"""DataTools Outlier Detector — stub page."""

from __future__ import annotations

import sys
from pathlib import Path

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,
    require_feature_or_render_upgrade,
    require_normalization_gate,
)
from src.license import FeatureFlag

hide_streamlit_chrome()
require_feature_or_render_upgrade(FeatureFlag.OUTLIER_DETECTOR)
require_normalization_gate()

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

st.title("📊 Find Unusual Values")
st.caption("Detect and handle outliers in numeric columns.")

st.info("This tool is under development.")

# ---------------------------------------------------------------------------
# What this tool will do
# ---------------------------------------------------------------------------

st.markdown("""
**Features:**
- Z-score detection (configurable threshold)
- IQR (interquartile range) detection
- MAD (median absolute deviation) detection
- Domain-rule violations (e.g., age < 0, price > $1M)
- Visual outlier highlighting in data preview
- Handling: flag only, remove, cap/winsorize to bounds
""")

st.divider()

# ---------------------------------------------------------------------------
# File upload (functional)
# ---------------------------------------------------------------------------

uploaded = st.file_uploader(
    "Upload CSV or Excel file",
    type=["csv", "tsv", "xlsx", "xls"],
    help="Upload a file to preview. Processing is not yet available.",
    key="outlier_file_upload",
)

if uploaded is not None:
    import pandas as pd
    try:
        if uploaded.name.endswith((".xlsx", ".xls")):
            df = pd.read_excel(uploaded)
        else:
            df = pd.read_csv(uploaded)
        st.subheader(f"Preview: {uploaded.name}")
        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
        st.dataframe(df.head(10), use_container_width=True)
    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```"
        )

# ---------------------------------------------------------------------------
# Placeholder options
# ---------------------------------------------------------------------------

st.subheader("Detection Method")

st.selectbox("Method", ["Z-Score", "IQR (Interquartile Range)", "MAD (Median Absolute Deviation)"], disabled=True)
st.slider("Z-Score threshold", 1.0, 5.0, 3.0, 0.1, disabled=True)
st.slider("IQR multiplier", 1.0, 3.0, 1.5, 0.1, disabled=True)

st.subheader("Handling")

st.selectbox("Action", ["Flag only (add column)", "Remove outlier rows", "Cap / Winsorize to bounds"], disabled=True)

st.divider()
st.button("Detect Outliers", type="primary", use_container_width=True, disabled=True)

# ---------------------------------------------------------------------------
# Footer
# ---------------------------------------------------------------------------

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