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: 1.094293328, 3297: 0.924916122, 3350: 0.664586629, 3399: 0.91573613, 3455: 1.300869714, 3509: 1.821975901, 3562: 1.178862418, 3665: 0.298697327, 3720: 0.58379781, 3773: 0.891088481, 3824: 1.145509641, 3879: 0.81833191, 3933: 2.93084335, 3985: 1.593758847, 4039: 0.966055013, 4089: 1.465671338, 4145: 0.30309335, 4190: 1.321615138, 4245: 1.709752495, 4298: 0.868534701, 4349: 1.222907645, 4402: 0.58873557, 4455: 1.185522985, 4510: 1.266797682, 4561: 1.109913024, 4615: 1.181106084, 4668: 1.408533949, 4720: 0.714151142, 4773: 1.471959437, 4828: 0.95879943, 4882: 1.464503885 }) # 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({ 4402: 0.58873557, 4349: 1.222907645, 4298: 0.868534701, 4245: 1.709752495, 4190: 1.321615138, 4145: 0.30309335, 4089: 1.465671338, 4039: 0.966055013, 3985: 1.593758847, 3933: 2.93084335, 3879: 0.81833191, 3824: 1.145509641, 3773: 0.891088481, 3720: 0.58379781, 3665: 0.298697327, 3562: 1.178862418, 3509: 1.821975901, 3455: 1.300869714, 3399: 0.91573613, 3350: 0.664586629, 3297: 0.924916122, 3244: 1.094293328, 4882: 1.464503885, 4828: 0.95879943, 4773: 1.471959437, 4720: 0.714151142, 4668: 1.408533949, 4615: 1.181106084, 4561: 1.109913024, 4510: 1.266797682, 4455: 1.185522985 }) # === 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()} # === Utility functions === 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) # def string_to_binary_labels(s: str) -> list[int]: # bits = [] # for char in s: # ascii_code = ord(char) # char_bits = [(ascii_code >> bit) & 1 for bit in range(7, -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), 8): # byte = bits[i:i+8] # if len(byte) < 8: # byte += [0] * (8 - len(byte)) # ascii_val = sum(b << (7 - j) for j, b in enumerate(byte)) # chars.append(chr(ascii_val)) # return ''.join(chars) def clean_image(img: Image.Image, min_size: int = 256) -> Image.Image: img = img.convert("RGB") if img.width < min_size or img.height < min_size: img = img.resize((min_size, min_size)) img = img.filter(ImageFilter.GaussianBlur(radius=1)) return img def image_to_binary_labels_rgb(img: Image.Image, max_pixels: int = 256) -> list[int]: img = clean_image(img) img.thumbnail((int(np.sqrt(max_pixels)), int(np.sqrt(max_pixels)))) img_array = np.array(img) flat_pixels = img_array.reshape(-1, 3) bits = [] for pixel in flat_pixels: for channel in pixel: channel_bits = [(channel >> bit) & 1 for bit in range(7, -1, -1)] bits.extend(channel_bits) return bits def binary_labels_to_rgb_image(binary_labels: list[int], width: int = None, height: int = None) -> Image.Image: total_pixels = len(binary_labels) // 24 if width is None or height is None: side = int(np.ceil(np.sqrt(total_pixels))) width = height = side needed_pixels = width * height needed_bits = needed_pixels * 24 if len(binary_labels) < needed_bits: binary_labels += [0] * (needed_bits - len(binary_labels)) pixels = [] for i in range(0, needed_bits, 24): r_bits = binary_labels[i:i+8] g_bits = binary_labels[i+8:i+16] b_bits = binary_labels[i+16:i+24] r = sum(b << (7-j) for j, b in enumerate(r_bits)) g = sum(b << (7-j) for j, b in enumerate(g_bits)) b = sum(b << (7-j) for j, b in enumerate(b_bits)) pixels.append((r, g, b)) array = np.array(pixels, dtype=np.uint8).reshape((height, width, 3)) img = Image.fromarray(array, mode='RGB') return img # === Streamlit App === st.title("ASCII & Binary Label Converter") tab1, tab2, tab3 = st.tabs(["Text to Binary Labels", "Image to Binary Labels", "EF → Binary"]) # Tab 1: Text to Binary with tab1: user_input = st.text_input("Enter text", value="DNA") 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_32_binary_labels.csv") # Additional table with ascending mutation site headers (3244 to 4455) ascending_headers = sorted([h for h in mutation_site_headers if h <= 4455]) df_sorted = df[[str(h) for h in ascending_headers if str(h) in df.columns]] st.subheader("Binary Labels (Ascending Order 3244 → 4455)") st.dataframe(df_sorted) st.download_button("Download Ascending Order CSV", df_sorted.to_csv(index=False), "text_binary_labels_ascending.csv") # st.subheader("Binary Labels (27-bit groups)") # groups = [] # for i in range(0, len(binary_labels), 27): # group = binary_labels[i:i+27] # group += [0] * (27 - len(group)) # groups.append(group + [sum(group)]) # df_27 = pd.DataFrame(groups, columns=[str(h) for h in mutation_site_headers] + ["Edited Sites"]) # st.dataframe(df_27) # st.download_button("Download as CSV", df_27.to_csv(index=False), "text_27_binary_labels.csv") # Tab 2: Image to Binary with tab2: uploaded = st.file_uploader("Upload an image (jpg/png)", type=["jpg", "jpeg", "png"]) if uploaded: img = Image.open(uploaded) st.image(img, caption="Original", use_column_width=True) cropped = st_cropper(img, realtime_update=True, box_color="blue", aspect_ratio=None) st.image(cropped, caption="Cropped", use_column_width=True) max_pixels = st.slider("Max pixels to encode", 32, 1024, 256, 32) binary_labels = image_to_binary_labels_rgb(cropped, max_pixels=max_pixels) st.subheader("Binary Labels from Image") 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] + ["Edited Sites"]) st.dataframe(df) st.subheader("Reconstructed Image") recon = binary_labels_to_rgb_image(binary_labels) st.image(recon, caption="Reconstructed", use_column_width=True) st.download_button("Download CSV", df.to_csv(index=False), "image_binary_labels.csv") # Tab 3: EF → Binary with tab3: 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 4455.") ef_file = st.file_uploader("Upload EF CSV", type=["csv"], key="ef") ascending_input_headers = sorted([h for h in mutation_site_headers if 3244 <= h <= 4455]) if ef_file: ef_df = pd.read_csv(ef_file, header=None) ef_df.columns = [str(site) for site in ascending_input_headers] else: ef_df = pd.DataFrame(columns=[str(site) for site in ascending_input_headers]) edited_df = st.data_editor(ef_df, num_rows="dynamic") if st.button("Convert to Binary Labels"): # Use ascending headers to create binary first binary_part = pd.DataFrame() for col in ascending_input_headers: col_str = str(col) threshold = thresholds[col] binary_part[col_str] = (edited_df[col_str].astype(float) >= threshold).astype(int) # Rearranged for output: custom order from mutation_site_headers 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") # Reconstruct original string from binary values (flatten row-wise) for i, row in binary_reordered.iterrows(): binary_sequence = row.tolist() text = binary_labels_to_string(binary_sequence) st.write(f"Row {i+1} decoded string: {text}") # # Tab 3: EF → Binary # with tab3: # st.write("Upload an Editing Frequency CSV or enter manually:") # st.write("**Note:** Please upload CSV files **without column headers**. Just the 31 editing frequencies per row.") # ef_file = st.file_uploader("Upload EF CSV", type=["csv"], key="ef") # if ef_file: # # Read CSV without headers and assign mutation site headers # ef_df = pd.read_csv(ef_file, header=None) # ef_df.columns = [str(site) for site in mutation_site_headers] # else: # ef_df = pd.DataFrame(columns=[str(site) for site in mutation_site_headers]) # edited_df = st.data_editor(ef_df, num_rows="dynamic") # if st.button("Convert to Binary Labels"): # int_map = {str(k): k for k in thresholds.index} # matching_cols = [col for col in edited_df.columns if col in int_map] # binary_part = pd.DataFrame() # for col in matching_cols: # col_threshold = thresholds[int_map[col]] # binary_part[col] = (edited_df[col].astype(float) >= col_threshold).astype(int) # non_binary_part = edited_df.drop(columns=matching_cols, errors='ignore') # binary_df = pd.concat([non_binary_part, binary_part], axis=1) # def color_binary(val): # if val == 1: return "background-color: lightgreen" # if val == 0: return "background-color: lightcoral" # return "" # st.subheader("Binary Labels") # styled = binary_df.style.applymap(color_binary, subset=matching_cols) # st.dataframe(styled) # st.download_button("Download CSV", binary_df.to_csv(index=False), "ef_binary_labels.csv") # # Convert to bitstrings and strings # binary_strings = [] # decoded_strings = [] # for _, row in binary_part.iterrows(): # bitlist = row.values.tolist() # bitstring = ''.join(str(b) for b in bitlist) # binary_strings.append(bitstring) # decoded_strings.append(binary_labels_to_string(bitlist)) # st.subheader("Binary as Bitstrings") # for b in binary_strings: # st.code(b) # st.subheader("Decoded Voyager Strings") # for s in decoded_strings: # st.write(s)