Update app.py

app.py

@@ -144,7 +144,7 @@ def binary_labels_to_rgb_image(binary_labels: list[int], width: int = None, heig
 # === Streamlit App ===
 
 st.title("ASCII & Binary Label Converter")
-tab1, tab2, tab3 = st.tabs(["Text to Binary Labels", "Image to Binary Labels", "EF → Binary"])
+tab1, tab2 = st.tabs(["Text to Binary Labels (31)", "EF → Binary (31)"])
 
 # Tab 1: Text to Binary
 with tab1:
@@ -170,7 +170,7 @@ with tab1:
 
         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")
+        st.download_button("Download as CSV", df.to_csv(index=False), "text_31_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])
@@ -191,57 +191,34 @@ with tab1:
         # 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:
+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 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])
+    high_index_headers = sorted([h for h in mutation_site_headers if h >= 4480])
 
     if ef_file:
         ef_df = pd.read_csv(ef_file, header=None)
         ef_df.columns = [str(site) for site in ascending_input_headers]
+        for h in high_index_headers:
+            ef_df[str(h)] = 0  # add dummy columns for high index as 0
     else:
-        ef_df = pd.DataFrame(columns=[str(site) for site in ascending_input_headers])
+        ef_df = pd.DataFrame(columns=[str(site) for site in ascending_input_headers + high_index_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)
+        for col in high_index_headers:
+            binary_part[str(col)] = 0
 
-        # 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):
@@ -254,13 +231,11 @@ with tab3:
         st.dataframe(styled)
         st.download_button("Download CSV", binary_reordered.to_csv(index=False), "ef_binary_labels.csv")
 
-        # === NEW: Continuous decoding across rows ===
         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)
 
-        # Optional: ascending order output
         binary_ascending = binary_part[[str(h) for h in ascending_input_headers if str(h) in binary_part.columns]]
         st.subheader("Binary Labels (Ascending 3244→4455)")
         st.dataframe(binary_ascending)
@@ -321,3 +296,30 @@ with tab3:
 #         st.subheader("Decoded Voyager Strings")
 #         for s in decoded_strings:
 #             st.write(s)
+
+
+# # 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")