Update app.py

app.py

@@ -1,31 +1,41 @@
 import gradio as gr
-from basicsr.archs.rrdbnet_arch import RRDBNet
+import numpy as np
+from PIL import Image
 from realesrgan import RealESRGANer
+from basicsr.archs.rrdbnet_arch import RRDBNet
 
 # Initialize Real-ESRGAN
-model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
-upsampler = RealESRGANer(
-    scale=4,
-    model_path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
-    model=model,
-    tile=400,  # Reduce if you get memory errors
-    tile_pad=10
-)
+def init_enhancer():
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
+    return RealESRGANer(
+        scale=4,
+        model_path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+        model=model,
+        tile=400,
+        tile_pad=10,
+        pre_pad=0,
+        half=False
+    )
 
-def enhance(image):
-    # Convert to RGB if needed
-    if image.mode != 'RGB':
-        image = image.convert('RGB')
+enhancer = init_enhancer()
+
+def enhance_image(input_img):
+    # Convert to numpy array
+    img_array = np.array(input_img)
     
     # Enhance image
-    output, _ = upsampler.enhance(np.array(image), outscale=4)
+    output, _ = enhancer.enhance(img_array, outscale=2)  # 2x upscale
+    
     return Image.fromarray(output)
 
+# Gradio interface
 demo = gr.Interface(
-    fn=enhance,
+    fn=enhance_image,
     inputs=gr.Image(type="pil", label="Input Image"),
-    outputs=gr.Image(type="pil", label="Enhanced (4x)"),
-    title="Professional Image Enhancement"
+    outputs=gr.Image(type="pil", label="Enhanced"),
+    title="Professional Image Enhancement",
+    examples=["example.jpg"] if os.path.exists("example.jpg") else None,
+    allow_flagging="never"
 )
 
 demo.launch()