Update app.py

app.py

@@ -1,31 +1,27 @@
-import torch
 import gradio as gr
-from PIL import Image
-from transformers import AutoImageProcessor, AutoModelForImageProcessing
+from PIL import Image, ImageEnhance
 
-# Load model and processor
-processor = AutoImageProcessor.from_pretrained("eugenesiow/EnhanceNet")
-model = AutoModelForImageProcessing.from_pretrained("eugenesiow/EnhanceNet").to('cpu')
-
-def enhance_image(image):
-    # Convert PIL Image to tensor
-    inputs = processor(images=image, return_tensors="pt").to('cpu')
+def enhance_vibrancy(image):
+    # Convert to RGB if needed
+    image = image.convert("RGB")
+    
+    # Enhance color saturation
+    enhancer = ImageEnhance.Color(image)
+    vibrant_image = enhancer.enhance(2.0)  # 2.0x saturation
     
-    # Forward pass
-    with torch.no_grad():
-        outputs = model(**inputs)
+    # Enhance brightness slightly
+    enhancer = ImageEnhance.Brightness(vibrant_image)
+    vibrant_image = enhancer.enhance(1.2)
     
-    # Convert tensor back to PIL Image
-    enhanced_image = processor.post_process_output(outputs.pixel_values)
-    return enhanced_image[0]
+    return vibrant_image
 
 # Gradio Interface
 iface = gr.Interface(
-    fn=enhance_image,
-    inputs=gr.Image(type="pil", label="Upload Dull Image"),
+    fn=enhance_vibrancy,
+    inputs=gr.Image(type="pil", label="Original Image"),
     outputs=gr.Image(type="pil", label="Vibrant Image"),
-    title="Dull to Vibrant Image Enhancer",
-    description="Enhance dull images using EnhanceNet on CPU. For faster GPU processing, duplicate this Space and enable GPU in settings."
+    title="Simple Image Vibrancy Booster",
+    description="Enhance image vibrancy using basic color adjustments (no AI involved)"
 )
 
 if __name__ == "__main__":