import gradio as gr

import torch
from PIL import Image
import numpy as np
import random
from einops import rearrange
import matplotlib.pyplot as plt
from torchvision.transforms import v2

from model import MAE_ViT, MAE_Encoder, MAE_Decoder, MAE_Encoder_FeatureExtractor

path  = [['images/cat.jpg'], ['images/dog.jpg'], ['images/horse.jpg'], ['images/airplane.jpg'], ['images/truck.jpg']]
model_name = "vit-t-mae-pretrain.pt"
model = torch.load(model_name, map_location='cpu')

model.eval()
device = torch.device("cpu")
model.to(device)

transform = v2.Compose([
        v2.Resize((32, 32)),
        v2.ToTensor(), 
        v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
    ])

# Load and Preprocess the Image
def load_image(image_path, transform):
    img = Image.open(image_path).convert('RGB')
    img = transform(img).unsqueeze(0)  # Add batch dimension
    return img

def show_image(img, title):
    img = rearrange(img, "c h w -> h w c")
    img = (img.cpu().detach().numpy() + 1) / 2  # Normalize to [0, 1]

    plt.imshow(img)
    plt.axis('off')
    plt.title(title)

# Visualize a Single Image
def visualize_single_image(image_path):
    img = load_image(image_path, transform).to(device)
    
    # Run inference
    model.eval()
    with torch.no_grad():
        predicted_img, mask = model(img)
    
    # Convert the tensor back to a displayable image
    # masked image
    im_masked = img * (1 - mask)

    # MAE reconstruction pasted with visible patches
    im_paste = img * (1 - mask) + predicted_img * mask

    # resize the image to 96 x 96
    img = v2.functional.resize(img[0], (96, 96))
    im_masked = v2.functional.resize(im_masked[0], (96, 96))
    predicted_img = v2.functional.resize(predicted_img[0], (96, 96))
    im_paste = v2.functional.resize(im_paste[0], (96, 96))
    
    # make the plt figure larger
    plt.figure(figsize=(18, 8))

    plt.subplot(1, 4, 1)
    show_image(img, "original")

    plt.subplot(1, 4, 2)
    show_image(im_masked, "masked")

    plt.subplot(1, 4, 3)
    show_image(predicted_img, "reconstruction")

    plt.subplot(1, 4, 4)
    show_image(im_paste, "reconstruction + visible")

    plt.tight_layout()

    # convert the plt figure to a numpy array
    plt.savefig("output.png")

    return np.array(plt.imread("output.png"))

inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]

outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]

gr.Interface(
    fn=visualize_single_image,
    inputs=inputs_image,
    outputs=outputs_image,
    examples=path,
    title="MAE-ViT Image Reconstruction",
    description="This is a demo of the MAE-ViT model for image reconstruction.",
).launch()