Update app.py

app.py

@@ -393,7 +393,7 @@ with tab5:
     st.header("Decode Binary Labels to String")
 
     # Utility: Track source volumes and update if exceeds limit
-    def track_and_replace_source(source_list, robot_script, volume_limit=170):
+    def track_and_replace_source(source_list, robot_script, volume_limit=180):
         source_volumes = {}
         adjusted_sources = []
 
@@ -461,11 +461,10 @@ with tab5:
             wells.append(f"{row}{col}")
         return wells
 
-    # ========== 32-BIT DECODING ==========
-    st.subheader("32-bit Binary per Row")
-    st.write("Upload CSV with 32 columns (0 or 1), no headers, from EF Binary format or enter manually below.")
+    st.subheader("Binary per Row")
+    st.write("Upload CSV with any number of columns (0 or 1), no headers, from EF Binary format or enter manually below.")
 
-    binary32_file = st.file_uploader("Upload 32-bit Binary CSV", type=["csv"], key="binary_32")
+    binary32_file = st.file_uploader("Upload Binary CSV", type=["csv"], key="binary_any")
 
     st.subheader("Optional Metadata (Optional)")
     barcode_id_input = st.text_input("Barcode ID (applied to all rows, optional)", value="")
@@ -475,36 +474,25 @@ with tab5:
 
     if binary32_file:
         df_32 = pd.read_csv(binary32_file, header=None)
-        df_32.columns = [str(h) for h in mutation_site_headers_actual_3614]
+        df_32.columns = [str(h) for h in range(1, len(df_32.columns)+1)]
     else:
         df_32 = st.data_editor(
-            pd.DataFrame(columns=[str(h) for h in mutation_site_headers_actual_3614]),
+            pd.DataFrame(columns=[str(h) for h in range(1, 33)]),
             num_rows="dynamic",
-            key="manual_32_input"
+            key="manual_any_input"
         )
 
     if not df_32.empty:
-        reordered_df_32 = df_32[[str(h) for h in mutation_site_headers_3614 if str(h) in df_32.columns]]
-        st.subheader("Binary Labels (Reordered 4402→3244, 4882→4455)")
-        st.dataframe(reordered_df_32.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral"))
-        st.download_button("Download Reordered CSV", reordered_df_32.to_csv(index=False), "decoded_binary_32_reordered.csv", key="download_csv_tab5_32_reordered")
-
-        decoded_reordered = binary_labels_to_string(reordered_df_32.values.flatten().astype(int).tolist())
-        st.subheader("Decoded String (Reordered 4402→3244, 4882→4455)")
-        st.write(decoded_reordered)
-        st.download_button("Download Concatenated Output", decoded_reordered, "decoded_32bit_string_reordered.txt", key="download_txt_tab5_32")
+        st.subheader("Binary Labels (Uploaded)")
+        st.dataframe(df_32.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral"))
+        st.download_button("Download CSV", df_32.to_csv(index=False), "decoded_binary_uploaded.csv", key="download_csv_uploaded")
 
-        df_32_asc = df_32[[str(h) for h in mutation_site_headers_actual_3614 if str(h) in df_32.columns]]
-        st.subheader("Binary Labels (Ascending 3244→4882)")
-        st.dataframe(df_32_asc.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral"))
-        st.download_button("Download Ascending CSV", df_32_asc.to_csv(index=False), "decoded_binary_32_ascending.csv", key="download_csv_tab5_32_ascend")
+        decoded = binary_labels_to_string(df_32.values.flatten().astype(int).tolist())
+        st.subheader("Decoded String")
+        st.write(decoded)
+        st.download_button("Download Concatenated Output", decoded, "decoded_binary_string.txt", key="download_txt_any")
 
-        decoded_asc = binary_labels_to_string(df_32_asc.values.flatten().astype(int).tolist())
-        st.subheader("Decoded String (Flattened 32-bit Ascending)")
-        st.write(decoded_asc)
-        st.download_button("Download Concatenated Output", decoded_asc, "decoded_32bit_string_ascending.txt", key="download_txt_tab5_32_asc")
-
-        st.subheader("Robot Preparation Script from 32-bit Binary")
+        st.subheader("Robot Preparation Script from Binary")
 
         df_32_robot = df_32.copy()
         df_32_robot.insert(0, 'Sample', range(1, len(df_32_robot)+1))
@@ -513,14 +501,12 @@ with tab5:
 
         robot_script_32 = []
         source_wells_32 = generate_source_wells(df_32.shape[1])
-        used_destinations = set()
 
         for i, col in enumerate(df_32.columns):
             for row_idx, sample in df_32_robot.iterrows():
                 if int(sample[col]) == 1:
                     source = source_wells_32[i]
                     dest = get_well_position(int(sample['Sample']))
-                    used_destinations.add(dest)
                     vol = round(sample['volume donors (µl)'], 2)
                     tool = 'TS_10' if vol < 10 else 'TS_50'
                     robot_script_32.append({
@@ -543,77 +529,10 @@ with tab5:
         robot_script_32_df = robot_script_32_df[['Barcode ID', 'Labware_Source', 'Source', 'Labware_Destination', 'Destination', 'Volume', 'Tool', 'Name']]
 
         st.dataframe(robot_script_32_df)
-        st.download_button("Download Robot Script (32-bit)", robot_script_32_df.to_csv(index=False), "robot_script_32bit.csv", key="download_robot_32")
+        st.download_button("Download Robot Script", robot_script_32_df.to_csv(index=False), "robot_script.csv", key="download_robot_any")
 
         st.subheader("Total Volume Used Per Source")
         combined_volumes = {**source_volumes_32, **d_volumes}
         source_volume_df = pd.DataFrame(list(combined_volumes.items()), columns=['Source', 'Total Volume (µl)'])
         st.dataframe(source_volume_df)
-        st.download_button("Download Source Volumes", source_volume_df.to_csv(index=False), "source_total_volumes.csv", key="download_volume_32")
-
-    st.markdown("---")
-
-    # ========== 31-BIT DECODING ==========
-    st.subheader("31-bit Binary Grouped per Row")
-    st.write("Upload CSV with 31 columns (no headers), each row = one 6-bit ASCII character group or enter manually below.")
-
-    binary31_file = st.file_uploader("Upload 31-bit Group CSV", type=["csv"], key="binary_31")
-
-    if binary31_file:
-        df_31 = pd.read_csv(binary31_file, header=None)
-        df_31.columns = [str(h) for h in mutation_site_headers_actual]  # assume ascending
-    else:
-        df_31 = st.data_editor(
-            pd.DataFrame(columns=[str(h) for h in mutation_site_headers_actual]),
-            num_rows="dynamic",
-            key="manual_31_input"
-        )
-
-    if not df_31.empty:
-        reordered_df_31 = df_31[[str(h) for h in mutation_site_headers if str(h) in df_31.columns]]
-        st.subheader("Binary Labels (Reordered 4402→3244, 4882→4455)")
-        st.dataframe(reordered_df_31.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral"))
-        st.download_button("Download Reordered CSV", reordered_df_31.to_csv(index=False), "decoded_binary_31_reordered.csv", key="download_csv_tab5_31_reordered")
-
-        decoded_flat_reordered = binary_labels_to_string(reordered_df_31.values.flatten().astype(int).tolist())
-        st.subheader("Decoded String (Flattened 31-bit Reordered)")
-        st.write(decoded_flat_reordered)
-        st.download_button("Download Concatenated Output", decoded_flat_reordered, "decoded_31bit_string_reordered.txt", key="download_csv_tab5_31")
-
-        df_31_asc = df_31[[str(h) for h in mutation_site_headers_actual if str(h) in df_31.columns]]
-        st.subheader("Binary Labels (Ascending 3244→4882)")
-        st.dataframe(df_31_asc.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral"))
-        st.download_button("Download Ascending CSV", df_31_asc.to_csv(index=False), "decoded_binary_31_ascending.csv", key="download_csv_tab5_31_ascend")
-
-        decoded_flat_asc = binary_labels_to_string(df_31_asc.values.flatten().astype(int).tolist())
-        st.subheader("Decoded String (Flattened 31-bit Ascending)")
-        st.write(decoded_flat_asc)
-        st.download_button("Download Concatenated Output", decoded_flat_asc, "decoded_31bit_string_ascending.txt", key="download_csv_tab5_31_asc")
-
-        # === Robot Preparation Script from 31-bit Binary ===
-        st.subheader("Robot Preparation Script from 31-bit Binary")
-        robot_template_31 = pd.read_csv("/home/user/app/Robot2.csv", skiprows=3)
-        robot_template_31.columns = ['Labware', 'Source', 'Labware_2', 'Destination', 'Volume', 'Tool', 'Name']
-
-        df_31_robot = df_31.copy()
-        df_31_robot.insert(0, 'Sample', range(1, len(df_31_robot)+1))
-        df_31_robot['# donors'] = df_31_robot.iloc[:, 1:].astype(int).sum(axis=1)
-        df_31_robot['volume donors (µl)'] = 64 / df_31_robot['# donors']
-
-        robot_script_31 = []
-        source_wells_31 = robot_template_31['Source'].unique().tolist()
-        if len(source_wells_31) < df_31.shape[1]:
-            source_wells_31 += [f"Fake{i}" for i in range(df_31.shape[1] - len(source_wells_31))]
-        source_wells_31 = source_wells_31[:df_31.shape[1]]
-
-        for i, col in enumerate(df_31.columns):
-            for row_idx, sample in df_31_robot.iterrows():
-                if int(sample[col]) == 1:
-                    source = source_wells_31[i]
-                    dest = get_well_position(int(sample['Sample']))
-                    vol = round(sample['volume donors (µl)'], 2)
-                    robot_script_31.append({'Source': source, 'Destination': dest, 'Volume': vol})
-
-        robot_script_31_df = pd.DataFrame(robot_script_31)
-        st.dataframe(robot_script_31_df)
-        st.download_button("Download Robot Script (31-bit)", robot_script_31_df.to_csv(index=False), "robot_script_31bit.csv", key="download_robot_31")
+        st.download_button("Download Source Volumes", source_volume_df.to_csv(index=False), "source_total_volumes.csv", key="download_volume_any")