Update app.py

app.py

@@ -1,15 +1,11 @@
 import streamlit as st
-from PIL import Image
+from PIL import Image, ImageFilter
 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)
@@ -17,27 +13,30 @@ def string_to_binary_labels(s: str) -> list[int]:
         bits.extend(char_bits)
     return bits
 
-def image_to_binary_labels_rgb(img: Image.Image, max_pixels: int = 256) -> list[int]:
+def clean_image(img: Image.Image, min_size: int = 256) -> Image.Image:
     """
-    Convert an RGB image to binary labels (0/1).
-    Store full RGB values (24 bits per pixel).
+    Preprocess image to reduce noise: resize if needed and apply light blur.
     """
     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:  # R, G, B
+        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:
-    """
-    Convert binary labels (0/1) into an RGB image.
-    """
     total_pixels = len(binary_labels) // 24
     if width is None or height is None:
         side = int(np.ceil(np.sqrt(total_pixels)))
@@ -106,7 +105,7 @@ with tab1:
 
         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 Table as CSV",
             data=df.to_csv(index=False),
@@ -114,7 +113,6 @@ with tab1:
             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"])
@@ -123,7 +121,7 @@ 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=4096, max_value=16384, value=8192, step=32)
+        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_rgb(img, max_pixels=max_pixels)
 
@@ -148,9 +146,9 @@ with tab2:
         st.image(reconstructed_img, caption="Reconstructed Image", use_column_width=True)
 
         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",
+            label="Download Image Binary Labels Table as CSV",
+            data=df.to_csv(index=False),
+            file_name="image_binary_labels_table.csv",
             mime="text/csv"
         )