Update app.py

app.py

@@ -79,12 +79,11 @@ def enhance_image(input_image):
     # Convert tensor → numpy
     output = output.squeeze().permute(1, 2, 0).cpu().numpy()
 
-    # Handle range
+    # Handle range safely
     if output.min() < 0:
         output = (output + 1) / 2
 
     output = np.clip(output, 0, 1)
-
     output_img = (output * 255).astype(np.uint8)
 
     # Resize back
@@ -93,10 +92,10 @@ def enhance_image(input_image):
     output_img = np.array(output_img)
 
     # ---------------------------
-    # FINAL SAFE POST-PROCESSING
+    # FINAL BALANCED PROCESSING
     # ---------------------------
 
-    # 1. Light smoothing (remove artifacts)
+    # 1. Very light smoothing (remove artifacts)
     output_img = cv2.GaussianBlur(output_img, (3, 3), 0)
 
     # 2. Mild sharpening (safe)
@@ -107,19 +106,14 @@ def enhance_image(input_image):
     ])
     output_img = cv2.filter2D(output_img, -1, sharpen_kernel)
 
-    # 3. Blend with original (VERY IMPORTANT)
+    # 3. Color-safe blending (MOST IMPORTANT)
     original_np = np.array(img.resize(original_size))
-    output_img = cv2.addWeighted(original_np, 0.7, output_img, 0.3, 0)
-
-    # 4. Adaptive contrast (CLAHE - BEST FINAL TOUCH)
-    lab = cv2.cvtColor(output_img, cv2.COLOR_RGB2LAB)
-    l, a, b = cv2.split(lab)
-
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-    l = clahe.apply(l)
+    output_img = cv2.addWeighted(original_np, 0.8, output_img, 0.2, 0)
 
-    lab = cv2.merge((l, a, b))
-    output_img = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
+    # 4. Very light contrast (SAFE — no color shift)
+    alpha = 1.05  # slight contrast
+    beta = 2      # slight brightness
+    output_img = cv2.convertScaleAbs(output_img, alpha=alpha, beta=beta)
 
     output_img = np.clip(output_img, 0, 255)