Update app.py

app.py

@@ -4,18 +4,18 @@ import numpy as np
 import pandas as pd
 from streamlit_cropper import st_cropper
 
-# Mutation site headers
+# Mutation site headers removed 3614,
 mutation_site_headers = [
-    3244, 3297, 3350, 3399, 3455, 3509, 3562, 3614,
+    3244, 3297, 3350, 3399, 3455, 3509, 3562, 
     3665, 3720, 3773, 3824, 3879, 3933, 3985, 4039,
     4089, 4145, 4190, 4245, 4298, 4349, 4402, 4455,
     4510, 4561, 4615, 4668, 4720, 4773, 4828, 4882
 ]
 
-# Thresholds for each mutation site
+# Thresholds for each mutation site removed 3614: 0.091557752,
 thresholds = pd.Series({
     3244: 1.094293328, 3297: 0.924916122, 3350: 0.664586629, 3399: 0.91573613,
-    3455: 1.300869714, 3509: 1.821975901, 3562: 1.178862418, 3614: 0.091557752,
+    3455: 1.300869714, 3509: 1.821975901, 3562: 1.178862418, 
     3665: 0.298697327, 3720: 0.58379781, 3773: 0.891088481, 3824: 1.145509641,
     3879: 0.81833191, 3933: 2.93084335, 3985: 1.593758847, 4039: 0.966055013,
     4089: 1.465671338, 4145: 0.30309335, 4190: 1.321615138, 4245: 1.709752495,
@@ -58,6 +58,7 @@ def binary_labels_to_string(bits: list[int]) -> str:
         val = sum(b << (5 - j) for j, b in enumerate(chunk))
         chars.append(voyager_table.get(val, '?'))
     return ''.join(chars)
+    
 # def string_to_binary_labels(s: str) -> list[int]:
 #     bits = []
 #     for char in s:
@@ -141,16 +142,27 @@ with tab1:
         for i, bits in enumerate(grouped):
             st.write(f"'{user_input[i]}' → {bits}")
 
-        st.subheader("Binary Labels (32-bit groups)")
+        st.subheader("Binary Labels (31-bit groups)")
         groups = []
-        for i in range(0, len(binary_labels), 32):
-            group = binary_labels[i:i+32]
-            group += [0] * (32 - len(group))
+        for i in range(0, len(binary_labels), 31):
+            group = binary_labels[i:i+31]
+            group += [0] * (31 - len(group))
             groups.append(group + [sum(group)])
 
-        df = pd.DataFrame(groups, columns=[str(h) for h in mutation_site_headers] + ["Edited Sites"])
-        st.dataframe(df)
-        st.download_button("Download as CSV", df.to_csv(index=False), "text_binary_labels.csv")
+        df_31 = pd.DataFrame(groups, columns=[str(h) for h in mutation_site_headers] + ["Edited Sites"])
+        st.dataframe(df_31)
+        st.download_button("Download as CSV", df_31.to_csv(index=False), "text_32_binary_labels.csv")
+
+        st.subheader("Binary Labels (27-bit groups)")
+        groups = []
+        for i in range(0, len(binary_labels), 27):
+            group = binary_labels[i:i+27]
+            group += [0] * (27 - len(group))
+            groups.append(group + [sum(group)])
+
+        df_27 = pd.DataFrame(groups, columns=[str(h) for h in mutation_site_headers] + ["Edited Sites"])
+        st.dataframe(df_27)
+        st.download_button("Download as CSV", df_27.to_csv(index=False), "text_27_binary_labels.csv")
 
 # Tab 2: Image to Binary
 with tab2:
@@ -181,7 +193,7 @@ with tab2:
 # Tab 3: EF → Binary
 with tab3:
     st.write("Upload an Editing Frequency CSV or enter manually:")
-    st.write("**Note:** Please upload CSV files **without column headers**. Just the 32 editing frequencies per row.")
+    st.write("**Note:** Please upload CSV files **without column headers**. Just the 31 editing frequencies per row.")
     ef_file = st.file_uploader("Upload EF CSV", type=["csv"], key="ef")
     
     if ef_file: