Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| from PIL import Image, ImageFilter | |
| import numpy as np | |
| import pandas as pd | |
| from streamlit_cropper import st_cropper | |
| # 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 = st.tabs(["Text to Binary Labels (31)", "EF → Binary (31)", "Text to Binary Labels (32)", "EF → Binary (2)"]) | |
| # 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 = [ord(c) for c in user_input] | |
| binary_labels = string_to_binary_labels(user_input) | |
| 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[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") | |
| ascending_headers = sorted(mutation_site_headers_actual) | |
| 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") | |
| # === 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") | |
| # 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"): | |
| 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") | |
| 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") | |
| 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 === | |
| # 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()} | |
| # 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") | |
| 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") | |
| # === 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") | |
| # 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_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"): | |
| 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") | |
| 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") | |
| 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) | |