Update app.py

app.py

@@ -54,18 +54,18 @@ def display_image(image_path):
                             img_data = (img_data - np.min(img_data)) / (np.max(img_data) - np.min(img_data)) * 255
                             img_data = img_data.astype(np.uint8)
                         
-                        st.image(img_data, use_column_width=True)
+                        st.image(img_data, use_container_width=True)
                 except Exception as rasterio_error:
                     # Fall back to PIL
                     try:
                         img = Image.open(image_path)
-                        st.image(img, use_column_width=True)
+                        st.image(img, use_container_width=True)
                     except Exception as pil_error:
                         st.error(f"Failed to load image: {str(pil_error)}")
             else:
                 # Use PIL for other formats
                 img = Image.open(image_path)
-                st.image(img, use_column_width=True)
+                st.image(img, use_container_width=True)
         else:
             st.info(f"Image file not found: {image_path}")
     except Exception as e:
@@ -1560,7 +1560,7 @@ if st.session_state.app_mode == "SAR Colorization":
             # Display legend
             st.markdown("<h4 style='color: #a78bfa; margin-top: 20px;'>Land Cover Classes</h4>", unsafe_allow_html=True)
             legend_img = create_legend()
-            st.image(legend_img, use_column_width=True)
+            st.image(legend_img, use_container_width=True)
             
             st.markdown("</div>", unsafe_allow_html=True)
         else:
@@ -1632,7 +1632,7 @@ if st.session_state.app_mode == "SAR Colorization":
                                     
                                     # Display results with metrics
                                     st.markdown("<h4 style='color: #a78bfa;'>Segmentation Results with Ground Truth</h4>", unsafe_allow_html=True)
-                                    st.image(result_buf, use_column_width=True)
+                                    st.image(result_buf, use_container_width=True)
                                     
                                     # Calculate metrics
                                     pred_class = np.argmax(prediction[0], axis=-1)
@@ -1668,7 +1668,7 @@ if st.session_state.app_mode == "SAR Colorization":
                                     # Fall back to regular visualization without ground truth
                                     result_img = visualize_prediction(prediction, np.expand_dims(sar_normalized, axis=-1))
                                     st.markdown("<h4 style='color: #a78bfa;'>Segmentation Results</h4>", unsafe_allow_html=True)
-                                    st.image(result_img, use_column_width=True)
+                                    st.image(result_img, use_container_width=True)
                                     
                                     # Add download button for the result
                                     btn = st.download_button(
@@ -1682,7 +1682,7 @@ if st.session_state.app_mode == "SAR Colorization":
                                 # Regular visualization without ground truth
                                 result_img = visualize_prediction(prediction, np.expand_dims(sar_normalized, axis=-1))
                                 st.markdown("<h4 style='color: #a78bfa;'>Segmentation Results</h4>", unsafe_allow_html=True)
-                                st.image(result_img, use_column_width=True)
+                                st.image(result_img, use_container_width=True)
                                 
                                 # Add download button for the result
                                 btn = st.download_button(
@@ -1927,7 +1927,7 @@ if st.session_state.app_mode == "SAR Colorization":
                         if 'accuracy' in img_data:
                             st.markdown(f"<p style='color: #a78bfa;'>Pixel Accuracy: {img_data['accuracy']:.2f}%</p>", unsafe_allow_html=True)
                         
-                        st.image(img_data['result'], use_column_width=True)
+                        st.image(img_data['result'], use_container_width=True)
                         st.markdown("<hr style='border-color: rgba(147, 51, 234, 0.3);'>", unsafe_allow_html=True)
                 else:
                     st.warning("No images were successfully processed.")
@@ -2057,7 +2057,7 @@ if st.session_state.app_mode == "SAR Colorization":
                                             # Direct mapping if values are already 0-10
                                             colored_label[label_data == class_idx] = color
                                     
-                                    st.image(colored_label, use_column_width=True)
+                                    st.image(colored_label, use_container_width=True)
                             else:
                                 # For regular image formats
                                 display_image(label_path)
@@ -2107,7 +2107,7 @@ if st.session_state.app_mode == "SAR Colorization":
                                 
                                 # Display results with metrics
                                 st.markdown("<h4 style='color: #a78bfa;'>Segmentation Results with Ground Truth</h4>", unsafe_allow_html=True)
-                                st.image(result_buf, use_column_width=True)
+                                st.image(result_buf, use_container_width=True)
                                 
                                 # Calculate metrics
                                 pred_class = np.argmax(prediction[0], axis=-1)
@@ -2142,7 +2142,7 @@ if st.session_state.app_mode == "SAR Colorization":
                                 # Regular visualization without ground truth
                                 result_img = visualize_prediction(prediction, np.expand_dims(sar_normalized, axis=-1))
                                 st.markdown("<h4 style='color: #a78bfa;'>Segmentation Results</h4>", unsafe_allow_html=True)
-                                st.image(result_img, use_column_width=True)
+                                st.image(result_img, use_container_width=True)
                                 
                                 # Add download button for the result
                                 btn = st.download_button(
@@ -2389,30 +2389,30 @@ elif st.session_state.app_mode == "SAR to Optical Translation":
                                         
                                         with col1:
                                             st.subheader("Original SAR Image")
-                                            st.image(sar_rgb, use_column_width=True)
+                                            st.image(sar_rgb, use_container_width=True)
                                         
                                         with col2:
                                             st.subheader("Predicted Segmentation")
-                                            st.image(colored_pred, use_column_width=True)
+                                            st.image(colored_pred, use_container_width=True)
                                         
                                         with col3:
                                             st.subheader("Colorized SAR")
-                                            st.image(overlay, use_column_width=True)
+                                            st.image(overlay, use_container_width=True)
                                 else:
                                     # Regular 3-panel visualization without ground truth
                                     col1, col2, col3 = st.columns(3)
                                     
                                     with col1:
                                         st.subheader("Original SAR Image")
-                                        st.image(sar_rgb, use_column_width=True)
+                                        st.image(sar_rgb, use_container_width=True)
                                     
                                     with col2:
                                         st.subheader("Predicted Segmentation")
-                                        st.image(colored_pred, use_column_width=True)
+                                        st.image(colored_pred, use_container_width=True)
                                     
                                     with col3:
                                         st.subheader("Colorized SAR")
-                                        st.image(overlay, use_column_width=True)
+                                        st.image(overlay, use_container_width=True)
                                 
                                 # Display colorized image if available
                                 if colorized_img is not None:
@@ -2864,7 +2864,7 @@ elif st.session_state.app_mode == "SAR to Optical Translation":
                                 st.markdown("</div>", unsafe_allow_html=True)
 
                         
-                        st.image(result['result'], use_column_width=True)
+                        st.image(result['result'], use_container_width=True)
                         
                         # Add download button for individual result
                         col1, col2 = st.columns(2)
@@ -3084,20 +3084,20 @@ elif st.session_state.app_mode == "SAR to Optical Translation":
                                     
                                     with col1:
                                         st.subheader("Original SAR Image")
-                                        st.image(sar_rgb, use_column_width=True)
+                                        st.image(sar_rgb, use_container_width=True)
                                     
                                     with col2:
                                         st.subheader("Predicted Segmentation")
-                                        st.image(colored_pred, use_column_width=True)
+                                        st.image(colored_pred, use_container_width=True)
                                     
                                     with col3:
                                         st.subheader("Colorized SAR")
                                         if colorized_img is not None:
                                             # Convert from -1,1 to 0,1 range
                                             colorized_display = (colorized_img * 0.5) + 0.5
-                                            st.image(colorized_display, use_column_width=True)
+                                            st.image(colorized_display, use_container_width=True)
                                         else:
-                                            st.image(overlay, use_column_width=True)
+                                            st.image(overlay, use_container_width=True)
                                 
                                 # Add download buttons
                                 col1, col2 = st.columns(2)