Create app.py

app.py

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+import gradio as gr
+import torch
+from torchvision.transforms import ToTensor, ToPILImage
+from PIL import Image
+import numpy as np
+import cv2
+
+# Load a lightweight model (example: ESRGAN)
+model = torch.hub.load('facebookresearch/esrgan', 'esrgan', pretrained=True)
+model.eval()
+
+def enhance_image(input_img):
+    # Convert to PIL Image if not already
+    if isinstance(input_img, np.ndarray):
+        input_img = Image.fromarray(input_img)
+    
+    # Preprocess
+    input_tensor = ToTensor()(input_img).unsqueeze(0)
+    
+    # Enhance (disable gradients for CPU)
+    with torch.no_grad():
+        output_tensor = model(input_tensor)
+    
+    # Convert back to PIL Image
+    output_img = ToPILImage()(output_tensor.squeeze(0))
+    return output_img
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=enhance_image,
+    inputs=gr.Image(label="Upload Image"),
+    outputs=gr.Image(label="Enhanced Image"),
+    title="🚀 Image Enhancer (CPU)",
+    description="Upload an image to enhance its quality (runs on CPU)."
+)
+
+iface.launch()