Update app.py

app.py

@@ -1,731 +1,11 @@
-import streamlit as st
-import numpy as np
-import pandas as pd
-
-# 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: 1.096910677, 3297: 0.923658795, 3350: 0.668939037, 3399: 0.914305214,
-    3455: 1.297392984, 3509: 1.812636208, 3562: 1.185047484, 
-    3665: 0.298007308, 3720: 0.58857544, 3773: 0.882561082, 3824: 1.149082617,
-    3879: 0.816050702, 3933: 2.936517653, 3985: 1.597166791, 4039: 0.962108082,
-    4089: 1.479783497, 4145: 0.305853225, 4190: 1.311869541, 4245: 1.707556905,
-    4298: 0.875013076, 4349: 1.227704526, 4402: 0.593206446, 4455: 1.179633137,
-    4510: 1.272477799, 4561: 1.293841573, 4615: 1.16821885, 4668: 1.40306,
-    4720: 0.706530878, 4773: 1.483114072, 4828: 0.954939873, 4882: 1.47524328
-})
-
-# Mutation site headers reordered: 4402 to 3244, 4882 to 4455
-mutation_site_headers = [
-    4402, 4349, 4298, 4245, 4190, 4145, 4089, 4039,
-    3985, 3933, 3879, 3824, 3773, 3720, 3665,
-    3562, 3509, 3455, 3399, 3350, 3297, 3244,  # 1–23
-    4882, 4828, 4773, 4720, 4668, 4615, 4561, 4510, 4455   # 24–32
-]
-
-# Thresholds reordered accordingly
-thresholds = pd.Series({h: thresholds_actual[h] for h in mutation_site_headers})
-
-# === Utility functions ===
-
-# Voyager ASCII 6-bit conversion table
-voyager_table = {
-    i: ch for i, ch in enumerate([
-        ' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I',
-        'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S',
-        'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '0', '1', '2',
-        '3', '4', '5', '6', '7', '8', '9', '.', ',', '(', 
-        ')','+', '-', '*', '/', '=', '$', '!', ':', '%', 
-        '"', '#', '@', "'", '?', '&'
-    ])
-}
-reverse_voyager_table = {v: k for k, v in voyager_table.items()}
-
-def string_to_binary_labels(s: str) -> list[int]:
-    bits = []
-    for char in s:
-        val = reverse_voyager_table.get(char.upper(), 0)
-        char_bits = [(val >> bit) & 1 for bit in range(5, -1, -1)]
-        bits.extend(char_bits)
-    return bits
-
-def binary_labels_to_string(bits: list[int]) -> str:
-    chars = []
-    for i in range(0, len(bits), 6):
-        chunk = bits[i:i+6]
-        if len(chunk) < 6:
-            chunk += [0] * (6 - len(chunk))
-        val = sum(b << (5 - j) for j, b in enumerate(chunk))
-        chars.append(voyager_table.get(val, '?'))
-    return ''.join(chars)
-
-
-# === Streamlit App ===
-
-st.title("ASCII & Binary Label Converter")
-tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Text to Binary Labels (31)", "EF → Binary → String (31)", "Text to Binary Labels (32)", "EF → Binary (32)", "Binary → String", "Robot Script Generator"])
-
-# Tab 1: Text to Binary
-with tab1:
-    user_input = st.text_input("Enter text", value="DNA", key="input_text_31")
-    if user_input:
-        ascii_codes = [reverse_voyager_table.get(c.upper(), 0) for c in user_input]
-        binary_labels = string_to_binary_labels(user_input)
-
-        # st.subheader("Voyager ASCII Codes")
-        # st.write(ascii_codes)
-
-        st.subheader("Binary Labels per Character")
-        grouped = [binary_labels[i:i+6] for i in range(0, len(binary_labels), 6)]
-        for i, bits in enumerate(grouped):
-            st.write(f"'{user_input[i]}' → {bits}")
-
-        st.subheader("Binary Labels (31-bit groups)")
-        groups = []
-        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_31_binary_labels.csv", key="download_csv_tab1_31csv")
-
-        ascending_headers = sorted(mutation_site_headers_actual)
-        df_sorted = df[[str(h) for h in ascending_headers if str(h) in df.columns]].copy()
-        
-        if "3614" not in df_sorted.columns:
-            idx = df_sorted.columns.get_loc("3562") + 1  # Insert after 3562
-            df_sorted.insert(idx, "3614", 0)
-
-        st.subheader("Binary Labels (Ascending Order 3244 → 4882)")
-        st.dataframe(df_sorted)
-        st.download_button("Download Ascending Order CSV", df_sorted.to_csv(index=False), "text_binary_labels_ascending.csv", key="download_csv_tab1_ascend")
-
-        # === Robot Preparation Script Generation ===
-        st.subheader("Robot Preparation Script")
-        robot_template = pd.read_csv("/home/user/app/Robot.csv", skiprows=3)
-        robot_template.columns = ['Labware', 'Source', 'Labware_2', 'Destination', 'Volume', 'Tool', 'Name']
-
-        # Add Sample numbers for well referencing
-        df_sorted.insert(0, 'Sample', range(1, len(df_sorted)+1))
-
-        # Step 1: Count the number of edited sites per row
-        df_sorted['# donors'] = df_sorted.iloc[:, 1:].sum(axis=1)
-
-        # Step 2: Calculate volume per donor (32 / # donors)
-        df_sorted['volume donors (µl)'] = 32 / df_sorted['# donors']
-
-        # Step 3: Generate the robot script
-        robot_script = []
-        source_wells = robot_template['Source'].unique().tolist()
-        if len(source_wells) < 32:
-            source_wells += [f"Fake{i}" for i in range(32 - len(source_wells))]
-        source_wells = source_wells[:32]
-
-
-        st.write(f"Number of source wells: {len(source_wells)}")
-        st.write(f"Number of binary columns: {len(df_sorted.columns[1:33])}")
-
-        for i, col in enumerate(df_sorted.columns[1:33]):
-            for row_idx, sample in df_sorted.iterrows():
-                if sample[col] == 1:
-                    source = source_wells[i]
-                    dest = f"A{sample['Sample']}"
-                    vol = round(sample['volume donors (µl)'], 2)
-                    robot_script.append({'Source': source, 'Destination': dest, 'Volume': vol})
-
-        robot_script_df = pd.DataFrame(robot_script)
-        st.dataframe(robot_script_df)
-        st.download_button("Download Robot Script CSV", robot_script_df.to_csv(index=False), "robot_script.csv", key="download_csv_tab1_robot")
-
-        # === Robot Preparation Script (Custom Order: 4402 → 3244, 4882 → 4455) ===
-        st.subheader("Robot Preparation Script (Custom Order: 4402 → 3244, 4882 → 4455)")
-        
-        # Include 3614 in custom header list
-        custom_headers = [
-            4402, 4349, 4298, 4245, 4190, 4145, 4089, 4039,
-            3985, 3933, 3879, 3824, 3773, 3720, 3665, 3614,
-            3562, 3509, 3455, 3399, 3350, 3297, 3244,
-            4882, 4828, 4773, 4720, 4668, 4615, 4561, 4510, 4455
-        ]
-        
-        # Create a copy of df and reorder columns based on custom headers
-        df_sorted_custom = df[[str(h) for h in custom_headers if str(h) in df.columns]].copy()
-        
-        # Insert fake column "3614" if missing
-        if "3614" not in df_sorted_custom.columns:
-            idx = custom_headers.index(3614)
-            insert_at = idx  # 0-based index
-            df_sorted_custom.insert(insert_at, "3614", 0)
-        
-        # Insert 'Sample' if missing
-        if "Sample" not in df_sorted_custom.columns:
-            df_sorted_custom.insert(0, 'Sample', range(1, len(df_sorted_custom) + 1))
-        
-        # Calculate donor info
-        df_sorted_custom['# donors'] = df_sorted_custom.iloc[:, 1:].sum(axis=1)
-        df_sorted_custom['volume donors (µl)'] = 32 / df_sorted_custom['# donors']
-        
-        # Generate robot script
-        robot_script_custom = []
-        for i, col in enumerate(df_sorted_custom.columns[1:33]):  # 32 columns after Sample
-            for row_idx, sample in df_sorted_custom.iterrows():
-                if sample[col] == 1:
-                    source = source_wells[i]
-                    dest = f"A{sample['Sample']}"
-                    vol = round(sample['volume donors (µl)'], 2)
-                    robot_script_custom.append({'Source': source, 'Destination': dest, 'Volume': vol})
-        
-        robot_script_custom_df = pd.DataFrame(robot_script_custom)
-        st.dataframe(robot_script_custom_df)
-        st.download_button("Download Custom Order Robot Script CSV", robot_script_custom_df.to_csv(index=False), "robot_script_custom_order.csv", key="download_csv_tab1_robot_custom")
-
-# Tab 2: EF → Binary
-with tab2:
-    st.write("Upload an Editing Frequency CSV or enter manually:")
-    st.write("**Note:** Please upload CSV files **without column headers**, in ascending order from 3244 to 4882.")
-    ef_file = st.file_uploader("Upload EF CSV", type=["csv"], key="ef")
-
-    if ef_file:
-        ef_df = pd.read_csv(ef_file, header=None)
-        ef_df.columns = [str(site) for site in sorted(mutation_site_headers_actual)]
-    else:
-        ef_df = pd.DataFrame(columns=[str(site) for site in sorted(mutation_site_headers_actual)])
-
-    edited_df = st.data_editor(ef_df, num_rows="dynamic")
-
-    if st.button("Convert to Binary Labels", key="convert_button_tab2"):
-        binary_part = pd.DataFrame()
-        for col in sorted(mutation_site_headers_actual):
-            col_str = str(col)
-            threshold = thresholds_actual[col]
-            binary_part[col_str] = (edited_df[col_str].astype(float) >= threshold).astype(int)
-
-        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 (Reordered 4402→3244, 4882→4455)")
-        styled = binary_reordered.style.applymap(color_binary)
-        st.dataframe(styled)
-        st.download_button("Download CSV", binary_reordered.to_csv(index=False), "ef_binary_labels.csv", key="download_csv_tab2_csv")
-
-        all_bits = binary_reordered.values.flatten().tolist()
-        decoded_string = binary_labels_to_string(all_bits)
-        st.subheader("Decoded String (continuous across rows)")
-        st.write(decoded_string)
-
-        st.subheader("Binary Labels (Ascending 3244→4882)")
-        st.dataframe(binary_part.style.applymap(color_binary))
-        st.download_button("Download Ascending Order CSV", binary_part.to_csv(index=False), "ef_binary_labels_ascending.csv", key="download_csv_tab2_ascend")
-
-        all_bits = binary_part.values.flatten().tolist()
-        decoded_string = binary_labels_to_string(all_bits)
-        st.subheader("Decoded String (continuous across rows)")
-        st.write(decoded_string)
-
-
-# Mutation site headers did not remove 3614,
-mutation_site_headers_actual_3614 = [
-    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
-]
-# Thresholds for each mutation site removed 3614: 0.091557752,
-thresholds_actual_3614 = pd.Series({
-    3244: 1.096910677, 3297: 0.923658795, 3350: 0.668939037, 3399: 0.914305214,
-    3455: 1.297392984, 3509: 1.812636208, 3562: 1.185047484, 3614: 0.157969131375,
-    3665: 0.298007308, 3720: 0.58857544, 3773: 0.882561082, 3824: 1.149082617,
-    3879: 0.816050702, 3933: 2.936517653, 3985: 1.597166791, 4039: 0.962108082,
-    4089: 1.479783497, 4145: 0.305853225, 4190: 1.311869541, 4245: 1.707556905,
-    4298: 0.875013076, 4349: 1.227704526, 4402: 0.593206446, 4455: 1.179633137,
-    4510: 1.272477799, 4561: 1.293841573, 4615: 1.16821885, 4668: 1.40306,
-    4720: 0.706530878, 4773: 1.483114072, 4828: 0.954939873, 4882: 1.47524328
-})
-
-# Mutation site headers reordered: 4402 to 3244, 4882 to 4455
-mutation_site_headers_3614 = [
-    4402, 4349, 4298, 4245, 4190, 4145, 4089, 4039,
-    3985, 3933, 3879, 3824, 3773, 3720, 3665, 3614,
-    3562, 3509, 3455, 3399, 3350, 3297, 3244,  # 1–23
-    4882, 4828, 4773, 4720, 4668, 4615, 4561, 4510, 4455   # 24–32
-]
-
-# Thresholds reordered accordingly
-thresholds_3614 = pd.Series({h: thresholds_actual_3614[h] for h in mutation_site_headers_3614})
-
-# === Utility functions ===
-
-reverse_voyager_table = {v: k for k, v in voyager_table.items()}
-
-
-# Tab 3: Text to Binary (32)
-with tab3:
-    user_input_32 = st.text_input("Enter text", value="DNA", key="input_text_32")
-    if user_input_32:
-        ascii_codes = [ord(c) for c in user_input_32]
-        binary_labels = string_to_binary_labels(user_input_32)
-
-        st.subheader("ASCII Codes")
-        st.write(ascii_codes)
-
-        st.subheader("Binary Labels per Character")
-        grouped = [binary_labels[i:i+6] for i in range(0, len(binary_labels), 6)]
-        for i, bits in enumerate(grouped):
-            st.write(f"'{user_input_32[i]}' → {bits}")
-
-        st.subheader("Binary Labels (32-bit groups)")
-        groups = []
-        for i in range(0, len(binary_labels), 32):
-            group = binary_labels[i:i+32]
-            group += [0] * (32 - len(group))
-            groups.append(group + [sum(group)])
-
-        df = pd.DataFrame(groups, columns=[str(h) for h in mutation_site_headers_3614] + ["Edited Sites"])
-        st.dataframe(df)
-        st.download_button("Download as CSV", df.to_csv(index=False), "text_32_binary_labels.csv", key="download_csv_tab3_csv")
-
-        ascending_headers = sorted(mutation_site_headers_actual_3614)
-        df_sorted = df[[str(h) for h in ascending_headers if str(h) in df.columns]]
-        st.subheader("Binary Labels (Ascending Order 3244 → 4882)")
-        st.dataframe(df_sorted)
-        st.download_button("Download Ascending Order CSV", df_sorted.to_csv(index=False), "text_binary_labels_ascending.csv", key="download_csv_tab3_ascend")
-
-        # === Robot Preparation Script Generation ===
-        st.subheader("Robot Preparation Script")
-        robot_template = pd.read_csv("/home/user/app/Robot.csv", skiprows=3)
-        robot_template.columns = ['Labware', 'Source', 'Labware_2', 'Destination', 'Volume', 'Tool', 'Name']
-
-        # Add Sample numbers for well referencing
-        df_sorted.insert(0, 'Sample', range(1, len(df_sorted)+1))
-
-        # Step 1: Count the number of edited sites per row
-        df_sorted['# donors'] = df_sorted.iloc[:, 1:].sum(axis=1)
-
-        # Step 2: Calculate volume per donor (32 / # donors)
-        df_sorted['volume donors (µl)'] = 32 / df_sorted['# donors']
-
-        # Step 3: Generate the robot script
-        robot_script = []
-        source_wells = robot_template['Source'].unique().tolist()[:32]
-
-        for i, col in enumerate(df_sorted.columns[1:33]):
-            for row_idx, sample in df_sorted.iterrows():
-                if sample[col] == 1:
-                    source = source_wells[i]
-                    dest = f"A{sample['Sample']}"
-                    vol = round(sample['volume donors (µl)'], 2)
-                    robot_script.append({'Source': source, 'Destination': dest, 'Volume': vol})
-
-        robot_script_df = pd.DataFrame(robot_script)
-        st.dataframe(robot_script_df)
-        st.download_button("Download Robot Script CSV", robot_script_df.to_csv(index=False), "robot_script.csv", key="download_csv_tab3_robot")
-
-
-# Tab 4: EF → Binary (32)
-with tab4:
-    st.write("Upload an Editing Frequency CSV or enter manually:")
-    st.write("**Note:** Please upload CSV files **without column headers**, in ascending order from 3244 to 4882.")
-    ef_file_2 = st.file_uploader("Upload EF CSV", type=["csv"], key="ef2")
-
-    if ef_file_2:
-        ef_df = pd.read_csv(ef_file_2, header=None)
-        ef_df.columns = [str(site) for site in sorted(mutation_site_headers_actual_3614)]
-    else:
-        ef_df = pd.DataFrame(columns=[str(site) for site in sorted(mutation_site_headers_actual_3614)])
-
-    edited_df = st.data_editor(ef_df, num_rows="dynamic")
-
-    if st.button("Convert to Binary Labels", key="convert_button_tab4"):
-        binary_part = pd.DataFrame()
-        for col in sorted(mutation_site_headers_actual_3614):
-            col_str = str(col)
-            threshold = thresholds_actual_3614[col]
-            binary_part[col_str] = (edited_df[col_str].astype(float) >= threshold).astype(int)
-
-        binary_reordered = binary_part[[str(h) for h in mutation_site_headers_3614 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 (Reordered 4402→3244, 4882→4455)")
-        styled = binary_reordered.style.applymap(color_binary)
-        st.dataframe(styled)
-        st.download_button("Download CSV", binary_reordered.to_csv(index=False), "ef_binary_labels.csv", key="download_csv_tab4_csv")
-
-        all_bits = binary_reordered.values.flatten().tolist()
-        decoded_string = binary_labels_to_string(all_bits)
-        st.subheader("Decoded String (continuous across rows)")
-        st.write(decoded_string)
-
-        st.subheader("Binary Labels (Ascending 3244→4882)")
-        st.dataframe(binary_part.style.applymap(color_binary))
-        st.download_button("Download Ascending Order CSV", binary_part.to_csv(index=False), "ef_binary_labels_ascending.csv", key="download_csv_tab4_ascend")
-
-        all_bits = binary_part.values.flatten().tolist()
-        decoded_string = binary_labels_to_string(all_bits)
-        st.subheader("Decoded String (continuous across rows)")
-        st.write(decoded_string)
-
-def get_well_position(sample_index):
-    """
-    Convert sample index (1-based) into well position (e.g., A1, A2, ..., B1, B2, ..., etc.)
-    """
-    row_letter = chr(65 + (sample_index - 1) // 12)  # 65 = 'A'
-    col_number = ((sample_index - 1) % 12) + 1
-    return f"{row_letter}{col_number}"
-
-# # Tab 5: Binary → String
-# 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=180):
-#         source_volumes = {}
-#         adjusted_sources = []
-
-#         for entry in robot_script:
-#             src = entry['Source']
-#             vol = entry['Volume']
-
-#             if src not in source_volumes:
-#                 source_volumes[src] = 0
-
-#             source_volumes[src] += vol
-
-#             if source_volumes[src] > volume_limit:
-#                 row_letter = src[0]
-#                 col_number = src[1:]
-#                 new_row_letter = chr(ord(row_letter) + 4)
-#                 new_src = f"{new_row_letter}{col_number}"
-#                 entry['Source'] = new_src
-
-#                 if new_src not in source_volumes:
-#                     source_volumes[new_src] = 0
-#                 source_volumes[new_src] += vol
-#                 source_volumes[src] -= vol
-
-#             adjusted_sources.append(entry)
-
-#         return adjusted_sources, source_volumes
-
-#     # Utility: Generate fixed-volume D source to all sample wells
-#     def generate_fixed_d_source_instructions_to_all_samples(n_samples, fixed_volume=16, volume_limit=170):
-#         d_source_volumes = {}
-#         d_source_script = []
-#         current_d_index = 1
-
-#         for i in range(n_samples):
-#             dest = get_well_position(i + 1)
-#             current_d_well = f"D{current_d_index}"
-
-#             if current_d_well not in d_source_volumes:
-#                 d_source_volumes[current_d_well] = 0
-
-#             if d_source_volumes[current_d_well] + fixed_volume > volume_limit:
-#                 current_d_index += 1
-#                 current_d_well = f"D{current_d_index}"
-#                 d_source_volumes[current_d_well] = 0
-
-#             d_source_volumes[current_d_well] += fixed_volume
-#             tool = 'TS_10' if fixed_volume < 10 else 'TS_50'
-
-#             d_source_script.append({
-#                 'Source': current_d_well,
-#                 'Destination': dest,
-#                 'Volume': fixed_volume,
-#                 'Tool': tool
-#             })
-
-#         return d_source_script, d_source_volumes
-
-#     def generate_source_wells(n):
-#         wells = []
-#         rows = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
-#         for i in range(n):
-#             row = rows[i // 12]  # cycle through A, B, C...
-#             col = (i % 12) + 1    # 1 to 12
-#             wells.append(f"{row}{col}")
-#         return wells
-
-#     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 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="")
-
-#     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)]
-#     else:
-#         df_32 = st.data_editor(
-#             pd.DataFrame(columns=[str(h) for h in range(1, 33)]),
-#             num_rows="dynamic",
-#             key="manual_any_input"
-#         )
-
-#     if not df_32.empty:
-#         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 = 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")
-
-#         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))
-#         df_32_robot['# donors'] = df_32_robot.iloc[:, 1:].astype(int).sum(axis=1)
-#         df_32_robot['volume donors (µl)'] = 64 / df_32_robot['# donors']
-
-#         robot_script_32 = []
-#         source_wells_32 = generate_source_wells(df_32.shape[1])
-
-#         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']))
-#                     vol = round(sample['volume donors (µl)'], 2)
-#                     tool = 'TS_10' if vol < 10 else 'TS_50'
-#                     robot_script_32.append({
-#                         'Source': source,
-#                         'Destination': dest,
-#                         'Volume': vol,
-#                         'Tool': tool
-#                     })
-
-#         robot_script_32, source_volumes_32 = track_and_replace_source(source_wells_32, robot_script_32)
-
-#         d_script, d_volumes = generate_fixed_d_source_instructions_to_all_samples(len(df_32_robot))
-#         full_robot_script = robot_script_32 + d_script
-
-#         robot_script_32_df = pd.DataFrame(full_robot_script)
-#         robot_script_32_df.insert(0, 'Barcode ID', barcode_id_input)
-#         robot_script_32_df.insert(1, 'Labware_Source', labware_source_input)
-#         robot_script_32_df.insert(3, 'Labware_Destination', labware_dest_input)
-#         robot_script_32_df['Name'] = name_input
-#         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", 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_any")
-
-# Tab 5: Binary → String
-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=150):
-        source_volumes = {}
-        adjusted_sources = []
-
-        for entry in robot_script:
-            src = entry['Source']
-            vol = entry['Volume']
-
-            if src not in source_volumes:
-                source_volumes[src] = 0
-
-            source_volumes[src] += vol
-
-            if source_volumes[src] > volume_limit:
-                row_letter = src[0]
-                col_number = src[1:]
-                new_row_letter = chr(ord(row_letter) + 4)
-                new_src = f"{new_row_letter}{col_number}"
-                entry['Source'] = new_src
-
-                if new_src not in source_volumes:
-                    source_volumes[new_src] = 0
-                source_volumes[new_src] += vol
-                source_volumes[src] -= vol
-
-            adjusted_sources.append(entry)
-
-        return adjusted_sources, source_volumes
-
-    # Utility: Generate fixed-volume D source to all sample wells
-    def generate_fixed_d_source_instructions_to_all_samples(n_samples, fixed_volume=16, volume_limit=170):
-        d_source_volumes = {}
-        d_source_script = []
-        current_d_index = 1
-
-        for i in range(n_samples):
-            dest = get_well_position(i + 1)
-            current_d_well = f"D{current_d_index}"
-
-            if current_d_well not in d_source_volumes:
-                d_source_volumes[current_d_well] = 0
-
-            if d_source_volumes[current_d_well] + fixed_volume > volume_limit:
-                current_d_index += 1
-                current_d_well = f"D{current_d_index}"
-                d_source_volumes[current_d_well] = 0
-
-            d_source_volumes[current_d_well] += fixed_volume
-
-            # 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
-
-    def generate_source_wells(n):
-        wells = []
-        rows = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
-        for i in range(n):
-            row = rows[i // 12]  # cycle through A, B, C...
-            col = (i % 12) + 1    # 1 to 12
-            wells.append(f"{row}{col}")
-        return wells
-
-    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 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("Maximum Volume Per Source Well (µl)", value=150)
-
-    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)]
-    else:
-        df_32 = st.data_editor(
-            pd.DataFrame(columns=[str(h) for h in range(1, 33)]),
-            num_rows="dynamic",
-            key="manual_any_input"
-        )
-
-    if not df_32.empty:
-        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 = 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")
-
-        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))
-        df_32_robot['# donors'] = df_32_robot.iloc[:, 1:].astype(int).sum(axis=1)
-        df_32_robot['volume donors (µl)'] = 64 / df_32_robot['# donors']
-
-        robot_script_32 = []
-        source_wells_32 = generate_source_wells(df_32.shape[1])
-
-        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']))
-                    vol = round(sample['volume donors (µl)'], 2)
-
-                    if vol > 10:
-                        half_vol = round(vol / 2, 2)
-                        robot_script_32.append({
-                            'Source': source,
-                            'Destination': dest,
-                            'Volume': half_vol,
-                            'Tool': 'TS_10'
-                        })
-                        robot_script_32.append({
-                            'Source': source,
-                            'Destination': dest,
-                            'Volume': vol - half_vol,
-                            'Tool': 'TS_10'
-                        })
-                    else:
-                        robot_script_32.append({
-                            'Source': source,
-                            'Destination': dest,
-                            'Volume': vol,
-                            'Tool': 'TS_10'
-                        })
-
-        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), 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)
-        robot_script_32_df.insert(0, 'Barcode ID', barcode_id_input)
-        robot_script_32_df.insert(1, 'Labware_Source', labware_source_input)
-        robot_script_32_df.insert(3, 'Labware_Destination', labware_dest_input)
-        robot_script_32_df['Name'] = name_input
-        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", 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_any")
-
-
-
 import streamlit as st
 import pandas as pd
+import math
 
 # === App Title ===
-with tab6: 
-    st.header("Robot Script Generator")
-    
+st.set_page_config(page_title="Robot Script Generator", layout="wide")
+st.title("🧪 Robot Script Generator")
+
 # === Voyager ASCII 6-bit conversion table ===
 voyager_table = {
     i: ch for i, ch in enumerate([