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0f8ddfb 126fff1 0f8ddfb 126fff1 0f8ddfb 126fff1 0f8ddfb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | import streamlit as st
from PIL import Image
import numpy as np
import pandas as pd
# Simple app: convert user input into ASCII codes and binary labels
def string_to_binary_labels(s: str) -> list[int]:
"""
Convert a string into a flat list of binary labels (0 or 1) representing
each character's 8-bit ASCII code.
"""
bits: list[int] = []
for char in s:
ascii_code = ord(char)
# Extract 8-bit binary representation (MSB first)
char_bits = [(ascii_code >> bit) & 1 for bit in range(7, -1, -1)]
bits.extend(char_bits)
return bits
def image_to_binary_labels(img: Image.Image, max_pixels: int = 256) -> list[int]:
"""
Convert an image to binary labels.
Convert to grayscale, resize to limit pixel count, and binarize (threshold 128).
"""
img = img.convert("L") # Convert to grayscale
img.thumbnail((int(np.sqrt(max_pixels)), int(np.sqrt(max_pixels)))) # Resize while maintaining aspect ratio
img_array = np.array(img)
flat = img_array.flatten()
binarized = (flat > 128).astype(int) # Threshold at 128 to 0/1
return binarized.tolist()
# Predefined headers for the 32 mutation sites
mutation_site_headers = [
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
]
st.title("ASCII & Binary Label Converter")
# Create tabs
tab1, tab2 = st.tabs(["Text to Binary Labels", "Image to Binary Labels"])
with tab1:
st.write("Enter text to see its ASCII codes and corresponding binary labels:")
user_input = st.text_input("Text Input", value="DNA")
if user_input:
# Compute ASCII codes
ascii_codes = [ord(c) for c in user_input]
# Compute binary labels
binary_labels = string_to_binary_labels(user_input)
st.subheader("ASCII Codes")
st.write(ascii_codes)
st.subheader("Binary Labels per Character")
# Display bits grouped per character for readability
grouped_chars = [binary_labels[i:i+8] for i in range(0, len(binary_labels), 8)]
for idx, bits in enumerate(grouped_chars):
st.write(f"'{user_input[idx]}' → {bits}")
# New section: 32-bit group display in table format
st.subheader("Binary Labels (32-bit groups)")
num_groups = (len(binary_labels) + 31) // 32
table_data = []
for grp_idx in range(num_groups):
start = grp_idx * 32
end = start + 32
group = binary_labels[start:end]
if len(group) < 32:
group += [0] * (32 - len(group))
edited_sites = sum(group)
row = group + [edited_sites]
table_data.append(row)
df = pd.DataFrame(table_data, columns=[str(h) for h in mutation_site_headers] + ["Edited Sites"])
st.dataframe(df)
st.download_button(
label="Download Binary Labels as CSV",
data=','.join(str(b) for b in binary_labels),
file_name="binary_labels.csv",
mime="text/csv"
)
with tab2:
st.write("Upload an image (JPG or PNG) to convert it into binary labels:")
uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
img = Image.open(uploaded_file)
st.image(img, caption="Uploaded Image", use_column_width=True)
max_pixels = st.slider("Max number of pixels to encode", min_value=32, max_value=1024, value=256, step=32)
binary_labels = image_to_binary_labels(img, max_pixels=max_pixels)
st.subheader("Binary Labels from Image")
num_groups = (len(binary_labels) + 31) // 32
table_data = []
for grp_idx in range(num_groups):
start = grp_idx * 32
end = start + 32
group = binary_labels[start:end]
if len(group) < 32:
group += [0] * (32 - len(group))
edited_sites = sum(group)
row = group + [edited_sites]
table_data.append(row)
df = pd.DataFrame(table_data, columns=[str(h) for h in mutation_site_headers] + ["Edited Sites"])
st.dataframe(df)
st.download_button(
label="Download Image Binary Labels as CSV",
data=','.join(str(b) for b in binary_labels),
file_name="image_binary_labels.csv",
mime="text/csv"
)
# Future: integrate DNA editor mapping for each mutation site here
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