Update app.py

app.py

@@ -542,7 +542,7 @@ 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):
+    def track_and_replace_source(source_list, robot_script, volume_limit=180):
         source_volumes = {}
         adjusted_sources = []
 
@@ -591,12 +591,28 @@ with tab5:
 
             d_source_volumes[current_d_well] += fixed_volume
 
-            d_source_script.append({
-                'Source': current_d_well,
-                'Destination': dest,
-                'Volume': fixed_volume,
-                'Tool': 'TS_10'
-            })
+            # Split if >10 and assign TS_10
+            if fixed_volume > 10:
+                half_vol = round(fixed_volume / 2, 2)
+                d_source_script.append({
+                    'Source': current_d_well,
+                    'Destination': dest,
+                    'Volume': half_vol,
+                    'Tool': 'TS_10'
+                })
+                d_source_script.append({
+                    'Source': current_d_well,
+                    'Destination': dest,
+                    'Volume': fixed_volume - half_vol,
+                    'Tool': 'TS_10'
+                })
+            else:
+                d_source_script.append({
+                    'Source': current_d_well,
+                    'Destination': dest,
+                    'Volume': fixed_volume,
+                    'Tool': 'TS_10'
+                })
 
         return d_source_script, d_source_volumes
 
@@ -609,19 +625,18 @@ 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="")
     labware_source_input = st.text_input("Labware for Source (optional, default = 1)", value="1")
     labware_dest_input = st.text_input("Labware for Destination (optional, default = 1)", value="1")
     name_input = st.text_input("Name field (optional, default = blank)", value="")
-    volume_limit_input = st.number_input("Volume limit per source well (µl)", min_value=1, max_value=500, value=180, step=10)
-    
+    volume_limit_input = st.number_input("Maximum Volume Per Source Well (µl)", value=180)
+
     if binary32_file:
         df_32 = pd.read_csv(binary32_file, header=None)
         df_32.columns = [str(h) for h in range(1, len(df_32.columns)+1)]
@@ -633,27 +648,16 @@ with tab5:
         )
 
     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")
+        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")
 
-        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")
-
-        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))
@@ -662,15 +666,14 @@ 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)
+
                     if vol > 10:
                         half_vol = round(vol / 2, 2)
                         robot_script_32.append({
@@ -695,7 +698,7 @@ with tab5:
 
         robot_script_32, source_volumes_32 = track_and_replace_source(source_wells_32, robot_script_32, volume_limit=volume_limit_input)
 
-        d_script, d_volumes = generate_fixed_d_source_instructions_to_all_samples(len(df_32_robot), volume_limit=volume_limit_input)
+        d_script, d_volumes = generate_fixed_d_source_instructions_to_all_samples(len(df_32_robot), fixed_volume=16, volume_limit=volume_limit_input)
         full_robot_script = robot_script_32 + d_script
 
         robot_script_32_df = pd.DataFrame(full_robot_script)
@@ -706,10 +709,11 @@ 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.download_button("Download Source Volumes", source_volume_df.to_csv(index=False), "source_total_volumes.csv", key="download_volume_any")
+