"""Cached data loaders for the RHTP dashboard.

Every loader reads from /data/<topic>/<file>. Replace the file with a
real-data file of the same schema to switch from synthetic to live data.
"""
from __future__ import annotations

from pathlib import Path
from typing import Optional

import pandas as pd
import streamlit as st

# /app at runtime in Docker, project root locally
ROOT_CANDIDATES = [Path("/app"), Path(__file__).resolve().parents[2]]
for _root in ROOT_CANDIDATES:
    if (_root / "data").exists():
        ROOT = _root
        break
else:  # pragma: no cover
    ROOT = ROOT_CANDIDATES[-1]
DATA = ROOT / "data"


def _read(rel: str) -> pd.DataFrame:
    """Read a CSV (or its parquet sibling if present and newer).

    `fips` and `county_fips` are kept as zero-padded strings so they merge
    cleanly across files. Use object dtype rather than pandas string[python]
    -- the latter trips patsy when used as a categorical fixed effect.
    """
    csv = DATA / rel
    parquet = csv.with_suffix(".parquet")
    if parquet.exists() and parquet.stat().st_mtime >= csv.stat().st_mtime:
        df = pd.read_parquet(parquet)
    else:
        df = pd.read_csv(csv, dtype={"fips": str, "county_fips": str})
    for c in ("fips", "county_fips"):
        if c in df.columns:
            df[c] = df[c].astype(str).str.zfill(5)
    return df


# --------------------------------------------------------------------------
# Geography
# --------------------------------------------------------------------------
@st.cache_data(show_spinner=False)
def counties() -> pd.DataFrame:
    return _read("geography/montana_counties.csv")


@st.cache_data(show_spinner=False)
def population_panel() -> pd.DataFrame:
    return _read("geography/county_population_panel.csv")


@st.cache_data(show_spinner=False)
def hospitals() -> pd.DataFrame:
    return _read("geography/montana_hospitals.csv")


# --------------------------------------------------------------------------
# Facilities
# --------------------------------------------------------------------------
@st.cache_data(show_spinner=False)
def facility_quarter_ops() -> pd.DataFrame:
    df = _read("facilities/facility_quarter_ops.csv")
    df["period"] = df["year"].astype(str) + "Q" + df["quarter"].astype(str)
    df["period_index"] = df["year"] * 4 + (df["quarter"] - 1)
    return df


@st.cache_data(show_spinner=False)
def facility_quarter_treatment() -> pd.DataFrame:
    df = _read("facilities/facility_quarter_treatment_status.csv")
    df["period"] = df["year"].astype(str) + "Q" + df["quarter"].astype(str)
    df["period_index"] = df["year"] * 4 + (df["quarter"] - 1)
    return df


@st.cache_data(show_spinner=False)
def coe_implementation() -> pd.DataFrame:
    return _read("facilities/coe_implementation.csv")


@st.cache_data(show_spinner=False)
def telehealth_activation() -> pd.DataFrame:
    return _read("facilities/telehealth_activation.csv")


@st.cache_data(show_spinner=False)
def shared_services() -> pd.DataFrame:
    return _read("facilities/shared_services.csv")


# --------------------------------------------------------------------------
# Workforce
# --------------------------------------------------------------------------
@st.cache_data(show_spinner=False)
def workforce_panel() -> pd.DataFrame:
    return _read("workforce/workforce_county_year.csv")


@st.cache_data(show_spinner=False)
def service_commitment_awards() -> pd.DataFrame:
    return _read("workforce/service_commitment_awards.csv")


@st.cache_data(show_spinner=False)
def residency_slots() -> pd.DataFrame:
    return _read("workforce/residency_slots.csv")


@st.cache_data(show_spinner=False)
def training_participants() -> pd.DataFrame:
    return _read("workforce/training_participants.csv")


@st.cache_data(show_spinner=False)
def workforce_supports() -> pd.DataFrame:
    return _read("workforce/workforce_supports.csv")


# --------------------------------------------------------------------------
# Claims
# --------------------------------------------------------------------------
@st.cache_data(show_spinner=False)
def member_month_pmpm() -> pd.DataFrame:
    return _read("claims/member_month_pmpm.csv")


@st.cache_data(show_spinner=False)
def county_quarter_treatment_status() -> pd.DataFrame:
    return _read("claims/county_quarter_treatment_status.csv")


@st.cache_data(show_spinner=False)
def ed_high_utilizers() -> pd.DataFrame:
    return _read("claims/ed_high_utilizers.csv")


@st.cache_data(show_spinner=False)
def tnt_cpt_usage() -> pd.DataFrame:
    return _read("claims/tnt_cpt_usage.csv")


@st.cache_data(show_spinner=False)
def pharmacy_prescribing() -> pd.DataFrame:
    return _read("claims/pharmacy_prescribing.csv")


@st.cache_data(show_spinner=False)
def outpatient_share() -> pd.DataFrame:
    return _read("claims/outpatient_share.csv")


@st.cache_data(show_spinner=False)
def ems_modernization() -> pd.DataFrame:
    return _read("claims/ems_modernization.csv")


@st.cache_data(show_spinner=False)
def vbc_attribution() -> pd.DataFrame:
    return _read("claims/vbc_attribution.csv")


# --------------------------------------------------------------------------
# Prevention
# --------------------------------------------------------------------------
@st.cache_data(show_spinner=False)
def prevention_panel() -> pd.DataFrame:
    return _read("prevention/prevention_county_year.csv")


@st.cache_data(show_spinner=False)
def school_sites() -> pd.DataFrame:
    return _read("prevention/school_sites.csv")


@st.cache_data(show_spinner=False)
def mobile_care_runs() -> pd.DataFrame:
    return _read("prevention/mobile_care_runs.csv")


@st.cache_data(show_spinner=False)
def chap_rollout() -> pd.DataFrame:
    return _read("prevention/chap_rollout.csv")


@st.cache_data(show_spinner=False)
def crisis_infrastructure() -> pd.DataFrame:
    return _read("prevention/crisis_infrastructure.csv")


# --------------------------------------------------------------------------
# Technology
# --------------------------------------------------------------------------
@st.cache_data(show_spinner=False)
def hie_participation() -> pd.DataFrame:
    return _read("technology/hie_participation.csv")


@st.cache_data(show_spinner=False)
def ehr_modernization() -> pd.DataFrame:
    return _read("technology/ehr_modernization.csv")


@st.cache_data(show_spinner=False)
def bed_registry() -> pd.DataFrame:
    return _read("technology/bed_registry.csv")


@st.cache_data(show_spinner=False)
def bh_bed_wait_time() -> pd.DataFrame:
    return _read("technology/bh_bed_wait_time.csv")


@st.cache_data(show_spinner=False)
def dashboard_usage() -> pd.DataFrame:
    return _read("technology/dashboard_usage.csv")