app.py

import streamlit as st
import pandas as pd
import io
import re
import numpy as np
import openpyxl

# =========================
# Streamlit App Setup
# =========================
st.set_page_config(page_title="ASCII ↔ Binary Converter", layout="wide")
st.title("ASCII ↔ Binary Converter")

# =========================
# Voyager ASCII 6-bit 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()}

# =========================
# Helper Functions
# =========================
def string_to_binary_labels(s: str) -> list[int]:
    bits = []
    for char in s:
        val = reverse_voyager_table.get(char.upper(), 0)
        char_bits = [(val >> bit) & 1 for bit in range(5, -1, -1)]
        bits.extend(char_bits)
    return bits

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)

# =========================
# Tabs
# =========================
tab1, tab2, tab3 = st.tabs(["Text → Binary", "Binary → Text", "Robot Script"])

# --------------------------------------------------
# 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 row:", 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.")

# --------------------------------------------------
# TAB 3: Pipetting Command Generator
# --------------------------------------------------
with tab3:
    import numpy as np
    import pandas as pd
    import re
    from math import ceil

    st.header("🧪 Pipetting Command Generator")
    st.markdown("""
    Upload your sample file (Excel, CSV, or TXT) containing binary mutation data.
    The app will:
    - Auto-detect or create `Sample`, `Position#`, `Total edited`, and `Volume per "1"` columns  
    - Let you set the **Desired total volume per sample (µL)** used to compute `Volume per "1"`  
    - Calculate total demand per input and suggest a **uniform layout** (same # consecutive wells per input)  
    - **Preview** the layout on a plate map (with tooltips)  
    - After confirmation, generate pipetting commands and a source volume summary  
    """)

    uploaded = st.file_uploader("📤 Upload data file", type=["xlsx", "csv", "txt"])
    max_per_well_ul = st.number_input(
        "Maximum volume per source well (µL)",
        min_value=10.0, max_value=2000.0, value=160.0, step=10.0
    )

    # ---------- Helpers (plate geometry, parsing, viz) ----------
    ROWS_96 = ["A", "B", "C", "D", "E", "F", "G", "H"]
    COLS_96 = list(range(1, 13))

    def well_name(row_letter, col_number):
        return f"{row_letter}{col_number}"

    def enumerate_plate_wells():
        """Yield wells A1..A12, B1..B12, ..., H12 for a single plate."""
        for r in ROWS_96:
            for c in COLS_96:
                yield f"{r}{c}"

    def parse_well_name(well: str):
        """Split 'A1'/'H12' → (row_letter, col_num). Robust to stray spaces."""
        m = re.match(r"([A-Ha-h])\s*([0-9]+)", str(well).strip())
        if not m:
            return ("A", 0)
        return (m.group(1).upper(), int(m.group(2)))

    def sample_index_to_plate_and_well(sample_idx: int):
        """Destination mapping: 96-well plates in reading order, extends to multiple plates."""
        plate_num = ((sample_idx - 1) // 96) + 1
        within_plate = (sample_idx - 1) % 96
        row_idx = within_plate // 12
        col_idx = within_plate % 12
        return plate_num, well_name(ROWS_96[row_idx], COLS_96[col_idx])

    def build_global_wells_list(n_plates: int):
        out = []
        for p in range(1, n_plates + 1):
            for w in enumerate_plate_wells():
                out.append((p, w))
        return out

    def pick_tool(volume_ul: float) -> str:
        return "TS_10" if volume_ul <= 10.0 else "TS_50"

    # Color palette (cycled if many inputs)
    PALETTE = [
        "#4F46E5", "#22C55E", "#F59E0B", "#EF4444", "#06B6D4", "#A855F7", "#84CC16", "#F97316",
        "#0EA5E9", "#E11D48", "#10B981", "#7C3AED", "#15803D", "#EA580C", "#2563EB", "#DC2626"
    ]

    def render_plate_map_html(plates_used, well_to_input, max_wells_per_source, inputs_count):
        """Fancy HTML plate grids with tooltips."""
        legend_spans = []
        for i in range(1, inputs_count + 1):
            color = PALETTE[(i-1) % len(PALETTE)]
            legend_spans.append(
                f"<span style='display:inline-block;margin-right:12px'>"
                f"<span style='display:inline-block;width:12px;height:12px;background:{color};border:1px solid #333;margin-right:6px;vertical-align:middle'></span>"
                f"Input {i}</span>"
            )
        legend_html = "<div style='margin:8px 0 16px 0'>" + "".join(legend_spans) + "</div>"

        css = """
        <style>
        .plate { margin: 10px 0 24px 0; }
        .plate-title { font-weight: 600; margin: 4px 0 8px 0; }
        .grid { display: grid; grid-template-columns: 32px repeat(12, 38px); grid-auto-rows: 32px; gap: 4px; }
        .cell { width: 38px; height: 32px; border: 1px solid #DDD; display:flex; align-items:center; justify-content:center; font-size:12px; background:#FAFAFA; position:relative; }
        .head { font-weight:600; background:#F3F4F6; }
        .cell[data-color] { color:#111; }
        .cell .tip { visibility:hidden; opacity:0; transition:opacity 0.15s ease; position:absolute; bottom:100%; transform:translateY(-6px); left:50%; transform:translate(-50%, -6px); background:#111; color:#fff; padding:4px 6px; font-size:11px; border-radius:4px; white-space:nowrap; pointer-events:none; }
        .cell:hover .tip { visibility:visible; opacity:0.95; }
        </style>
        """

        body = [css, legend_html]
        for p in range(1, plates_used + 1):
            body.append(f"<div class='plate'><div class='plate-title'>Plate {p}</div>")
            body.append("<div class='grid'>")
            body.append("<div class='cell head'></div>")
            for c in COLS_96:
                body.append(f"<div class='cell head'>{c}</div>")
            for r in ROWS_96:
                body.append(f"<div class='cell head'>{r}</div>")
                for c in COLS_96:
                    well = f"{r}{c}"
                    key = (p, well)
                    if key in well_to_input:
                        input_idx, within_idx = well_to_input[key]
                        color = PALETTE[(input_idx-1) % len(PALETTE)]
                        tip = f"Input {input_idx} • P{p}:{well} • Block well {within_idx}/{max_wells_per_source}"
                        cell_html = (
                            f"<div class='cell' data-color style='background:{color};border-color:#555' title='{tip}'>"
                            f"<span class='tip'>{tip}</span>"
                            "</div>"
                        )
                    else:
                        cell_html = "<div class='cell'></div>"
                    body.append(cell_html)
            body.append("</div></div>")
        return "".join(body)

    # ---------- Main flow ----------
    if uploaded is not None:
        try:
            # --- Load file ---
            if uploaded.name.endswith(".xlsx"):
                df = pd.read_excel(uploaded)
            elif uploaded.name.endswith(".csv"):
                df = pd.read_csv(uploaded)
            else:  # TXT (tab-delimited try, else CSV)
                try:
                    df = pd.read_csv(uploaded, sep="\t")
                except Exception:
                    df = pd.read_csv(uploaded)

            st.success(f"✅ Loaded file with {len(df)} rows and {len(df.columns)} columns")

            # --- Clean column names ---
            df.columns = [str(c).strip() for c in df.columns]

            # --- Ensure Sample column ---
            if not any(c.lower() == "sample" for c in df.columns):
                df.insert(0, "Sample", np.arange(1, len(df) + 1))
                st.info("`Sample` column missing — automatically generated 1..N.")

            # --- Detect & numerically sort Position columns ---
            position_cols = [c for c in df.columns if re.match(r"(?i)^position\s*\d+", c)]
            if not position_cols:
                non_pos_cols = {"sample", "total edited", 'volume per "1"', "volume per 1"}
                candidate_cols = [c for c in df.columns if c.lower() not in non_pos_cols]
                position_cols = candidate_cols
                st.info(f"Position columns inferred automatically: {len(position_cols)} detected.")

            def pos_key(col_name: str):
                m = re.search(r"(\d+)", col_name)
                return int(m.group(1)) if m else 10**9
            position_cols = sorted(position_cols, key=pos_key)

            # Normalize Position columns to numeric {0,1}
            df[position_cols] = df[position_cols].apply(pd.to_numeric, errors="coerce").fillna(0).astype(int)

            # --- Ensure Total edited ---
            if "Total edited" not in df.columns:
                df["Total edited"] = df[position_cols].sum(axis=1).astype(int)
                st.info("`Total edited` column missing — calculated automatically as sum of 1s per row.")

            # --- User setting for Volume per "1" calculation ---
            st.markdown("#### ⚙️ Volume Calculation Settings")
            default_total_vol = st.number_input(
                "Desired total volume per sample (µL)",
                min_value=1.0, max_value=10000.0, value=64.0, step=1.0,
                help="Used to compute Volume per '1' as (Desired total volume / Total edited) when not provided."
            )

            vol_candidates = [c for c in df.columns if "volume per" in c.lower()]
            if not vol_candidates:
                df['Volume per "1"'] = default_total_vol / df["Total edited"].replace(0, np.nan)
                df['Volume per "1"'] = df['Volume per "1"'].fillna(0)  # rows with 0 edits → 0 µL
                st.info(f'`Volume per "1"` column missing — calculated automatically as {default_total_vol:.0f} µL / Total edited.')
                volume_col = 'Volume per "1"'
            else:
                volume_col = vol_candidates[0]

            # Safety: per-transfer must not exceed per-well cap
            if df[volume_col].max() > max_per_well_ul:
                st.error(
                    f"❌ At least one row has `Volume per \"1\"` greater than the per-well cap ({max_per_well_ul} µL). "
                    "Increase the cap or reduce per-transfer volume."
                )
                st.stop()

            # --- Compute total demand per input ---
            vol_per_one_series = pd.to_numeric(df[volume_col], errors="coerce").fillna(0.0)
            total_volume_per_input = [float(vol_per_one_series[df[pos] == 1].sum()) for pos in position_cols]
            wells_needed_per_input = [int(ceil(tv / max_per_well_ul)) if tv > 0 else 0 for tv in total_volume_per_input]
            num_inputs = len(position_cols)
            max_wells_per_source = max(wells_needed_per_input) if wells_needed_per_input else 0

            st.markdown("### 👀 Preview: Suggested Uniform Layout")
            if max_wells_per_source == 0:
                st.info("No edits detected — nothing to allocate.")
                st.stop()

            st.write(
                f"💡 Suggested layout: **{max_wells_per_source} consecutive wells per input** "
                f"(cap {max_per_well_ul:.0f} µL/well)."
            )

            # Total wells and plates needed
            total_wells_needed_uniform = num_inputs * max_wells_per_source
            plates_needed = int(ceil(total_wells_needed_uniform / 96)) or 1

            # ✅ Correct, robust well ordering for layout
            global_wells = sorted(
                build_global_wells_list(plates_needed),
                key=lambda x: (
                    x[0],  # plate
                    ROWS_96.index(parse_well_name(x[1])[0]),  # row index
                    parse_well_name(x[1])[1]  # column number
                )
            )
            global_wells = global_wells[:total_wells_needed_uniform]

            # Assign uniform blocks to each input
            assigned_wells_map, well_to_input, preview_rows = {}, {}, []
            for i in range(1, num_inputs + 1):
                start, end = (i - 1) * max_wells_per_source, i * max_wells_per_source
                block = global_wells[start:end]
                assigned_wells_map[i] = block
                for j, (p, w) in enumerate(block, start=1):
                    well_to_input[(p, w)] = (i, j)
                block_str = ", ".join([f"P{p}:{w}" for (p, w) in block])
                preview_rows.append({
                    "Input (Position #)": i,
                    "Total demand (µL)": round(total_volume_per_input[i-1], 2),
                    "Wells needed (actual)": wells_needed_per_input[i-1],
                    "Allocated (uniform)": max_wells_per_source,
                    "Assigned wells": block_str
                })

            preview_df = pd.DataFrame(preview_rows)
            st.dataframe(preview_df, use_container_width=True, height=300)

            st.markdown("#### Plate Map (hover cells for details)")
            plate_html = render_plate_map_html(plates_needed, well_to_input, max_wells_per_source, num_inputs)
            st.markdown(plate_html, unsafe_allow_html=True)

            # --- Generate Commands ---
            st.markdown("### ✅ Generate Pipetting Commands")
            if st.button("Generate using this layout"):
                # Track per-input per-well usage (µL)
                per_input_well_cum = {i: [0.0] * max_wells_per_source for i in range(1, num_inputs + 1)}
                commands, source_volume_totals = [], {}

                for _, row in df.iterrows():
                    sample_id = int(row["Sample"])
                    vol_per_one = float(row[volume_col])
                    if vol_per_one <= 0:
                        continue
                    dest_plate, dest_well = sample_index_to_plate_and_well(sample_id)
                    tool = pick_tool(vol_per_one)

                    for pos_idx, col in enumerate(position_cols, start=1):
                        if int(row[col]) != 1:
                            continue
                        wells_for_input = assigned_wells_map[pos_idx]
                        cum_list = per_input_well_cum[pos_idx]

                        chosen = None
                        for j, ((src_plate, src_well), current_vol) in enumerate(zip(wells_for_input, cum_list)):
                            if current_vol + vol_per_one <= max_per_well_ul:
                                chosen = (j, src_plate, src_well)
                                break

                        if chosen is None:
                            st.error(
                                f"Allocation exhausted for Input {pos_idx} while creating commands. "
                                "Increase the max volume per well or review per-transfer volume."
                            )
                            st.stop()

                        j, src_plate, src_well = chosen
                        cum_list[j] += vol_per_one
                        per_input_well_cum[pos_idx] = cum_list
                        source_volume_totals[(src_plate, src_well)] = source_volume_totals.get((src_plate, src_well), 0.0) + vol_per_one

                        commands.append({
                            "Input #": pos_idx,
                            "Source plate": src_plate,
                            "Source well": src_well,
                            "Destination plate": dest_plate,
                            "Destination well": dest_well,
                            "Volume": round(vol_per_one, 2),
                            "Tool": tool
                        })

                commands_df = pd.DataFrame(commands)

                # ✅ Add helper sort columns to ensure Source/Destination wells sort A1→A12, B1→B12, ...
                def row_idx_from_well(w): return ROWS_96.index(parse_well_name(w)[0])
                def col_num_from_well(w): return parse_well_name(w)[1]

                commands_df["Src_row_idx"] = commands_df["Source well"].apply(row_idx_from_well)
                commands_df["Src_col_num"] = commands_df["Source well"].apply(col_num_from_well)
                commands_df["Dst_row_idx"] = commands_df["Destination well"].apply(row_idx_from_well)
                commands_df["Dst_col_num"] = commands_df["Destination well"].apply(col_num_from_well)

                commands_df = commands_df.sort_values(
                    by=["Input #", "Source plate", "Src_row_idx", "Src_col_num",
                        "Destination plate", "Dst_row_idx", "Dst_col_num"],
                    kind="stable"
                )

                # Drop helper columns & order final columns
                commands_df = commands_df[[
                    "Input #", "Source plate", "Source well",
                    "Destination plate", "Destination well", "Volume", "Tool"
                ]]

                st.success(f"✅ Generated {len(commands_df)} commands across {num_inputs} inputs.")

                # ✅ Source summary numeric sort by plate → row → col
                summary_rows = []
                for i in range(1, num_inputs + 1):
                    for (p, w), used in zip(assigned_wells_map[i], per_input_well_cum[i]):
                        total = source_volume_totals.get((p, w), 0.0)
                        summary_rows.append({
                            "Source": i, "Source plate": p, "Source well": w,
                            "Total volume taken (µL)": round(total, 2),
                            "Allocated capacity (µL)": round(max_per_well_ul, 2)
                        })
                summary_df = pd.DataFrame(summary_rows)
                summary_df["Src_row_idx"] = summary_df["Source well"].apply(row_idx_from_well)
                summary_df["Src_col_num"] = summary_df["Source well"].apply(col_num_from_well)
                summary_df = summary_df.sort_values(
                    by=["Source", "Source plate", "Src_row_idx", "Src_col_num"],
                    kind="stable"
                )[
                    ["Source", "Source plate", "Source well", "Total volume taken (µL)", "Allocated capacity (µL)"]
                ]

                # Display results
                st.markdown("### 💧 Pipetting Commands")
                st.dataframe(commands_df, use_container_width=True, height=400)
                st.download_button("⬇️ Download Commands CSV", commands_df.to_csv(index=False), "pipetting_commands.csv", mime="text/csv")

                st.markdown("### 📊 Source Volume Summary")
                st.dataframe(summary_df, use_container_width=True, height=400)
                st.download_button("⬇️ Download Source Summary CSV", summary_df.to_csv(index=False), "source_volume_summary.csv", mime="text/csv")

        except Exception as e:
            st.error(f"❌ Error processing file: {e}")
    else:
        st.info("👆 Upload an Excel/CSV/TXT file to start.")