from datetime import timedelta

import pandas as pd
import plotly.graph_objects as go
import requests
import streamlit as st
from plotly.subplots import make_subplots

from auth import require_auth
from data_processing import TARGET_HIGH, TARGET_LOW, classify_treatments
from nightscout_client import NightscoutConfigError
from reporting import BOLUS_CATEGORY_SMB, classify_bolus, daily_stats, effective_basal_series, target_glucose_series
from ui_common import (
    MMOL_HOVERTEMPLATE,
    add_basal_area_trace,
    add_bolus_marker_traces,
    add_carb_bars_trace,
    add_target_line_trace,
    add_threshold_lines,
    get_colors,
    load_day_data,
    load_profile,
    render_completeness_warning,
    select_period_days,
)

st.set_page_config(page_title="Glucose dashboard — Dagoverzicht", page_icon="📊", layout="wide")
require_auth()

st.title("Dagoverzicht")
st.caption(
    "Eén dag per keer: boven glucose met koolhydraten/bolus-overlay, onder basaal "
    "en SMB — apart gehouden omdat één gecombineerde grafiek te veel informatie bevatte."
)

# De periode-knop bepaalt alleen het navigeerbare venster (hoe ver terug je kunt
# bladeren), niet welke data wordt opgehaald — dat gebeurt hieronder voor precies
# de geselecteerde dag (load_day_data), zodat het wisselen tussen dagen snel blijft.
window_start, window_end, days = select_period_days(default_label="14 dagen")

state_key = "dagoverzicht_selected_day"
if state_key not in st.session_state:
    st.session_state[state_key] = window_end
if not (window_start <= st.session_state[state_key] <= window_end):
    st.session_state[state_key] = window_end

selected_day = st.session_state[state_key]

col_prev, col_date, col_next = st.columns([1, 3, 1])
with col_prev:
    if st.button("← Vorige dag", disabled=selected_day <= window_start, width="stretch"):
        selected_day = selected_day - timedelta(days=1)
        st.session_state[state_key] = selected_day
with col_next:
    if st.button("Volgende dag →", disabled=selected_day >= window_end, width="stretch"):
        selected_day = selected_day + timedelta(days=1)
        st.session_state[state_key] = selected_day
with col_date:
    date_options = [window_end - timedelta(days=i) for i in range(days)]
    chosen = st.selectbox(
        "Datum",
        options=date_options,
        index=date_options.index(selected_day),
        format_func=lambda d: d.strftime("%A %d-%m-%Y"),
        label_visibility="collapsed",
    )
    if chosen != selected_day:
        selected_day = chosen
        st.session_state[state_key] = selected_day

try:
    glucose, treatments = load_day_data(selected_day)
    profile = load_profile()
except NightscoutConfigError as exc:
    st.error(str(exc))
    st.stop()
except (requests.RequestException, ValueError) as exc:
    st.error(f"Kon geen data ophalen van Nightscout: {exc}")
    st.stop()

if glucose.empty:
    st.info("Geen glucosemetingen gevonden op deze dag.")
    st.stop()

render_completeness_warning(glucose, selected_day, selected_day)

colors = get_colors()
day_start = pd.Timestamp(selected_day)
day_end = day_start + pd.Timedelta(1, unit="D")

# treatments bevat een lookback-marge vóór middernacht (zie
# nightscout_client.fetch_treatments_for_day) zodat een temp basal die al
# vóór deze dag begon toch meetelt — effective_basal_series() clipt dat
# zelf aan [day_start, day_end). Voor de koolhydraten/bolus-overlays willen
# we die marge juist NIET, anders duiken gisteravond se stippen op vandaag op.
day_treatments = treatments[(treatments["timestamp"] >= day_start) & (treatments["timestamp"] < day_end)]

carbs_day = classify_treatments(day_treatments)["carbs"]
bolus_day = classify_bolus(day_treatments)
basal_day = effective_basal_series(profile, treatments, day_start, day_end)
target_day = target_glucose_series(profile, day_start, day_end)


def _close_step_to_day_end(df: pd.DataFrame, value_col: str) -> pd.DataFrame:
    """Voeg een laatste punt op day_end toe (zelfde waarde als het laatste
    segment) zodat een stapvlak/-lijn de volle dag doortekent i.p.v. te
    stoppen bij het laatste breekpunt."""
    if df.empty:
        return df
    last_value = df.iloc[-1][value_col]
    closing_row = pd.DataFrame([{"timestamp": day_end, value_col: last_value}])
    return pd.concat([df, closing_row], ignore_index=True)


basal_day = _close_step_to_day_end(basal_day, "rate")
target_day = _close_step_to_day_end(target_day, "target")


def build_day_figure(glucose_day, carbs_day, bolus_day, basal_day, target_day, colors) -> go.Figure:
    fig = make_subplots(
        rows=2,
        cols=1,
        shared_xaxes=True,
        row_heights=[0.68, 0.32],
        vertical_spacing=0.08,
        specs=[[{"secondary_y": True}], [{"secondary_y": False}]],
    )

    # Rij 1: glucose + streefwaarde + koolhydraten + voeding-/correctiebolus.
    fig.add_hrect(
        y0=TARGET_LOW, y1=TARGET_HIGH, fillcolor=colors["band"], line_width=0, layer="below", row=1, col=1
    )
    add_target_line_trace(fig, target_day, colors, row=1)
    add_carb_bars_trace(fig, carbs_day, colors, row=1, secondary_y=True)

    top_bolus = bolus_day[bolus_day["category"] != BOLUS_CATEGORY_SMB] if not bolus_day.empty else bolus_day
    lane_y = glucose_day["mmol"].min() - 1.0 if not glucose_day.empty else TARGET_LOW - 1.0
    add_bolus_marker_traces(fig, top_bolus, colors, lane_y, row=1)

    fig.add_trace(
        go.Scatter(
            x=glucose_day["timestamp"],
            y=glucose_day["mmol"],
            mode="lines",
            line={"color": colors["glucose"], "width": 2},
            name="Glucose",
            hovertemplate=MMOL_HOVERTEMPLATE,
        ),
        row=1,
        col=1,
    )
    add_threshold_lines(fig, colors, row=1)
    fig.update_yaxes(title_text="mmol/L", gridcolor=colors["grid"], row=1, col=1, secondary_y=False)
    fig.update_yaxes(title_text="Koolhydraten (g)", showgrid=False, row=1, col=1, secondary_y=True)

    # Rij 2: basaal + SMB — apart van rij 1 zodat de glucosegrafiek niet
    # overladen raakt met insulinedetails.
    add_basal_area_trace(fig, basal_day, colors, row=2)
    smb_bolus = bolus_day[bolus_day["category"] == BOLUS_CATEGORY_SMB] if not bolus_day.empty else bolus_day
    smb_lane_y = (basal_day["rate"].max() * 1.15) if not basal_day.empty else 1.0
    add_bolus_marker_traces(fig, smb_bolus, colors, smb_lane_y, row=2)
    fig.update_yaxes(
        title_text="Basaal (E/uur)", gridcolor=colors["grid"], rangemode="tozero", row=2, col=1
    )

    fig.update_xaxes(range=[day_start, day_end], dtick=4 * 3600 * 1000, gridcolor=colors["grid"])
    fig.update_layout(
        paper_bgcolor="rgba(0,0,0,0)",
        plot_bgcolor="rgba(0,0,0,0)",
        font_color=colors["text"],
        height=560,
        showlegend=True,
        legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "left", "x": 0},
        margin={"l": 10, "r": 10, "t": 40, "b": 10},
    )
    return fig


fig = build_day_figure(glucose, carbs_day, bolus_day, basal_day, target_day, colors)
st.plotly_chart(fig, width="stretch")

stats = daily_stats(glucose, day_treatments, profile, day_start, day_end)
col1, col2, col3, col4 = st.columns(4)
col1.metric(
    "Glucose (gem./min/max)",
    f"{stats['mean_mmol']:.1f} mmol/L",
    delta=f"{stats['min_mmol']:.1f} - {stats['max_mmol']:.1f}",
    delta_color="off",
)
col2.metric("Totale insuline", f"{stats['total_insulin']:.1f} E")
col3.metric("Basaal / bolus / SMB", f"{stats['basal_insulin']:.1f} / {stats['bolus_insulin']:.1f} / {stats['smb_insulin']:.1f} E")
col4.metric(
    "Hypo's / hyper's",
    f"{stats['hypo_count']} / {stats['hyper_count']}",
    delta=f"{stats['hypo_duration_minutes']:.0f} / {stats['hyper_duration_minutes']:.0f} min",
    delta_color="off",
)

st.caption(
    "Boven: stippen = bolusinsuline (voeding/correctie, grootte naar dosis) · staafjes = koolhydraten · "
    "streepstippellijn = streefwaarde uit pompprofiel. Onder: vlak = basaal · stippen = SMB."
)