Update app.py

app.py

@@ -4,25 +4,25 @@ import numpy as np
 import pandas as pd
 from streamlit_cropper import st_cropper
 
-# # Mutation site headers removed 3614,
-# mutation_site_headers = [
-#     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 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, 
-#     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,
-#     4298: 0.868534701, 4349: 1.222907645, 4402: 0.58873557, 4455: 1.185522985,
-#     4510: 1.266797682, 4561: 1.109913024, 4615: 1.181106084, 4668: 1.408533949,
-#     4720: 0.714151142, 4773: 1.471959437, 4828: 0.95879943, 4882: 1.464503885
-# })
+# Mutation site headers removed 3614,
+mutation_site_headers_actual = [
+    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 removed 3614: 0.091557752,
+thresholds_actual = pd.Series({
+    3244: 1.094293328, 3297: 0.924916122, 3350: 0.664586629, 3399: 0.91573613,
+    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,
+    4298: 0.868534701, 4349: 1.222907645, 4402: 0.58873557, 4455: 1.185522985,
+    4510: 1.266797682, 4561: 1.109913024, 4615: 1.181106084, 4668: 1.408533949,
+    4720: 0.714151142, 4773: 1.471959437, 4828: 0.95879943, 4882: 1.464503885
+})
 
 # Mutation site headers reordered: 4402 to 3244, 4882 to 4455
 mutation_site_headers = [
@@ -172,6 +172,14 @@ with tab1:
         st.dataframe(df_31)
         st.download_button("Download as CSV", df_31.to_csv(index=False), "text_32_binary_labels.csv")
 
+        # Additional table with ascending mutation site headers (3244 to 4455)
+        ascending_headers = sorted([h for h in mutation_site_headers if h <= 4455])
+        df_sorted = df[[str(h) for h in ascending_headers if str(h) in df.columns]]
+        st.subheader("Binary Labels (Ascending Order 3244 → 4455)")
+        st.dataframe(df_sorted)
+        st.download_button("Download Ascending Order CSV", df_sorted.to_csv(index=False), "text_binary_labels_ascending.csv")
+
+
         # st.subheader("Binary Labels (27-bit groups)")
         # groups = []
         # for i in range(0, len(binary_labels), 27):
@@ -210,56 +218,99 @@ with tab2:
         st.download_button("Download CSV", df.to_csv(index=False), "image_binary_labels.csv")
 
 # Tab 3: EF → Binary
-with tab3:
+with st.tabs(["Text to Binary Labels", "Image to Binary Labels", "EF → Binary"])[2]:
     st.write("Upload an Editing Frequency CSV or enter manually:")
-    st.write("**Note:** Please upload CSV files **without column headers**. Just the 31 editing frequencies per row.")
+    st.write("**Note:** Please upload CSV files **without column headers**, in ascending order from 3244 to 4455.")
     ef_file = st.file_uploader("Upload EF CSV", type=["csv"], key="ef")
-    
+
+    ascending_input_headers = sorted([h for h in mutation_site_headers if 3244 <= h <= 4455])
+
     if ef_file:
-        # Read CSV without headers and assign mutation site headers
         ef_df = pd.read_csv(ef_file, header=None)
-        ef_df.columns = [str(site) for site in mutation_site_headers]
+        ef_df.columns = [str(site) for site in ascending_input_headers]
     else:
-        ef_df = pd.DataFrame(columns=[str(site) for site in mutation_site_headers])
-
+        ef_df = pd.DataFrame(columns=[str(site) for site in ascending_input_headers])
 
     edited_df = st.data_editor(ef_df, num_rows="dynamic")
 
     if st.button("Convert to Binary Labels"):
-        int_map = {str(k): k for k in thresholds.index}
-        matching_cols = [col for col in edited_df.columns if col in int_map]
-
+        # Use ascending headers to create binary first
         binary_part = pd.DataFrame()
-        for col in matching_cols:
-            col_threshold = thresholds[int_map[col]]
-            binary_part[col] = (edited_df[col].astype(float) >= col_threshold).astype(int)
+        for col in ascending_input_headers:
+            col_str = str(col)
+            threshold = thresholds[col]
+            binary_part[col_str] = (edited_df[col_str].astype(float) >= threshold).astype(int)
 
-        non_binary_part = edited_df.drop(columns=matching_cols, errors='ignore')
-        binary_df = pd.concat([non_binary_part, binary_part], axis=1)
+        # Rearranged for output: custom order from mutation_site_headers
+        binary_reordered = binary_part[[str(h) for h in mutation_site_headers if str(h) in binary_part.columns]]
 
         def color_binary(val):
             if val == 1: return "background-color: lightgreen"
             if val == 0: return "background-color: lightcoral"
             return ""
 
-        st.subheader("Binary Labels")
-        styled = binary_df.style.applymap(color_binary, subset=matching_cols)
+        st.subheader("Binary Labels (Reordered 4402→3244, 4882→4455)")
+        styled = binary_reordered.style.applymap(color_binary)
         st.dataframe(styled)
-        st.download_button("Download CSV", binary_df.to_csv(index=False), "ef_binary_labels.csv")
-
-        # Convert to bitstrings and strings
-        binary_strings = []
-        decoded_strings = []
-        for _, row in binary_part.iterrows():
-            bitlist = row.values.tolist()
-            bitstring = ''.join(str(b) for b in bitlist)
-            binary_strings.append(bitstring)
-            decoded_strings.append(binary_labels_to_string(bitlist))
-
-        st.subheader("Binary as Bitstrings")
-        for b in binary_strings:
-            st.code(b)
-
-        st.subheader("Decoded Voyager Strings")
-        for s in decoded_strings:
-            st.write(s)
+        st.download_button("Download CSV", binary_reordered.to_csv(index=False), "ef_binary_labels.csv")
+
+        # Reconstruct original string from binary values (flatten row-wise)
+        for i, row in binary_reordered.iterrows():
+            binary_sequence = row.tolist()
+            text = binary_labels_to_string(binary_sequence)
+            st.write(f"Row {i+1} decoded string: {text}")
+
+# # 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 31 editing frequencies per row.")
+#     ef_file = st.file_uploader("Upload EF CSV", type=["csv"], key="ef")
+    
+#     if ef_file:
+#         # Read CSV without headers and assign mutation site headers
+#         ef_df = pd.read_csv(ef_file, header=None)
+#         ef_df.columns = [str(site) for site in mutation_site_headers]
+#     else:
+#         ef_df = pd.DataFrame(columns=[str(site) for site in mutation_site_headers])
+
+
+#     edited_df = st.data_editor(ef_df, num_rows="dynamic")
+
+#     if st.button("Convert to Binary Labels"):
+#         int_map = {str(k): k for k in thresholds.index}
+#         matching_cols = [col for col in edited_df.columns if col in int_map]
+
+#         binary_part = pd.DataFrame()
+#         for col in matching_cols:
+#             col_threshold = thresholds[int_map[col]]
+#             binary_part[col] = (edited_df[col].astype(float) >= col_threshold).astype(int)
+
+#         non_binary_part = edited_df.drop(columns=matching_cols, errors='ignore')
+#         binary_df = pd.concat([non_binary_part, binary_part], axis=1)
+
+#         def color_binary(val):
+#             if val == 1: return "background-color: lightgreen"
+#             if val == 0: return "background-color: lightcoral"
+#             return ""
+
+#         st.subheader("Binary Labels")
+#         styled = binary_df.style.applymap(color_binary, subset=matching_cols)
+#         st.dataframe(styled)
+#         st.download_button("Download CSV", binary_df.to_csv(index=False), "ef_binary_labels.csv")
+
+#         # Convert to bitstrings and strings
+#         binary_strings = []
+#         decoded_strings = []
+#         for _, row in binary_part.iterrows():
+#             bitlist = row.values.tolist()
+#             bitstring = ''.join(str(b) for b in bitlist)
+#             binary_strings.append(bitstring)
+#             decoded_strings.append(binary_labels_to_string(bitlist))
+
+#         st.subheader("Binary as Bitstrings")
+#         for b in binary_strings:
+#             st.code(b)
+
+#         st.subheader("Decoded Voyager Strings")
+#         for s in decoded_strings:
+#             st.write(s)