Update app.py

app.py

@@ -71,7 +71,7 @@ def binary_labels_to_string(bits: list[int]) -> str:
 # === Streamlit App ===
 
 st.title("ASCII & Binary Label Converter")
-tab1, tab2 = st.tabs(["Text to Binary Labels (31)", "EF → Binary (31)"])
+tab1, tab2, tab3, tab4 = st.tabs(["Text to Binary Labels (31)", "EF → Binary (31)", "Text to Binary Labels (32)", "EF → Binary (2)"])
 
 # Tab 1: Text to Binary
 with tab1:
@@ -182,3 +182,161 @@ with tab2:
         decoded_string = binary_labels_to_string(all_bits)
         st.subheader("Decoded String (continuous across rows)")
         st.write(decoded_string)
+
+
+# Mutation site headers did not remove 3614,
+mutation_site_headers_actual_3614 = [
+    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
+]
+# Thresholds for each mutation site removed 3614: 0.091557752,
+thresholds_actual_3614 = pd.Series({
+    3244: 1.096910677, 3297: 0.923658795, 3350: 0.668939037, 3399: 0.914305214,
+    3455: 1.297392984, 3509: 1.812636208, 3562: 1.185047484, 3614: 0.157969131375,
+    3665: 0.298007308, 3720: 0.58857544, 3773: 0.882561082, 3824: 1.149082617,
+    3879: 0.816050702, 3933: 2.936517653, 3985: 1.597166791, 4039: 0.962108082,
+    4089: 1.479783497, 4145: 0.305853225, 4190: 1.311869541, 4245: 1.707556905,
+    4298: 0.875013076, 4349: 1.227704526, 4402: 0.593206446, 4455: 1.179633137,
+    4510: 1.272477799, 4561: 1.293841573, 4615: 1.16821885, 4668: 1.40306,
+    4720: 0.706530878, 4773: 1.483114072, 4828: 0.954939873, 4882: 1.47524328
+})
+
+# Mutation site headers reordered: 4402 to 3244, 4882 to 4455
+mutation_site_headers_3614 = [
+    4402, 4349, 4298, 4245, 4190, 4145, 4089, 4039,
+    3985, 3933, 3879, 3824, 3773, 3720, 3665, 3614
+    3562, 3509, 3455, 3399, 3350, 3297, 3244,  # 1–23
+    4882, 4828, 4773, 4720, 4668, 4615, 4561, 4510, 4455   # 24–32
+]
+
+# Thresholds reordered accordingly
+thresholds_3614 = pd.Series({h: thresholds_actual_3614[h] for h in mutation_site_headers_3614})
+
+# === Utility functions ===
+
+# Voyager ASCII 6-bit conversion table
+voyager_table = {
+    i: ch for i, ch in enumerate([
+        ' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I',
+        'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S',
+        'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '0', '1', '2',
+        '3', '4', '5', '6', '7', '8', '9', '.', '(', ')',
+        '+', '-', '*', '/', '=', '$', '!', ':', '%', '"',
+        '#', '@', '\'', '?', '&'
+    ])
+}
+reverse_voyager_table = {v: k for k, v in voyager_table.items()}
+
+
+# Tab 3: Text to Binary (32)
+with tab3:
+    user_input = st.text_input("Enter text", value="DNA")
+    if user_input:
+        ascii_codes = [ord(c) for c in user_input]
+        binary_labels = string_to_binary_labels(user_input)
+
+        st.subheader("ASCII Codes")
+        st.write(ascii_codes)
+
+        st.subheader("Binary Labels per Character")
+        grouped = [binary_labels[i:i+6] for i in range(0, len(binary_labels), 6)]
+        for i, bits in enumerate(grouped):
+            st.write(f"'{user_input[i]}' → {bits}")
+
+        st.subheader("Binary Labels (32-bit groups)")
+        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_3614] + ["Edited Sites"])
+        st.dataframe(df)
+        st.download_button("Download as CSV", df.to_csv(index=False), "text_32_binary_labels.csv")
+
+        ascending_headers = sorted(mutation_site_headers_actual_3614)
+        df_sorted = df[[str(h) for h in ascending_headers if str(h) in df.columns]]
+        st.subheader("Binary Labels (Ascending Order 3244 → 4882)")
+        st.dataframe(df_sorted)
+        st.download_button("Download Ascending Order CSV", df_sorted.to_csv(index=False), "text_binary_labels_ascending.csv")
+
+        # === Robot Preparation Script Generation ===
+        st.subheader("Robot Preparation Script")
+        robot_template = pd.read_csv("/home/user/app/Robot.csv", skiprows=3)
+        robot_template.columns = ['Labware', 'Source', 'Labware_2', 'Destination', 'Volume', 'Tool', 'Name']
+
+        # Add Sample numbers for well referencing
+        df_sorted.insert(0, 'Sample', range(1, len(df_sorted)+1))
+
+        # Step 1: Count the number of edited sites per row
+        df_sorted['# donors'] = df_sorted.iloc[:, 1:].sum(axis=1)
+
+        # Step 2: Calculate volume per donor (32 / # donors)
+        df_sorted['volume donors (µl)'] = 32 / df_sorted['# donors']
+
+        # Step 3: Generate the robot script
+        robot_script = []
+        source_wells = robot_template['Source'].unique().tolist()[:32]
+
+        for i, col in enumerate(df_sorted.columns[1:33]):
+            for row_idx, sample in df_sorted.iterrows():
+                if sample[col] == 1:
+                    source = source_wells[i]
+                    dest = f"A{sample['Sample']}"
+                    vol = round(sample['volume donors (µl)'], 2)
+                    robot_script.append({'Source': source, 'Destination': dest, 'Volume': vol})
+
+        robot_script_df = pd.DataFrame(robot_script)
+        st.dataframe(robot_script_df)
+        st.download_button("Download Robot Script CSV", robot_script_df.to_csv(index=False), "robot_script.csv")
+
+
+# Tab 2: EF → Binary
+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 4882.")
+    ef_file = st.file_uploader("Upload EF CSV", type=["csv"], key="ef")
+
+    if ef_file:
+        ef_df = pd.read_csv(ef_file, header=None)
+        ef_df.columns = [str(site) for site in sorted(mutation_site_headers_actual_3614)]
+    else:
+        ef_df = pd.DataFrame(columns=[str(site) for site in sorted(mutation_site_headers_actual_3614)])
+
+    edited_df = st.data_editor(ef_df, num_rows="dynamic")
+
+    if st.button("Convert to Binary Labels"):
+        binary_part = pd.DataFrame()
+        for col in sorted(mutation_site_headers_actual_3614):
+            col_str = str(col)
+            threshold = thresholds_actual_3614[col]
+            binary_part[col_str] = (edited_df[col_str].astype(float) >= threshold).astype(int)
+
+        binary_reordered = binary_part[[str(h) for h in mutation_site_headers_3614 if str(h) in binary_part.columns]]
+
+        def color_binary(val):
+            if val == 1: return "background-color: lightgreen"
+            if val == 0: return "background-color: lightcoral"
+            return ""
+
+        st.subheader("Binary Labels (Reordered 4402→3244, 4882→4455)")
+        styled = binary_reordered.style.applymap(color_binary)
+        st.dataframe(styled)
+        st.download_button("Download CSV", binary_reordered.to_csv(index=False), "ef_binary_labels.csv")
+
+        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)
+
+        st.subheader("Binary Labels (Ascending 3244→4882)")
+        st.dataframe(binary_part.style.applymap(color_binary))
+        st.download_button("Download Ascending Order CSV", binary_part.to_csv(index=False), "ef_binary_labels_ascending.csv")
+
+        all_bits = binary_part.values.flatten().tolist()
+        decoded_string = binary_labels_to_string(all_bits)
+        st.subheader("Decoded String (continuous across rows)")
+        st.write(decoded_string)
+