"""DataTools Clean Text — 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 (
    back_to_home_link,
    hide_streamlit_chrome,
    pickup_or_upload,
    render_hidden_aware_preview,
    require_feature_or_render_upgrade,
)
from src.license import FeatureFlag
from src.core.text_clean import (
    PRESETS,
    CleanOptions,
    clean_dataframe,
    hidden_char_css,
    visualize_hidden_html,
)

hide_streamlit_chrome()
back_to_home_link()
require_feature_or_render_upgrade(FeatureFlag.TEXT_CLEANER)


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

st.title("✂️ Clean Text")
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()

# Collapse the input preview once the user has clicked Clean Text so
# the Results section below is the primary visual focus. The user can
# re-expand the expander to re-inspect the source rows.
_has_result = st.session_state.get("textclean_result") is not None
with st.expander(f"Preview: {uploaded.name}", expanded=not _has_result):
    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
# ---------------------------------------------------------------------------
#
# Wrapped in an outer expander whose default state mirrors the preview
# expander above: open before a result exists, folded once the user has
# clicked Clean Text. Together they push the Results section to the top
# of the visible area after a run.

with st.expander("Options", expanded=not _has_result):
    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
    # One-shot flag picked up on the next pass to scroll the parent
    # document to the Results anchor (see scroll snippet below).
    st.session_state["_textclean_scroll_to_results"] = True
    # Force a second rerun so the preview and options expanders see
    # the new result on the NEXT script pass and collapse themselves.
    # Without this they stay expanded until the user touches any
    # other widget.
    st.rerun()

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

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

# Anchor target for the auto-scroll snippet at the end of this block.
# A bare ``<div id="...">`` survives Streamlit's HTML sanitizer (only
# ``<script>`` is stripped), and a 1px-tall div doesn't visually shift
# anything. Placed before the subheader so the scrolled-to viewport
# starts a few pixels above the section heading rather than below it.
st.markdown(
    '<div id="textclean-results-anchor" style="height:1px"></div>',
    unsafe_allow_html=True,
)

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.
        # ``mark_outer_whitespace=True`` matches the input preview's
        # rendering so leading/trailing spaces show up as badges in the
        # Before/After columns — without it, the examples table missed
        # exactly the whitespace the cleaner is removing.
        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']), mark_outer_whitespace=True)}</code></td>"
                f"<td>{visualize_hidden_html(str(row['old']), mark_outer_whitespace=True)}</td>"
                f"<td>{visualize_hidden_html(str(row['new']), mark_outer_whitespace=True)}</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
# ---------------------------------------------------------------------------
#
# All three byte buffers are prepared up front (outside the columns) so
# each ``st.download_button`` sees stable ``data`` across reruns and an
# explicit ``key`` — without those, Streamlit auto-derived widget IDs
# can collide for multiple download_buttons in adjacent columns and
# only the first one actually fires on click. The empty-changes case
# now renders a disabled button (rather than vanishing) so the layout
# stays steady and the user understands why nothing's available.

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

cleaned_bytes = result.cleaned_df.to_csv(index=False).encode("utf-8-sig")
changes_bytes = (
    result.changes.to_csv(index=False).encode("utf-8-sig")
    if not result.changes.empty
    else b""
)
config_bytes = json.dumps(options.to_dict(), indent=2).encode("utf-8")

dl_a, dl_b, dl_c = st.columns(3)
with dl_a:
    st.download_button(
        "Download cleaned CSV",
        data=cleaned_bytes,
        file_name=f"{stem}_cleaned.csv",
        mime="text/csv",
        key="textclean_dl_cleaned",
        use_container_width=True,
    )
with dl_b:
    st.download_button(
        "Download changes audit",
        data=changes_bytes,
        file_name=f"{stem}_changes.csv",
        mime="text/csv",
        key="textclean_dl_changes",
        disabled=result.changes.empty,
        help="No changes to audit." if result.changes.empty else None,
        use_container_width=True,
    )
with dl_c:
    st.download_button(
        "Download config JSON",
        data=config_bytes,
        file_name="text_clean_config.json",
        mime="application/json",
        key="textclean_dl_config",
        use_container_width=True,
    )

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

# ---------------------------------------------------------------------------
# Post-run auto-scroll
# ---------------------------------------------------------------------------
#
# When the user clicks Clean Text, the preview + options collapse but
# Streamlit by itself doesn't scroll — the Results section is at the
# bottom of a tall script so the user has to find it. Inject a tiny
# component-html iframe that calls ``scrollIntoView`` on the parent's
# Results anchor. Streamlit's main page is same-origin with component
# iframes so ``window.parent.document`` access is allowed.
#
# The flag is one-shot (``pop`` removes it) so re-renders triggered by
# unrelated widgets in the Results section (e.g., the Show-hidden
# toggle) don't yank the viewport back to the top of Results.
if st.session_state.pop("_textclean_scroll_to_results", False):
    from streamlit.components.v1 import html as _components_html
    _components_html(
        """
        <script>
          const doc = window.parent.document;
          const target = doc.getElementById('textclean-results-anchor');
          if (target) target.scrollIntoView({behavior: 'smooth', block: 'start'});
        </script>
        """,
        height=0,
    )