import gradio as gr
from pathlib import Path
import os
from PIL import Image
import torch
import torchvision.transforms as transforms
import requests
import numpy as np

# Preprocessing
from modules import PaletteModelV2
from diffusion import Diffusion_cond


# Check for GPU availability, else use CPU
device = 'cuda' if torch.cuda.is_available() else 'cpu'

model = PaletteModelV2(c_in=2, c_out=1, num_classes=5, image_size=256, device=device, true_img_size=64).to(device)
ckpt = torch.load('ema_ckpt_cond.pt', map_location=torch.device(device))
model.load_state_dict(ckpt)

diffusion = Diffusion_cond(img_size=256, device=device)
model.eval()

transform_hmi = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((256, 256)),
    transforms.RandomVerticalFlip(p=1.0),
    transforms.Normalize(mean=(0.5,), std=(0.5,))
])

def generate_image(seed_image):
    seed_image_tensor = transform_hmi(Image.open(seed_image)).reshape(1, 1, 256, 256).to(device)
    generated_image = diffusion.sample(model, y=seed_image_tensor, labels=None, n=1)
    # generated_image_pil = transforms.ToPILImage()(generated_image.squeeze().cpu())
    img = generated_image[0].reshape(1, 256, 256).permute(1, 2, 0)              # Permute dimensions to height x width x channels
    img = np.squeeze(img.cpu().numpy())
    v = Image.fromarray(img)                # Create a PIL Image from array
    v = v.transpose(Image.FLIP_TOP_BOTTOM)  
    
    return v

# Create Gradio interface
iface = gr.Interface(
    fn=generate_image,
    inputs="file",
    outputs="image",
    title="Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution",
    description="Upload a LoS magnetogram and predict how it is going to be in 24 hours."
)

iface.launch()