Update app.py

app.py

@@ -1,16 +1,12 @@
 import streamlit as st
 import pandas as pd
+import io
 
 # =========================
 # Streamlit App Setup
 # =========================
-st.set_page_config(page_title="ASCII → Binary Label Converter", layout="wide")
-st.title("🔢 ASCII → Binary Label Converter")
-
-st.markdown("""
-Convert text into binary labels using the **Voyager 6-bit ASCII table**.  
-You can control how many positions (columns) are grouped per row.
-""")
+st.set_page_config(page_title="ASCII ↔ Binary Converter", layout="wide")
+st.title("🔢 ASCII ↔ Binary Converter")
 
 # =========================
 # Voyager ASCII 6-bit Table
@@ -27,12 +23,10 @@ voyager_table = {
 }
 reverse_voyager_table = {v: k for k, v in voyager_table.items()}
 
-
 # =========================
 # Helper Functions
 # =========================
 def string_to_binary_labels(s: str) -> list[int]:
-    """Convert string to list of 0/1 bits using the 6-bit Voyager table."""
     bits = []
     for char in s:
         val = reverse_voyager_table.get(char.upper(), 0)
@@ -40,9 +34,7 @@ def string_to_binary_labels(s: str) -> list[int]:
         bits.extend(char_bits)
     return bits
 
-
 def binary_labels_to_string(bits: list[int]) -> str:
-    """Convert list of 0/1 bits back to string using Voyager table."""
     chars = []
     for i in range(0, len(bits), 6):
         chunk = bits[i:i+6]
@@ -52,58 +44,124 @@ def binary_labels_to_string(bits: list[int]) -> str:
         chars.append(voyager_table.get(val, '?'))
     return ''.join(chars)
 
-
-# =========================
-# User Controls
-# =========================
-st.subheader("Step 1 – Input Text")
-user_input = st.text_input("Enter your text:", value="DNA", key="input_text")
-
-col1, col2 = st.columns([2, 1])
-with col1:
-    group_size = st.slider("Select number of positions per group:", min_value=12, max_value=32, value=25)
-with col2:
-    custom_cols = st.number_input("Or enter custom number:", min_value=1, max_value=128, value=group_size)
-if custom_cols != group_size:
-    group_size = custom_cols
-
 # =========================
-# Conversion and Display
+# Tabs
 # =========================
-if user_input:
-    binary_labels = string_to_binary_labels(user_input)
-
-    st.markdown("### Step 2 – Binary Labels per Character")
-    st.caption("Scroll to view all characters")
-
-    # Prepare scrollable block
-    grouped_bits = [binary_labels[i:i+6] for i in range(0, len(binary_labels), 6)]
-    scroll_html = "<div style='max-height: 300px; overflow-y: auto; font-family: monospace; padding: 6px; border: 1px solid #ccc;'>"
-    for i, bits in enumerate(grouped_bits):
-        ch = user_input[i] if i < len(user_input) else "?"
-        scroll_html += f"<div>'{ch}' → {bits}</div>"
-    scroll_html += "</div>"
-    st.markdown(scroll_html, unsafe_allow_html=True)
-
-    # Group into user-selected positions
-    st.markdown("### Step 3 – Grouped Binary Matrix")
-    groups = []
-    for i in range(0, len(binary_labels), group_size):
-        group = binary_labels[i:i+group_size]
-        if len(group) < group_size:
-            group += [0] * (group_size - len(group))
-        groups.append(group)
-
-    # Build DataFrame
-    columns = [f"Position {i+1}" for i in range(group_size)]
-    df = pd.DataFrame(groups, columns=columns)
-
-    st.dataframe(df, use_container_width=True)
-    st.download_button(
-        "⬇️ Download as CSV",
-        df.to_csv(index=False),
-        file_name=f"binary_labels_{group_size}_positions.csv",
-        mime="text/csv"
-    )
-else:
-    st.info("👆 Enter text above to see binary labels.")
+tab1, tab2 = st.tabs(["Text → Binary", "Binary → Text"])
+
+# --------------------------------------------------
+# TAB 1: Text → Binary
+# --------------------------------------------------
+with tab1:
+    st.markdown("""
+    Convert any text into binary labels using the **Voyager 6-bit ASCII table**.  
+    You can control how many positions (columns) are grouped per row.
+    """)
+
+    st.subheader("Step 1 – Input Text")
+    user_input = st.text_input("Enter your text:", value="DNA", key="input_text")
+
+    col1, col2 = st.columns([2, 1])
+    with col1:
+        group_size = st.slider("Select number of positions per group:", min_value=12, max_value=32, value=25)
+    with col2:
+        custom_cols = st.number_input("Or enter custom number:", min_value=1, max_value=128, value=group_size)
+    if custom_cols != group_size:
+        group_size = custom_cols
+
+    if user_input:
+        binary_labels = string_to_binary_labels(user_input)
+        binary_concat = ''.join(map(str, binary_labels))
+
+        # Step 2: Binary Labels per Character
+        st.markdown("### Step 2 – Binary Labels per Character")
+        st.caption("Scroll to view all characters")
+
+        # Scrollable block
+        grouped_bits = [binary_labels[i:i+6] for i in range(0, len(binary_labels), 6)]
+        scroll_html = "<div style='max-height: 300px; overflow-y: auto; font-family: monospace; padding: 6px; border: 1px solid #ccc;'>"
+        for i, bits in enumerate(grouped_bits):
+            ch = user_input[i] if i < len(user_input) else "?"
+            scroll_html += f"<div>'{ch}' → {bits}</div>"
+        scroll_html += "</div>"
+        st.markdown(scroll_html, unsafe_allow_html=True)
+
+        # Download full concatenated binary text
+        st.download_button(
+            "⬇️ Download Full Binary (.txt)",
+            data=binary_concat,
+            file_name="binary_full.txt",
+            mime="text/plain",
+            key="download_binary_txt"
+        )
+
+        # Step 3: Grouped Binary Matrix
+        st.markdown("### Step 3 – Grouped Binary Matrix")
+        groups = []
+        for i in range(0, len(binary_labels), group_size):
+            group = binary_labels[i:i+group_size]
+            if len(group) < group_size:
+                group += [0] * (group_size - len(group))
+            groups.append(group)
+
+        columns = [f"Position {i+1}" for i in range(group_size)]
+        df = pd.DataFrame(groups, columns=columns)
+        st.dataframe(df, use_container_width=True)
+
+        st.download_button(
+            "⬇️ Download as CSV",
+            df.to_csv(index=False),
+            file_name=f"binary_labels_{group_size}_positions.csv",
+            mime="text/csv",
+            key="download_binary_csv"
+        )
+    else:
+        st.info("👆 Enter text above to see binary labels.")
+
+# --------------------------------------------------
+# TAB 2: Binary → Text
+# --------------------------------------------------
+with tab2:
+    st.markdown("""
+    Convert binary data back into readable text.  
+    Upload either:
+    - `.csv` file with 0/1 values (any number of columns/rows)  
+    - `.xlsx` Excel file  
+    - `.txt` file containing a concatenated binary string (e.g. `010101...`)
+    """)
+
+    uploaded = st.file_uploader("Upload your file (.csv, .xlsx, or .txt):", type=["csv", "xlsx", "txt"])
+
+    if uploaded is not None:
+        try:
+            if uploaded.name.endswith(".csv"):
+                df = pd.read_csv(uploaded)
+                bits = df.values.flatten().astype(int).tolist()
+            elif uploaded.name.endswith(".xlsx"):
+                df = pd.read_excel(uploaded)
+                bits = df.values.flatten().astype(int).tolist()
+            elif uploaded.name.endswith(".txt"):
+                content = uploaded.read().decode().strip()
+                bits = [int(b) for b in content if b in ['0', '1']]
+            else:
+                bits = []
+
+            if not bits:
+                st.warning("No binary data detected.")
+            else:
+                recovered_text = binary_labels_to_string(bits)
+                st.success("✅ Conversion complete!")
+                st.markdown("**Recovered text:**")
+                st.text_area("Output", recovered_text, height=150)
+
+                st.download_button(
+                    "⬇️ Download Recovered Text (.txt)",
+                    data=recovered_text,
+                    file_name="recovered_text.txt",
+                    mime="text/plain",
+                    key="download_recovered"
+                )
+        except Exception as e:
+            st.error(f"Error reading or converting file: {e}")
+    else:
+        st.info("👆 Upload a file to start the reverse conversion.")