Update app.py

app.py

@@ -2,39 +2,27 @@ import streamlit as st
 from PIL import Image
 import numpy as np
 import pandas as pd
+from matplotlib import cm
 
 # 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 = img.convert("L")
+    img.thumbnail((int(np.sqrt(max_pixels)), int(np.sqrt(max_pixels))))
     img_array = np.array(img)
     flat = img_array.flatten()
-    binarized = (flat > 128).astype(int)  # Threshold at 128 to 0/1
+    binarized = (flat > 128).astype(int)
     return binarized.tolist()
 
 def binary_labels_to_image(binary_labels: list[int], width: int = None, height: int = None) -> Image.Image:
-    """
-    Convert binary labels (0/1) back into an image.
-    If width and height are not provided, assumes a square image.
-    """
     total_pixels = len(binary_labels)
     if width is None or height is None:
         side = int(np.ceil(np.sqrt(total_pixels)))
@@ -47,7 +35,13 @@ def binary_labels_to_image(binary_labels: list[int], width: int = None, height:
     img = Image.fromarray(image_array, mode='L')
     return img
 
-# Predefined headers for the 32 mutation sites
+def colorize_grayscale(img: Image.Image) -> Image.Image:
+    img_array = np.array(img) / 255.0
+    colored_array = cm.viridis(img_array)[:, :, :3]
+    colored_array = (colored_array * 255).astype(np.uint8)
+    color_img = Image.fromarray(colored_array, mode='RGB')
+    return color_img
+
 mutation_site_headers = [
     3244, 3297, 3350, 3399, 3455, 3509, 3562, 3614,
     3665, 3720, 3773, 3824, 3879, 3933, 3985, 4039,
@@ -57,7 +51,6 @@ mutation_site_headers = [
 
 st.title("ASCII & Binary Label Converter")
 
-# Create tabs
 tab1, tab2 = st.tabs(["Text to Binary Labels", "Image to Binary Labels"])
 
 with tab1:
@@ -107,7 +100,7 @@ with tab2:
         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=4112, value=2056, step=32)
+        max_pixels = st.slider("Max number of pixels to encode", min_value=32, max_value=2056, value=1024, step=32)
 
         binary_labels = image_to_binary_labels(img, max_pixels=max_pixels)
 
@@ -137,6 +130,10 @@ with tab2:
         st.subheader("Reconstruct Image from Binary Labels")
         if st.button("Reconstruct Image"):
             reconstructed_img = binary_labels_to_image(binary_labels)
-            st.image(reconstructed_img, caption="Reconstructed Image", use_column_width=True)
+            st.image(reconstructed_img, caption="Reconstructed Grayscale Image", use_column_width=True)
+
+            st.subheader("Colorized Image (Fake Color)")
+            colorized_img = colorize_grayscale(reconstructed_img)
+            st.image(colorized_img, caption="Colorized Reconstructed Image", use_column_width=True)
 
 # Future: integrate DNA editor mapping for each mutation site here