app.py

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 = st.tabs(["Text to Binary Labels (31)", "EF → Binary → String (31)", "Text to Binary Labels (32)", "EF → Binary (32)", "Binary → String"])

# 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

# === App Title ===
with tab6: 
    st.header("Robot Script Generator")
    
# === 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()}

# === Binary ↔ String conversion ===
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)

# === Well mapping ===
def get_well_position(sample_index):
    row_letter = chr(65 + (sample_index - 1) // 12)
    col_number = ((sample_index - 1) % 12) + 1
    return f"{row_letter}{col_number}"

# === Track and replace source if volume exceeded ===
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']
        source_volumes[src] = source_volumes.get(src, 0) + 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
            source_volumes[new_src] = source_volumes.get(new_src, 0) + vol
            source_volumes[src] -= vol
        adjusted_sources.append(entry)
    return adjusted_sources, source_volumes

# === Fixed D-source transfers ===
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}"
        d_source_volumes.setdefault(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

        # ✅ Updated: use TS_50 if volume >10 µL, else TS_10
        tool = 'TS_50' if fixed_volume > 10 else 'TS_10'
        d_source_script.append({
            'Source': current_d_well,
            'Destination': dest,
            'Volume': fixed_volume,
            'Tool': tool
        })

    return d_source_script, d_source_volumes

# === Source well generation ===
def generate_source_wells(n):
    wells, rows = [], 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
    for i in range(n):
        row, col = rows[i // 12], (i % 12) + 1
        wells.append(f"{row}{col}")
    return wells

# === Main UI ===
st.header("Upload Binary Data (0/1)")

binary_file = st.file_uploader("Upload Binary CSV", type=["csv"])
st.divider()

st.subheader("Optional Metadata")
barcode_id_input = st.text_input("Barcode ID (optional)", value="")
labware_source_input = st.text_input("Labware Source ID", value="1")
labware_dest_input = st.text_input("Labware Destination ID", value="1")
name_input = st.text_input("Name field (optional)", value="")
volume_limit_input = st.number_input("Maximum Volume per Source Well (µL)", value=150, min_value=10, step=10)

# === Load Data ===
if binary_file:
    df_binary = pd.read_csv(binary_file, header=None)
    df_binary.columns = [str(i+1) for i in range(df_binary.shape[1])]
else:
    st.info("No file uploaded — manually enter binary data below.")
    df_binary = st.data_editor(
        pd.DataFrame(columns=[str(1)]),
        num_rows="dynamic", key="manual_input"
    )

if not df_binary.empty:
    st.subheader("Binary Matrix")
    st.dataframe(df_binary.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral"))
    st.download_button("⬇️ Download Binary CSV", df_binary.to_csv(index=False), "binary_matrix.csv")

    # Decode to string
    decoded = binary_labels_to_string(df_binary.values.flatten().astype(int).tolist())
    st.subheader("Decoded String Output")
    st.code(decoded)
    st.download_button("⬇️ Download Decoded String", decoded, "decoded_string.txt")

    # === Generate Robot Script ===
    st.divider()
    st.subheader("Generated Robot Script")

    df_robot = df_binary.copy()
    df_robot.insert(0, 'Sample', range(1, len(df_robot) + 1))
    df_robot['# donors'] = df_robot.iloc[:, 1:].astype(int).sum(axis=1)
    df_robot['volume donors (µL)'] = 64 / df_robot['# donors']

    robot_script = []
    source_wells = generate_source_wells(df_robot.shape[1] - 1)

    for i, col in enumerate(df_robot.columns[1:]):
        for _, sample in df_robot.iterrows():
            if int(sample[col]) == 1:
                source = source_wells[i]
                dest = get_well_position(int(sample['Sample']))
                vol = round(sample['volume donors (µL)'], 2)

                # ✅ Updated: use TS_50 for volumes >10 µL, TS_10 otherwise
                tool = 'TS_50' if vol > 10 else 'TS_10'
                robot_script.append({'Source': source, 'Destination': dest, 'Volume': vol, 'Tool': tool})

    robot_script, source_volumes = track_and_replace_source(source_wells, robot_script, volume_limit=volume_limit_input)
    d_script, d_volumes = generate_fixed_d_source_instructions_to_all_samples(
        len(df_robot), fixed_volume=16, volume_limit=volume_limit_input
    )

    full_script = robot_script + d_script

    robot_script_df = pd.DataFrame(full_script)
    robot_script_df.insert(0, 'Barcode ID', barcode_id_input)
    robot_script_df.insert(1, 'Labware_Source', labware_source_input)
    robot_script_df.insert(3, 'Labware_Destination', labware_dest_input)
    robot_script_df['Name'] = name_input
    robot_script_df = robot_script_df[['Barcode ID', 'Labware_Source', 'Source',
                                       'Labware_Destination', 'Destination', 'Volume', 'Tool', 'Name']]

    st.dataframe(robot_script_df)
    st.download_button("⬇️ Download Robot Script", robot_script_df.to_csv(index=False), "robot_script.csv")

    # === Source Volume Summary ===
    st.divider()
    st.subheader("Total Volume Used Per Source")
    combined_volumes = {**source_volumes, **d_volumes}
    volume_df = pd.DataFrame(list(combined_volumes.items()), columns=['Source', 'Total Volume (µL)'])
    st.dataframe(volume_df)
    st.download_button("⬇️ Download Volume Summary", volume_df.to_csv(index=False), "source_volumes.csv")