Update app.py

app.py

@@ -6,6 +6,8 @@ from streamlit_cropper import st_cropper
 
 # Simple app: convert user input into ASCII codes and binary labels
 
+# (functions string_to_binary_labels, clean_image, image_to_binary_labels_rgb, binary_labels_to_rgb_image stay unchanged)
+
 def string_to_binary_labels(s: str) -> list[int]:
     bits: list[int] = []
     for char in s:
@@ -59,14 +61,6 @@ def binary_labels_to_rgb_image(binary_labels: list[int], width: int = None, heig
     img = Image.fromarray(array, mode='RGB')
     return img
 
-# Predefined headers for the 32 mutation sites
-mutation_site_headers = [
-    3244, 3297, 3350, 3399, 3455, 3509, 3562, 3614,
-    3665, 3720, 3773, 3824, 3879, 3933, 3985, 4039,
-    4089, 4145, 4190, 4245, 4298, 4349, 4402, 4455,
-    4510, 4561, 4615, 4668, 4720, 4773, 4828, 4882
-]
-
 # Load thresholds from file
 thresholds = pd.read_csv("Column_Thresholds.csv", index_col=0).squeeze()
 
@@ -175,8 +169,16 @@ with tab3:
         binary_part = edited_df[common_cols].ge(thresholds[common_cols]).astype(int)
         non_binary_part = edited_df.drop(columns=common_cols, errors='ignore')
         binary_df = pd.concat([non_binary_part, binary_part], axis=1)
+
+        def highlight_binary(val):
+            color = 'lightgreen' if val == 1 else 'lightcoral'
+            return f'background-color: {color}'
+
+        styled_binary_df = binary_df.style.applymap(highlight_binary, subset=common_cols)
+
         st.subheader("Binary Labels")
-        st.dataframe(binary_df)
+        st.dataframe(styled_binary_df)
+
         st.download_button(
             label="Download Binary Labels Table as CSV",
             data=binary_df.to_csv(index=False),
@@ -184,4 +186,4 @@ with tab3:
             mime="text/csv"
         )
 
-# Future: integrate DNA editor mapping for each mutation site here
+# Future: integrate DNA editor mapping for each mutation site here