datatools-dev/src/gui/pages/2_Text_Cleaner.py

"""DataTools Text Cleaner — Streamlit page."""

from __future__ import annotations

import io
import json
import sys
from pathlib import Path

import pandas as pd
import streamlit as st

_project_root = Path(__file__).resolve().parent.parent.parent.parent
if str(_project_root) not in sys.path:
    sys.path.insert(0, str(_project_root))

from src.gui.components import (
    hide_streamlit_chrome,
    pickup_or_upload,
    render_hidden_aware_preview,
    require_normalization_gate,
)
from src.core.text_clean import (
    PRESETS,
    CleanOptions,
    clean_dataframe,
    hidden_char_css,
    visualize_hidden_html,
)

hide_streamlit_chrome()
require_normalization_gate()


# ---------------------------------------------------------------------------
# Header
# ---------------------------------------------------------------------------

st.title("✂️ Text Cleaner")
st.caption(
    "Trim whitespace, fold smart quotes, strip invisible characters, and "
    "normalize line endings. Runs locally — your data never leaves this computer."
)

# ---------------------------------------------------------------------------
# File upload
# ---------------------------------------------------------------------------

uploaded = pickup_or_upload(
    label="Upload CSV or Excel file",
    key="textclean_file_upload",
    types=["csv", "tsv", "xlsx", "xls"],
)

if uploaded is None:
    st.info("Upload a CSV, TSV, or Excel file to begin.")
    st.stop()


@st.cache_data(show_spinner=False)
def _read_uploaded(name: str, data: bytes) -> pd.DataFrame:
    """Read the uploaded bytes into a DataFrame, treating all cells as strings."""
    suffix = Path(name).suffix.lower()
    bio = io.BytesIO(data)
    if suffix in (".xlsx", ".xls"):
        return pd.read_excel(bio, dtype=str, keep_default_na=False)
    # CSV / TSV — try utf-8 then utf-8-sig then latin-1 as a fallback
    for enc in ("utf-8", "utf-8-sig", "latin-1"):
        try:
            bio.seek(0)
            sep = "\t" if suffix == ".tsv" else ","
            return pd.read_csv(
                bio, dtype=str, keep_default_na=False,
                encoding=enc, sep=sep, on_bad_lines="warn",
            )
        except UnicodeDecodeError:
            continue
    bio.seek(0)
    return pd.read_csv(bio, dtype=str, keep_default_na=False, encoding="latin-1")


try:
    df = _read_uploaded(uploaded.name, uploaded.getvalue())
except UnicodeDecodeError as e:
    st.error(
        f"**Could not decode `{uploaded.name}`**\n\n"
        f"The file isn't UTF-8, UTF-8-with-BOM, or Latin-1.\n\n"
        f"_Underlying error: {e}_\n\n"
        f"Try re-saving the file as UTF-8 from the source application, "
        f"or convert it with `iconv -f <source-encoding> -t utf-8`."
    )
    st.stop()
except Exception as e:
    from src.core.errors import format_for_user
    st.error(
        f"**Could not read `{uploaded.name}`**\n\n"
        f"```\n{format_for_user(e)}\n```"
    )
    st.stop()

st.subheader(f"Preview: {uploaded.name}")
st.caption(f"{len(df)} rows, {len(df.columns)} columns")
preview_show_hidden = st.toggle(
    "Show hidden characters in preview",
    value=True,
    help="Highlights NBSP, zero-width chars, smart quotes, and leading/trailing whitespace.",
    key="textclean_preview_show_hidden",
)
if preview_show_hidden:
    render_hidden_aware_preview(df, n_rows=10)
else:
    st.dataframe(df.head(10), use_container_width=True)

st.divider()

# ---------------------------------------------------------------------------
# Options
# ---------------------------------------------------------------------------

st.subheader("Options")

preset_label = st.radio(
    "Preset",
    ["excel-hygiene (recommended)", "minimal", "paranoid"],
    index=0,
    horizontal=True,
    help=(
        "excel-hygiene: trim, collapse whitespace, fold smart quotes, strip "
        "invisible chars, normalize line endings, NFC. "
        "minimal: only trim and collapse. "
        "paranoid: everything including NFKC compat fold (lossy)."
    ),
)
preset_key = preset_label.split(" ", 1)[0]
options = CleanOptions.from_preset(preset_key)

with st.expander("Advanced options"):
    col_a, col_b = st.columns(2)
    with col_a:
        options.trim = st.checkbox("Trim leading/trailing whitespace", value=options.trim)
        options.collapse_whitespace = st.checkbox(
            "Collapse internal whitespace", value=options.collapse_whitespace,
        )
        options.normalize_line_endings = st.checkbox(
            "Normalize line endings (\\r\\n → \\n)", value=options.normalize_line_endings,
        )
        options.strip_control = st.checkbox(
            "Strip control characters", value=options.strip_control,
        )
        options.strip_bom = st.checkbox("Strip BOM", value=options.strip_bom)
    with col_b:
        options.fold_smart_chars = st.checkbox(
            "Fold smart characters (curly quotes, em-dash, NBSP)",
            value=options.fold_smart_chars,
        )
        options.strip_zero_width = st.checkbox(
            "Strip zero-width / invisible characters", value=options.strip_zero_width,
        )
        options.nfc = st.checkbox("Unicode NFC normalization", value=options.nfc)
        options.nfkc = st.checkbox(
            "Unicode NFKC compat fold (lossy: ① → 1, fi → fi)",
            value=options.nfkc,
        )

    st.markdown("**Scope**")
    string_cols = [
        c for c in df.columns
        if pd.api.types.is_object_dtype(df[c]) or pd.api.types.is_string_dtype(df[c])
    ]
    selected_cols = st.multiselect(
        "Columns to clean (default: all string columns)",
        options=list(df.columns),
        default=string_cols,
    )
    skip_cols = st.multiselect(
        "Columns to skip even if they look like text",
        options=list(df.columns),
        default=[],
    )
    options.columns = selected_cols if selected_cols else None
    options.skip_columns = list(skip_cols)

    st.markdown("**Case conversion**")
    case_global = st.selectbox(
        "Apply case conversion to selected columns",
        ["None", "UPPER", "lower", "Title", "Sentence"],
        index=0,
    )
    case_map = {
        "UPPER": "upper", "lower": "lower",
        "Title": "title", "Sentence": "sentence",
    }
    if case_global != "None":
        options.case = case_map[case_global]  # type: ignore[assignment]

# ---------------------------------------------------------------------------
# Run
# ---------------------------------------------------------------------------

st.divider()

if st.button("Clean Text", type="primary", use_container_width=True):
    with st.spinner("Cleaning..."):
        try:
            result = clean_dataframe(df, options)
        except ValueError as e:
            st.error(str(e))
            st.stop()
    st.session_state["textclean_result"] = result
    st.session_state["textclean_input_name"] = uploaded.name

result = st.session_state.get("textclean_result")
if result is None:
    st.stop()

# ---------------------------------------------------------------------------
# Results
# ---------------------------------------------------------------------------

st.subheader("Results")

pct = (result.cells_changed / result.cells_total * 100.0) if result.cells_total else 0.0
m1, m2, m3, m4 = st.columns(4)
m1.metric("Cells scanned", result.cells_total)
m2.metric("Cells changed", result.cells_changed)
m3.metric("% changed", f"{pct:.1f}%")
m4.metric("Columns processed", len(result.columns_processed))

if result.cells_changed:
    counts = result.changes["column"].value_counts()
    st.markdown("**Changes by column**")
    st.dataframe(
        counts.rename("cells_changed").to_frame(),
        use_container_width=True,
    )

    st.markdown("**Examples (first 25 changes)**")
    show_hidden = st.toggle(
        "Show hidden characters (NBSP, ZWSP, smart quotes, control chars…)",
        value=True,
        help=(
            "Highlights characters the cleaner is removing or replacing. "
            "Hover any badge to see the codepoint and label."
        ),
        key="textclean_show_hidden",
    )
    examples = result.changes.head(25).copy()
    examples["row"] = examples["row"] + 1
    if show_hidden:
        # Inject the badge CSS once, then render an HTML table so the
        # invisibles in old/new are actually visible to the user.
        st.markdown(hidden_char_css(), unsafe_allow_html=True)
        rows_html = []
        for _, row in examples.iterrows():
            rows_html.append(
                "<tr>"
                f"<td>{row['row']}</td>"
                f"<td><code>{visualize_hidden_html(str(row['column']))}</code></td>"
                f"<td>{visualize_hidden_html(str(row['old']))}</td>"
                f"<td>{visualize_hidden_html(str(row['new']))}</td>"
                f"<td><code>{row['ops_applied']}</code></td>"
                "</tr>"
            )
        st.markdown(
            "<table class='hidden-char-table'>"
            "<thead><tr>"
            "<th style='text-align:left'>Row</th>"
            "<th style='text-align:left'>Column</th>"
            "<th style='text-align:left'>Before</th>"
            "<th style='text-align:left'>After</th>"
            "<th style='text-align:left'>Ops applied</th>"
            "</tr></thead>"
            f"<tbody>{''.join(rows_html)}</tbody>"
            "</table>"
            "<style>"
            ".hidden-char-table { width: 100%; border-collapse: collapse; }"
            ".hidden-char-table th, .hidden-char-table td { "
            "  padding: 4px 8px; border-bottom: 1px solid #eee; "
            "  vertical-align: top; }"
            ".hidden-char-table tbody tr:hover { background: #fafafa; }"
            "</style>",
            unsafe_allow_html=True,
        )
    else:
        st.dataframe(examples, use_container_width=True, hide_index=True)

st.markdown("**Cleaned preview (first 10 rows)**")
# Reuse the same toggle the Examples table uses so the user controls both
# the changes audit and the cleaned preview with one switch.
if show_hidden:
    render_hidden_aware_preview(result.cleaned_df, n_rows=10)
else:
    st.dataframe(result.cleaned_df.head(10), use_container_width=True)

# ---------------------------------------------------------------------------
# Downloads
# ---------------------------------------------------------------------------

st.divider()
stem = Path(st.session_state.get("textclean_input_name", "input")).stem

dl_a, dl_b, dl_c = st.columns(3)
with dl_a:
    cleaned_bytes = result.cleaned_df.to_csv(index=False).encode("utf-8-sig")
    st.download_button(
        "Download cleaned CSV",
        data=cleaned_bytes,
        file_name=f"{stem}_cleaned.csv",
        mime="text/csv",
    )
with dl_b:
    if not result.changes.empty:
        changes_bytes = result.changes.to_csv(index=False).encode("utf-8-sig")
        st.download_button(
            "Download changes audit",
            data=changes_bytes,
            file_name=f"{stem}_changes.csv",
            mime="text/csv",
        )
with dl_c:
    config_bytes = json.dumps(options.to_dict(), indent=2).encode("utf-8")
    st.download_button(
        "Download config JSON",
        data=config_bytes,
        file_name="text_clean_config.json",
        mime="application/json",
    )

st.divider()
st.caption("Runs locally. Your data never leaves this computer. | DataTools v3.0")