_img)

    gif_filename = f"/tmp/morphing_{uuid.uuid4().hex}.gif"
    pil_images[0].save(gif_filename, save_all=True, append_images=pil_images[1:], duration=duration, loop=0)

    return gif_filename

def create_morphing_gif(image1, image2, num_interpolations=24, duration=200):
    decoded_images = infer_and_interpolate(image1, image2, num_interpolations)
    gif_path = create_gif(decoded_images, duration)
    
    return gif_path

examples = [
    ["example_images/image1.jpg", "example_images/image2.png", 24, 200],
    ["example_images/image3.jpg", "example_images/image4.jpg", 30, 150],
]

with gr.Blocks() as morphing:
    with gr.Column():
        with gr.Column():
            num_interpolations = gr.Slider(minimum=2, maximum=50, value=24, step=1, label="Number of interpolations")
            duration = gr.Slider(minimum=100, maximum=1000, value=200, step=50, label="Duration per frame (ms)")
            generate_button = gr.Button("Generate Morphing GIF")
        output_gif = gr.Image(label="Morphing GIF")
        with gr.Row():
            image1 = gr.Image(label="Upload first image", type="numpy")
            image2 = gr.Image(label="Upload second image", type="numpy")
            
    generate_button.click(fn=create_morphing_gif, inputs=[image1, image2, num_interpolations, duration], outputs=output_gif)

    gr.Examples(examples=examples, inputs=[image1, image2, num_interpolations, duration])