Create app.py

app.py

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+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import matplotlib.pyplot as plt
+
+# Load the trained generator model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+generator_A2B = Generator().to(device)
+generator_A2B.load_state_dict(torch.load("generator_A2B.pth", map_location=device))
+generator_A2B.eval()
+
+def transform_image(image):
+    transform = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+    ])
+    return transform(image).unsqueeze(0).to(device)
+
+def generate(image):
+    image = Image.open(image).convert("RGB")
+    input_tensor = transform_image(image)
+    with torch.no_grad():
+        output_tensor = generator_A2B(input_tensor)
+    
+    output_image = (output_tensor.squeeze(0).permute(1, 2, 0).cpu().numpy() + 1) / 2
+    plt.imshow(output_image)
+    plt.axis("off")
+    plt.show()
+    return output_image
+
+# Create Gradio Interface
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.Image(),
+    title="CycleGAN Image Translation",
+    description="Upload an image and get the translated output from the CycleGAN model."
+)
+
+demo.launch()