Update app.py

app.py

@@ -79,34 +79,38 @@ def enhance_image(input_image):
     with torch.no_grad():
         output = model(input_tensor)
 
+    # Model output
     output_img = output.squeeze().permute(1,2,0).cpu().numpy()
     output_img = (output_img * 255).astype(np.uint8)
 
-    # Resize back to original size
+    # Resize back
     output_img = Image.fromarray(output_img)
     output_img = output_img.resize(original_size, Image.BICUBIC)
     output_img = np.array(output_img)
 
-    # Sharpen image (balanced)
-    # 🔥 STEP 1: Light smoothing (removes artifacts)
+    # 🔥 ORIGINAL IMAGE (for blending)
+    original_np = np.array(img)
+
+    # 🔥 STEP 1: LIGHT SMOOTHING (remove artifacts)
     output_img = cv2.GaussianBlur(output_img, (3,3), 0)
 
-# 🔥 STEP 2: VERY mild sharpening (natural look)
+    # 🔥 STEP 2: BLEND WITH ORIGINAL (VERY IMPORTANT)
+    final_img = cv2.addWeighted(original_np, 0.6, output_img, 0.4, 0)
+
+    # 🔥 STEP 3: VERY LIGHT SHARPEN (safe)
     sharpen_kernel = np.array([[0,-1,0],
-                           [-1,5,-1],
-                           [0,-1,0]])
+                               [-1,5,-1],
+                               [0,-1,0]])
 
-    output_img = cv2.filter2D(output_img, -1, sharpen_kernel)
+    final_img = cv2.filter2D(final_img, -1, sharpen_kernel)
 
-    # Clip values
-    output_img = np.clip(output_img, 0, 255)
+    final_img = np.clip(final_img, 0, 255)
 
     # Save for download
     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
-    Image.fromarray(output_img).save(temp_file.name)
-
-    return output_img, temp_file.name
+    Image.fromarray(final_img).save(temp_file.name)
 
+    return final_img, temp_file.name
 # ---------------------------
 # GRADIO UI
 # ---------------------------