"""Interactive Meta Optimizer dashboard — Streamlit."""
import streamlit as st
from datetime import date, timedelta
import pandas as pd
import sys
import os

sys.path.insert(0, os.path.dirname(__file__))

from meta_ads_client import MetaAdsClient
from baserow_client import BaserowClient
import config


def _extract_vertical(name: str) -> str:
    """VIVIFUL_13_telefonia_leadads → telefonia"""
    prefix = config.META_CAMPAIGN_PREFIX
    rest   = name[len(prefix):].lstrip("_")
    parts  = rest.split("_")
    start  = 1 if parts and parts[0].isdigit() else 0
    return parts[start].lower() if start < len(parts) else "otros"

st.set_page_config(
    page_title=f"Meta Optimizer — {config.META_CAMPAIGN_PREFIX}",
    layout="wide",
    initial_sidebar_state="expanded",
)

_STRATEGY_LABELS = {
    "LOWEST_COST_WITHOUT_CAP": "Menor coste",
    "LOWEST_COST_WITH_BID_CAP": "Cap. puja",
    "COST_CAP":                 "Cap. coste",
    "MINIMUM_ROAS":             "ROAS mín.",
}


def _eur(val: float) -> str:
    return f"{val:.2f}€"


def _margin(val: float) -> str:
    return f"+{val:.0f}€" if val >= 0 else f"{val:.0f}€"


def _status(leads: int, spend: float) -> str:
    if leads > 0:
        return "✅"
    if spend > 0:
        return "❌"
    return "—"


@st.cache_data(ttl=300, show_spinner="Cargando datos de Meta API...")
def _load_data(date_from: str, date_to: str):
    meta    = MetaAdsClient()
    baserow = BaserowClient()
    vertical_cpls: dict = {}
    try:
        for v in baserow.get_all_verticals():
            name = (v.get("Nombre") or "").strip().lower()
            cpl  = float(v.get("target_cpl") or 0)
            if name and cpl:
                vertical_cpls[name] = cpl
    except Exception:
        pass
    daily_rows       = meta.get_daily_campaign_rows(date_from, date_to)
    campaign_metrics = meta.get_campaign_metrics(date_from, date_to)
    return daily_rows, campaign_metrics, vertical_cpls


@st.cache_data(ttl=300, show_spinner="Cargando detalle de campaña...")
def _load_detail(campaign_id: str, date_from: str, date_to: str):
    meta   = MetaAdsClient()
    adsets = meta.get_adset_metrics(campaign_id, date_from, date_to)
    ads    = meta.get_ad_metrics(campaign_id, date_from, date_to)
    bid    = meta.get_campaign_bid_config(campaign_id)
    bids   = meta.get_adset_bid_configs(campaign_id)
    for adset in adsets:
        b = bids.get(adset["id"], {})
        adset["cost_cap_eur"] = b.get("cost_cap_eur")
        adset["bid_strategy"] = b.get("bid_strategy", "")
    return adsets, ads, bid


# ── Sidebar ───────────────────────────────────────────────────────────────────

st.sidebar.title("Filtros")
today          = date.today()
yesterday      = today - timedelta(days=1)
default_from   = yesterday - timedelta(days=6)  # últimos 7 días por defecto
first_of_month = today.replace(day=1)

c1, c2 = st.sidebar.columns(2)
date_from = c1.date_input("Desde", value=default_from, max_value=yesterday)
date_to   = c2.date_input("Hasta", value=yesterday, min_value=date_from, max_value=yesterday)

if st.sidebar.button("🔄 Actualizar", use_container_width=True):
    st.cache_data.clear()

date_from_str = date_from.strftime("%Y-%m-%d")
date_to_str   = date_to.strftime("%Y-%m-%d")

if date_from > date_to:
    st.error("La fecha inicio debe ser anterior a la fecha fin.")
    st.stop()

# ── Data ──────────────────────────────────────────────────────────────────────

try:
    daily_rows, campaign_metrics, vertical_cpls = _load_data(date_from_str, date_to_str)
except Exception as _e:
    st.error(f"Error cargando datos de Meta API: {_e}")
    st.stop()

# Aggregate daily totals with per-vertical margins
_daily: dict = {}
for row in daily_rows:
    v      = _extract_vertical(row["campaign_name"])
    target = vertical_cpls.get(v, config.META_TARGET_CPL)
    margin = round((target - row["spend"] / row["leads"]) * row["leads"], 2) if row["leads"] > 0 else round(-row["spend"], 2)
    d = _daily.setdefault(row["date"], {"spend": 0.0, "leads": 0, "margin": 0.0})
    d["spend"]  += row["spend"]
    d["leads"]  += row["leads"]
    d["margin"] += margin

daily_totals = [
    {
        "date":   dt,
        "spend":  round(d["spend"], 2),
        "leads":  int(d["leads"]),
        "cpl":    round(d["spend"] / d["leads"], 2) if d["leads"] > 0 else 0.0,
        "margin": round(d["margin"], 2),
    }
    for dt, d in sorted(_daily.items())
]

# Aggregate verticals
verticals: dict = {}
for cid, m in campaign_metrics.items():
    v      = _extract_vertical(m["name"])
    target = vertical_cpls.get(v, config.META_TARGET_CPL)
    margin = round((target - m["cpl"]) * m["leads"], 2) if m["leads"] > 0 else round(-m["spend"], 2)
    if v not in verticals:
        verticals[v] = {"spend": 0.0, "leads": 0, "margin": 0.0, "target_cpl": target}
    verticals[v]["spend"]  += m["spend"]
    verticals[v]["leads"]  += m["leads"]
    verticals[v]["margin"] += margin

# Vertical filter (populated after load)
v_options         = ["Todos"] + sorted(verticals.keys())
selected_vertical = st.sidebar.selectbox("Vertical", v_options)
if selected_vertical != "Todos":
    campaign_metrics = {
        cid: m for cid, m in campaign_metrics.items()
        if _extract_vertical(m["name"]) == selected_vertical
    }

# ── Header ────────────────────────────────────────────────────────────────────

st.title(f"Meta Optimizer — {config.META_CAMPAIGN_PREFIX}")
st.caption(f"Período: **{date_from.strftime('%d/%m/%Y')}** → **{date_to.strftime('%d/%m/%Y')}**")

total_spend  = sum(d["spend"]  for d in daily_totals)
total_leads  = sum(d["leads"]  for d in daily_totals)
total_cpl    = round(total_spend / total_leads, 2) if total_leads > 0 else 0.0
total_margin = sum(d["margin"] for d in daily_totals)

k1, k2, k3, k4 = st.columns(4)
k1.metric("Gasto total",    _eur(total_spend))
k2.metric("Leads totales",  f"{total_leads:,}")
k3.metric("CPL medio",      _eur(total_cpl))
k4.metric("Margen total",   _margin(total_margin))

st.divider()

# ── Tabs ──────────────────────────────────────────────────────────────────────

tab1, tab2, tab3, tab4, tab5 = st.tabs(["📅 Por día", "📊 Campañas", "🏷️ Verticales", "🗂️ Histórico", "🎨 Creatividades"])


# ── Tab 1: Por día ────────────────────────────────────────────────────────────
with tab1:
    if not daily_totals:
        st.info("Sin datos para el período seleccionado.")
    else:
        df_daily = pd.DataFrame([
            {
                "Día":    d["date"][8:10] + "/" + d["date"][5:7],
                "Gasto":  _eur(d["spend"]),
                "Leads":  d["leads"],
                "CPL":    _eur(d["cpl"]),
                "Margen": _margin(d["margin"]),
                "Est":    _status(d["leads"], d["spend"]),
            }
            for d in daily_totals
        ])
        st.dataframe(df_daily, use_container_width=True, hide_index=True)

        st.subheader("Desglose por campaña")
        day_opts     = [d["date"] for d in reversed(daily_totals)]
        selected_day = st.selectbox(
            "Selecciona un día",
            day_opts,
            format_func=lambda s: s[8:10] + "/" + s[5:7] + "/" + s[:4],
        )
        if selected_day:
            day_camp: dict = {}
            for row in daily_rows:
                if row["date"] != selected_day:
                    continue
                key = row["campaign_name"]
                if key not in day_camp:
                    v      = _extract_vertical(key)
                    target = vertical_cpls.get(v, config.META_TARGET_CPL)
                    day_camp[key] = {
                        "name": key, "vertical": v,
                        "spend": 0.0, "leads": 0, "target_cpl": target,
                    }
                day_camp[key]["spend"] += row["spend"]
                day_camp[key]["leads"] += row["leads"]

            rows = []
            for c in sorted(day_camp.values(), key=lambda x: -x["spend"]):
                cpl    = round(c["spend"] / c["leads"], 2) if c["leads"] > 0 else 0.0
                margin = round((c["target_cpl"] - cpl) * c["leads"], 2) if c["leads"] > 0 else round(-c["spend"], 2)
                rows.append({
                    "Campaña":  c["name"],
                    "Vertical": c["vertical"],
                    "Gasto":    _eur(c["spend"]),
                    "Leads":    c["leads"],
                    "CPL":      _eur(cpl) if c["leads"] > 0 else "—",
                    "Obj":      _eur(c["target_cpl"]),
                    "Margen":   _margin(margin),
                })
            if rows:
                st.dataframe(pd.DataFrame(rows), use_container_width=True, hide_index=True)
            else:
                st.info("Sin campañas activas ese día.")


# ── Tab 2: Campañas ───────────────────────────────────────────────────────────
with tab2:
    if not campaign_metrics:
        st.info("Sin campañas para el período seleccionado.")
    else:
        camp_rows = []
        for cid, m in sorted(campaign_metrics.items(), key=lambda x: -x[1]["spend"]):
            v      = _extract_vertical(m["name"])
            target = vertical_cpls.get(v, config.META_TARGET_CPL)
            margin = round((target - m["cpl"]) * m["leads"], 2) if m["leads"] > 0 else round(-m["spend"], 2)
            camp_rows.append({
                "Campaña":  m["name"],
                "Vertical": v,
                "Gasto":    _eur(m["spend"]),
                "Leads":    m["leads"],
                "CPL":      _eur(m["cpl"]) if m["leads"] > 0 else "—",
                "Obj":      _eur(target),
                "Margen":   _margin(margin),
                "CTR":      f"{m['ctr']:.1f}%",
                "_cid":     cid,
            })

        df_camps = pd.DataFrame([{k: v for k, v in r.items() if k != "_cid"} for r in camp_rows])
        st.dataframe(df_camps, use_container_width=True, hide_index=True)

        st.subheader("Detalle de campaña")
        camp_id_map   = {r["Campaña"]: r["_cid"] for r in camp_rows}
        selected_camp = st.selectbox("Selecciona una campaña", list(camp_id_map.keys()))

        if selected_camp:
            selected_cid        = camp_id_map[selected_camp]
            adsets, ads, bid_cfg = _load_detail(selected_cid, date_from_str, date_to_str)

            strategy    = bid_cfg.get("bid_strategy", "")
            strat_label = _STRATEGY_LABELS.get(strategy, strategy or "—")
            budget      = bid_cfg.get("daily_budget_eur")
            budget_str  = f"{budget:.0f}€/día" if budget else "—"
            st.caption(f"Estrategia: **{strat_label}** | Presupuesto: **{budget_str}**")

            if adsets:
                st.markdown("**Conjuntos de anuncios**")
                df_adsets = pd.DataFrame([
                    {
                        "Nombre": a["name"],
                        "Gasto":  _eur(a["spend"]),
                        "Leads":  a["leads"],
                        "CPL":    _eur(a["cpl"]) if a["leads"] > 0 else "—",
                        "CTR":    f"{a['ctr']:.1f}%",
                        "Cap":    _eur(a["cost_cap_eur"]) if a.get("cost_cap_eur") else "Auto",
                    }
                    for a in adsets
                ])
                st.dataframe(df_adsets, use_container_width=True, hide_index=True)
            else:
                st.info("Sin conjuntos de anuncios con gasto en este período.")

            if ads:
                st.markdown("**Anuncios**")
                df_ads = pd.DataFrame([
                    {
                        "Nombre": a["name"],
                        "Gasto":  _eur(a["spend"]),
                        "Leads":  a["leads"],
                        "CPL":    _eur(a["cpl"]) if a["leads"] > 0 else "—",
                        "CTR":    f"{a['ctr']:.1f}%",
                        "CPM":    _eur(a["cpm"]),
                    }
                    for a in ads
                ])
                st.dataframe(df_ads, use_container_width=True, hide_index=True)
            else:
                st.info("Sin anuncios con gasto en este período.")


# ── Tab 3: Verticales ─────────────────────────────────────────────────────────
with tab3:
    if not verticals:
        st.info("Sin datos de verticales.")
    else:
        vert_rows = []
        for v, data in sorted(verticals.items(), key=lambda x: -x[1]["margin"]):
            v_leads = data["leads"]
            v_spend = data["spend"]
            v_cpl   = round(v_spend / v_leads, 2) if v_leads > 0 else 0.0
            vert_rows.append({
                "Vertical": v,
                "Gasto":    _eur(v_spend),
                "Leads":    v_leads,
                "CPL":      _eur(v_cpl),
                "Obj":      _eur(data["target_cpl"]) if data.get("target_cpl") else "—",
                "Margen":   _margin(data["margin"]),
            })
        st.dataframe(pd.DataFrame(vert_rows), use_container_width=True, hide_index=True)


# ── Tab 4: Histórico ──────────────────────────────────────────────────────────

_ACTION_COLORS = {
    "INCREASE_BUDGET":  "🟢",
    "REDUCE_BUDGET":    "🟠",
    "PAUSE":            "🔴",
    "REVIEW_CREATIVES": "🟣",
    "MAINTAIN":         "⚪",
}


@st.cache_data(ttl=120, show_spinner="Cargando fechas disponibles...")
def _load_snapshot_dates():
    return BaserowClient().get_snapshot_dates()


@st.cache_data(ttl=120, show_spinner="Cargando análisis del día...")
def _load_snapshots(run_date: str):
    import json
    rows = BaserowClient().get_snapshots_for_date(run_date)
    result = []
    for r in rows:
        try:
            adsets = json.loads(r.get("adsets_json") or "[]")
        except Exception:
            adsets = []
        try:
            ads = json.loads(r.get("ads_json") or "[]")
        except Exception:
            ads = []
        result.append({
            "campaign_name": r.get("campaign_name", ""),
            "vertical":      r.get("vertical", ""),
            "spend":         float(r.get("spend") or 0),
            "leads":         int(r.get("leads") or 0),
            "cpl":           float(r.get("cpl") or 0),
            "margin":        float(r.get("margin") or 0),
            "action_type":   r.get("action_type", "MAINTAIN"),
            "justification": r.get("justification", ""),
            "adsets":        adsets,
            "ads":           ads,
        })
    return sorted(result, key=lambda x: -x["spend"])


with tab4:
    dates = _load_snapshot_dates()

    if not dates:
        st.info("Sin análisis guardados aún. Los snapshots se generan al ejecutar run.py.")
    else:
        fmt_date = lambda s: s[8:10] + "/" + s[5:7] + "/" + s[:4]
        selected_date = st.selectbox(
            "Fecha del análisis",
            dates,
            format_func=fmt_date,
        )
        if st.button("🔄 Recargar", key="reload_hist"):
            st.cache_data.clear()

        snapshots = _load_snapshots(selected_date)
        if not snapshots:
            st.info("Sin datos para esa fecha.")
        else:
            # ── Resumen del día ───────────────────────────────────────────────
            d_spend  = sum(s["spend"]  for s in snapshots)
            d_leads  = sum(s["leads"]  for s in snapshots)
            d_cpl    = round(d_spend / d_leads, 2) if d_leads > 0 else 0.0
            d_margin = sum(s["margin"] for s in snapshots)
            h1, h2, h3, h4 = st.columns(4)
            h1.metric("Gasto",  _eur(d_spend))
            h2.metric("Leads",  f"{d_leads:,}")
            h3.metric("CPL",    _eur(d_cpl))
            h4.metric("Margen", _margin(d_margin))
            st.divider()

            # ── Tabla de campañas clicable ────────────────────────────────────
            df_snap = pd.DataFrame([
                {
                    "Acción":   _ACTION_COLORS.get(s["action_type"], "⚪") + " " + s["action_type"],
                    "Campaña":  s["campaign_name"],
                    "Vertical": s["vertical"],
                    "Gasto":    _eur(s["spend"]),
                    "Leads":    s["leads"],
                    "CPL":      _eur(s["cpl"]) if s["leads"] > 0 else "—",
                    "Margen":   _margin(s["margin"]),
                }
                for s in snapshots
            ])
            event = st.dataframe(
                df_snap,
                use_container_width=True,
                hide_index=True,
                on_select="rerun",
                selection_mode="single-row",
            )

            sel_rows = event.selection.rows
            if sel_rows:
                snap = snapshots[sel_rows[0]]
                st.subheader(snap["campaign_name"])
                st.caption(
                    f"Vertical: **{snap['vertical']}** | "
                    f"Decisión: **{snap['action_type']}** | "
                    f"Margen: **{_margin(snap['margin'])}**"
                )
                if snap["justification"]:
                    st.info(snap["justification"])

                # ── Adsets — expanders con evaluación visible ─────────────────
                adsets = snap["adsets"]
                if adsets:
                    st.markdown("**Conjuntos de anuncios**")
                    for a in adsets:
                        label = (
                            f"{a['name']}  —  "
                            f"{_eur(a['spend'])} · {a['leads']} leads · "
                            f"CPL {_eur(a['cpl']) if a['leads'] > 0 else '—'} · "
                            f"CTR {a.get('ctr', 0):.1f}%"
                        )
                        with st.expander(label):
                            if a.get("cost_cap_eur"):
                                st.caption(f"Cap: {_eur(a['cost_cap_eur'])}")
                            if a.get("evaluacion"):
                                st.write(f"_{a['evaluacion']}_")
                            if a.get("recomendacion"):
                                st.write(f"→ {a['recomendacion']}")

                # ── Anuncios — expanders con evaluación visible ───────────────
                ads = snap["ads"]
                if ads:
                    st.markdown("**Anuncios**")
                    for a in ads:
                        label = (
                            f"{a['name']}  —  "
                            f"{_eur(a['spend'])} · {a['leads']} leads · "
                            f"CPL {_eur(a['cpl']) if a['leads'] > 0 else '—'} · "
                            f"CTR {a.get('ctr', 0):.1f}% · "
                            f"CPM {_eur(a.get('cpm', 0))}"
                        )
                        with st.expander(label):
                            if a.get("evaluacion"):
                                st.write(f"_{a['evaluacion']}_")
                            if a.get("recomendacion"):
                                st.write(f"→ {a['recomendacion']}")


# ── Tab 5: Creatividades ──────────────────────────────────────────────────────
with tab5:
    @st.cache_data(ttl=300, show_spinner="Cargando análisis de creatividades...")
    def _load_creatives():
        return BaserowClient().get_all_creative_analyses()

    creatives_raw = _load_creatives()

    if not creatives_raw:
        st.info("No hay análisis de creatividades. Ejecuta `python analyze_creatives.py` para generar datos.")
        st.stop()

    # Build dataframe
    df_all = pd.DataFrame(creatives_raw)
    df_all["score"]      = pd.to_numeric(df_all.get("score", 0), errors="coerce").fillna(0)
    df_all["created_at"] = df_all.get("created_at", pd.Series(dtype=str))

    # ── Filters ───────────────────────────────────────────────────────────────
    f1, f2, f3 = st.columns([2, 2, 2])

    dates_available = sorted(df_all["created_at"].dropna().unique(), reverse=True)
    sel_date = f1.selectbox("Fecha análisis", dates_available)

    camp_ids = sorted(df_all["campaign_id"].dropna().unique().tolist()) if "campaign_id" in df_all.columns else []
    sel_camp = f2.selectbox("Campaña", ["Todas"] + camp_ids)

    score_min = f3.slider("Score mínimo", 0.0, 10.0, 0.0, step=0.5)

    # Apply filters
    df = df_all.copy()
    if sel_date:
        df = df[df["created_at"] == sel_date]
    if sel_camp != "Todas" and "campaign_id" in df.columns:
        df = df[df["campaign_id"] == sel_camp]
    if score_min > 0:
        df = df[df["score"] >= score_min]

    # ── KPIs ──────────────────────────────────────────────────────────────────
    scored_df = df[df["score"] > 0]
    avg_sc    = round(scored_df["score"].mean(), 1) if not scored_df.empty else 0.0
    fatigue_n = int(df["analysis"].str.contains("FATIGA", na=False).sum()) if "analysis" in df.columns else 0
    last_run  = df_all["created_at"].max() if not df_all.empty else "—"

    k1, k2, k3, k4 = st.columns(4)
    k1.metric("Anuncios", len(df))
    k2.metric("Score medio", f"{avg_sc}/10")
    k3.metric("Con fatiga", fatigue_n)
    k4.metric("Última ejecución", last_run)

    # ── Fatigue alerts ────────────────────────────────────────────────────────
    if fatigue_n:
        fatigued = df[df["analysis"].str.contains("FATIGA", na=False)]
        with st.expander(f"⚠️ {fatigue_n} anuncios con fatiga creativa", expanded=True):
            for _, row in fatigued.iterrows():
                st.warning(f"**{row.get('ad_name', '—')}** — Score {row.get('score', 0):.1f}/10")

    st.divider()

    # ── Table + Detail panel ──────────────────────────────────────────────────
    rename_map = {
        "campaign_id":     "Campaña ID",
        "ad_name":         "Anuncio",
        "score":           "Score",
        "created_at":      "Fecha",
        "analysis":        "Análisis",
        "recommendations": "Recomendaciones",
    }
    display_cols = [c for c in rename_map if c in df.columns]
    df_display   = df[display_cols].rename(columns=rename_map).reset_index(drop=True)

    col_table, col_detail = st.columns([3, 2])

    with col_table:
        st.caption("Haz clic en una fila para ver el detalle →")
        event = st.dataframe(
            df_display,
            use_container_width=True,
            selection_mode="single-row",
            on_select="rerun",
            column_config={
                "Score": st.column_config.ProgressColumn(
                    "Score", min_value=0, max_value=10, format="%.1f"
                ),
            },
            hide_index=True,
        )
        selected_rows = event.selection.rows if hasattr(event, "selection") else []

    with col_detail:
        if selected_rows:
            row = df.iloc[selected_rows[0]]
            score = float(row.get("score", 0))
            sc_emoji = "🟢" if score >= 8 else "🟡" if score >= 6 else "🟠" if score >= 4 else "🔴"

            st.markdown(f"### {row.get('ad_name', '—')}")
            st.markdown(f"{sc_emoji} **Score: {score:.1f} / 10**")

            img_url = str(row.get("image_url", ""))
            if img_url.startswith("http"):
                try:
                    st.image(img_url, use_container_width=True)
                except Exception:
                    st.caption("_Imagen no disponible_")

            analysis = str(row.get("analysis", ""))
            if analysis:
                st.markdown("**Análisis**")
                st.write(analysis)

            rec = str(row.get("recommendations", ""))
            if rec:
                st.markdown("**Recomendaciones**")
                st.info(rec)

            # Score evolution across runs
            ad_id = str(row.get("ad_id", ""))
            if ad_id:
                history = BaserowClient().get_creative_history_by_ad(ad_id)
                if len(history) >= 2:
                    st.markdown("**Evolución del score**")
                    hist_df = pd.DataFrame(history)[["created_at", "score"]].dropna()
                    hist_df["score"] = pd.to_numeric(hist_df["score"], errors="coerce")
                    hist_df = hist_df[hist_df["score"] > 0].set_index("created_at")
                    st.line_chart(hist_df)
        else:
            st.info("← Selecciona un anuncio en la tabla para ver el detalle.")