Create app_backup.py

app_backup.py

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+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()