Update app.py

app.py

@@ -717,3 +717,180 @@ with tab5:
         st.dataframe(source_volume_df)
         st.download_button("Download Source Volumes", source_volume_df.to_csv(index=False), "source_total_volumes.csv", key="download_volume_any")
 
+
+
+import streamlit as st
+import pandas as pd
+
+# === App Title ===
+with tab5: 
+    st.header("Robot Script Generator")
+    
+# === 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()}
+
+# === Binary ↔ String conversion ===
+def binary_labels_to_string(bits: list[int]) -> str:
+    chars = []
+    for i in range(0, len(bits), 6):
+        chunk = bits[i:i+6]
+        if len(chunk) < 6:
+            chunk += [0] * (6 - len(chunk))
+        val = sum(b << (5 - j) for j, b in enumerate(chunk))
+        chars.append(voyager_table.get(val, '?'))
+    return ''.join(chars)
+
+# === Well mapping ===
+def get_well_position(sample_index):
+    row_letter = chr(65 + (sample_index - 1) // 12)
+    col_number = ((sample_index - 1) % 12) + 1
+    return f"{row_letter}{col_number}"
+
+# === Track and replace source if volume exceeded ===
+def track_and_replace_source(source_list, robot_script, volume_limit=150):
+    source_volumes = {}
+    adjusted_sources = []
+    for entry in robot_script:
+        src = entry['Source']
+        vol = entry['Volume']
+        source_volumes[src] = source_volumes.get(src, 0) + vol
+        if source_volumes[src] > volume_limit:
+            row_letter = src[0]
+            col_number = src[1:]
+            new_row_letter = chr(ord(row_letter) + 4)
+            new_src = f"{new_row_letter}{col_number}"
+            entry['Source'] = new_src
+            source_volumes[new_src] = source_volumes.get(new_src, 0) + vol
+            source_volumes[src] -= vol
+        adjusted_sources.append(entry)
+    return adjusted_sources, source_volumes
+
+# === Fixed D-source transfers ===
+def generate_fixed_d_source_instructions_to_all_samples(n_samples, fixed_volume=16, volume_limit=170):
+    d_source_volumes = {}
+    d_source_script = []
+    current_d_index = 1
+    for i in range(n_samples):
+        dest = get_well_position(i + 1)
+        current_d_well = f"D{current_d_index}"
+        d_source_volumes.setdefault(current_d_well, 0)
+
+        if d_source_volumes[current_d_well] + fixed_volume > volume_limit:
+            current_d_index += 1
+            current_d_well = f"D{current_d_index}"
+            d_source_volumes[current_d_well] = 0
+
+        d_source_volumes[current_d_well] += fixed_volume
+
+        # ✅ Updated: use TS_50 if volume >10 µL, else TS_10
+        tool = 'TS_50' if fixed_volume > 10 else 'TS_10'
+        d_source_script.append({
+            'Source': current_d_well,
+            'Destination': dest,
+            'Volume': fixed_volume,
+            'Tool': tool
+        })
+
+    return d_source_script, d_source_volumes
+
+# === Source well generation ===
+def generate_source_wells(n):
+    wells, rows = [], 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
+    for i in range(n):
+        row, col = rows[i // 12], (i % 12) + 1
+        wells.append(f"{row}{col}")
+    return wells
+
+# === Main UI ===
+st.header("Upload Binary Data (0/1)")
+
+binary_file = st.file_uploader("Upload Binary CSV", type=["csv"])
+st.divider()
+
+st.subheader("Optional Metadata")
+barcode_id_input = st.text_input("Barcode ID (optional)", value="")
+labware_source_input = st.text_input("Labware Source ID", value="1")
+labware_dest_input = st.text_input("Labware Destination ID", value="1")
+name_input = st.text_input("Name field (optional)", value="")
+volume_limit_input = st.number_input("Maximum Volume per Source Well (µL)", value=150, min_value=10, step=10)
+
+# === Load Data ===
+if binary_file:
+    df_binary = pd.read_csv(binary_file, header=None)
+    df_binary.columns = [str(i+1) for i in range(df_binary.shape[1])]
+else:
+    st.info("No file uploaded — manually enter binary data below.")
+    df_binary = st.data_editor(
+        pd.DataFrame(columns=[str(i) for i in range(1, 33)]),
+        num_rows="dynamic", key="manual_input"
+    )
+
+if not df_binary.empty:
+    st.subheader("Binary Matrix")
+    st.dataframe(df_binary.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral"))
+    st.download_button("⬇️ Download Binary CSV", df_binary.to_csv(index=False), "binary_matrix.csv")
+
+    # Decode to string
+    decoded = binary_labels_to_string(df_binary.values.flatten().astype(int).tolist())
+    st.subheader("Decoded String Output")
+    st.code(decoded)
+    st.download_button("⬇️ Download Decoded String", decoded, "decoded_string.txt")
+
+    # === Generate Robot Script ===
+    st.divider()
+    st.subheader("Generated Robot Script")
+
+    df_robot = df_binary.copy()
+    df_robot.insert(0, 'Sample', range(1, len(df_robot) + 1))
+    df_robot['# donors'] = df_robot.iloc[:, 1:].astype(int).sum(axis=1)
+    df_robot['volume donors (µL)'] = 64 / df_robot['# donors']
+
+    robot_script = []
+    source_wells = generate_source_wells(df_robot.shape[1] - 1)
+
+    for i, col in enumerate(df_robot.columns[1:]):
+        for _, sample in df_robot.iterrows():
+            if int(sample[col]) == 1:
+                source = source_wells[i]
+                dest = get_well_position(int(sample['Sample']))
+                vol = round(sample['volume donors (µL)'], 2)
+
+                # ✅ Updated: use TS_50 for volumes >10 µL, TS_10 otherwise
+                tool = 'TS_50' if vol > 10 else 'TS_10'
+                robot_script.append({'Source': source, 'Destination': dest, 'Volume': vol, 'Tool': tool})
+
+    robot_script, source_volumes = track_and_replace_source(source_wells, robot_script, volume_limit=volume_limit_input)
+    d_script, d_volumes = generate_fixed_d_source_instructions_to_all_samples(
+        len(df_robot), fixed_volume=16, volume_limit=volume_limit_input
+    )
+
+    full_script = robot_script + d_script
+
+    robot_script_df = pd.DataFrame(full_script)
+    robot_script_df.insert(0, 'Barcode ID', barcode_id_input)
+    robot_script_df.insert(1, 'Labware_Source', labware_source_input)
+    robot_script_df.insert(3, 'Labware_Destination', labware_dest_input)
+    robot_script_df['Name'] = name_input
+    robot_script_df = robot_script_df[['Barcode ID', 'Labware_Source', 'Source',
+                                       'Labware_Destination', 'Destination', 'Volume', 'Tool', 'Name']]
+
+    st.dataframe(robot_script_df)
+    st.download_button("⬇️ Download Robot Script", robot_script_df.to_csv(index=False), "robot_script.csv")
+
+    # === Source Volume Summary ===
+    st.divider()
+    st.subheader("Total Volume Used Per Source")
+    combined_volumes = {**source_volumes, **d_volumes}
+    volume_df = pd.DataFrame(list(combined_volumes.items()), columns=['Source', 'Total Volume (µL)'])
+    st.dataframe(volume_df)
+    st.download_button("⬇️ Download Volume Summary", volume_df.to_csv(index=False), "source_volumes.csv")