Update app.py

"이미지가 옆으로 나열되어 보이게"

app.py

@@ -4,10 +4,8 @@ import cv2
 import numpy as np
 from PIL import Image, ExifTags
 
-# 가장 먼저 set_page_config() 호출
 st.set_page_config(page_title="딥페이크 사전 방지 필터(테스트)", layout="wide")
 
-
 ga_code = """
 <!-- Global site tag (gtag.js) - Google Analytics -->
 <script async src="https://www.googletagmanager.com/gtag/js?id=G-PZPBGNENQG"></script>
@@ -18,11 +16,8 @@ ga_code = """
   gtag('config', 'G-PZPBGNENQG');
 </script>
 """
-
-# Streamlit에 GA 코드 삽입
 components.html(ga_code, height=0)
 
-# 나머지 Streamlit 코드
 st.title("딥페이크 사전 방지 필터(테스트)")
 st.markdown("")
 st.markdown("<span style='font-size: 18px;'>안녕하세요! 저희는 딥페이크로부터 여러분의 사진을 보호하는 솔루션을 개발하고 있습니다.</span>", unsafe_allow_html=True)
@@ -113,37 +108,31 @@ st.markdown(
         color: #FF0080;
         text-decoration: none;
     }
+    @media only screen and (max-width: 768px) {
+        .column-container {
+            display: flex;
+            flex-direction: row;
+            justify-content: center;
+        }
+    }
     </style>
     """,
     unsafe_allow_html=True,
 )
 
 def change_hair_to_blonde(image):
-    # Convert to OpenCV format
     image = np.array(image)
-    # Convert the image to HSV color space
     hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
-
-    # Define the range for hair color (dark colors)
     lower_hair = np.array([0, 0, 0])
     upper_hair = np.array([180, 255, 30])
-
-    # Create a mask for hair
     mask = cv2.inRange(hsv, lower_hair, upper_hair)
-
-    # Change hair color to blonde (light yellow)
     hsv[mask > 0] = (30, 255, 200)
-
-    # Convert back to RGB color space
     image_blonde = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
     return image_blonde
 
 def add_noise(image):
-    # Convert to OpenCV format
     image_np = np.array(image)
-    # Generate random noise
     noise = np.random.normal(0, 25, image_np.shape).astype(np.uint8)
-    # Add noise to the image
     noisy_image = cv2.add(image_np, noise)
     return noisy_image
 
@@ -173,7 +162,6 @@ if uploaded_file is not None:
     
     st.write("이미지 처리 중...")
 
-    # Save the original image as a numpy array
     image_np = np.array(image)
 
     col1, col2 = st.columns(2)