src/streamlit_app.py

import io
import os
from datetime import datetime, date
from typing import Dict, List, Optional, Tuple
import smtplib
import ssl
from email.message import EmailMessage

import pandas as pd
import plotly.express as px
import streamlit as st

# -----------------------------
# App Configuration
# -----------------------------
st.set_page_config(
    page_title="Tableau de bord des inscriptions",
    page_icon="🧭",
    layout="wide",
    initial_sidebar_state="expanded",
)

import os

# Création du répertoire uploads s'il n'existe pas
os.makedirs("uploads", exist_ok=True)

# Configuration pour le cache Streamlit
if not os.path.exists(".streamlit"):
    os.makedirs(".streamlit", exist_ok=True)

# -----------------------------
# CSS intégré directement
# -----------------------------
def inject_base_css():
    css = """
    .card {
        background-color: var(--card);
        border-radius: 0.5rem;
        padding: 1rem;
        margin-bottom: 1rem;
        box-shadow: 0 1px 3px rgba(0,0,0,0.12), 0 1px 2px rgba(0,0,0,0.24);
    }
    .card-title {
        font-weight: bold;
        font-size: 1.2rem;
        margin-bottom: 0.5rem;
        color: var(--primary);
    }
    .kpi {
        text-align: center;
        padding: 1rem;
    }
    .card-label {
        font-size: 1rem;
        color: var(--muted);
    }
    .card-value {
        font-size: 2rem;
        font-weight: bold;
        color: var(--primary);
    }
    """
    st.markdown(f"<style>{css}</style>", unsafe_allow_html=True)


# -----------------------------
# Utilities
# -----------------------------
def try_parse_datetime(series: pd.Series) -> pd.Series:
    """Attempt to parse a pandas Series as datetimes, returning original on failure."""
    if pd.api.types.is_datetime64_any_dtype(series):
        return series
    try:
        parsed = pd.to_datetime(series, errors="coerce")
        if parsed.notna().sum() >= max(3, int(0.2 * len(parsed))):
            return parsed
    except Exception:
        pass
    return series


def make_unique_columns(columns: List[str]) -> List[str]:
    """Ensure column names are unique by appending suffixes (2), (3), ..."""
    seen: Dict[str, int] = {}
    unique_cols: List[str] = []
    for name in columns:
        base = str(name)
        if base not in seen:
            seen[base] = 1
            unique_cols.append(base)
        else:
            seen[base] += 1
            unique_cols.append(f"{base} ({seen[base]})")
    return unique_cols


def normalize_label(text: str) -> str:
    t = str(text).lower().strip()
    t = t.replace("\u00a0", " ").replace("&nbsp;", " ")
    t = " ".join(t.split())
    return t


def find_column(df: pd.DataFrame, candidates: List[str]) -> Optional[str]:
    """Return the first matching column by normalized name from candidates."""
    norm_to_col = {normalize_label(c): c for c in df.columns}
    for cand in candidates:
        n = normalize_label(cand)
        if n in norm_to_col:
            return norm_to_col[n]
    return None


def infer_pandas_types(df: pd.DataFrame) -> Dict[str, str]:
    """Return a mapping of column -> inferred logical type: 'categorical' | 'numeric' | 'date' | 'text'."""
    type_map: Dict[str, str] = {}
    for col in df.columns:
        s = df[col]
        if pd.api.types.is_datetime64_any_dtype(s):
            type_map[col] = "date"
        elif pd.api.types.is_bool_dtype(s):
            type_map[col] = "categorical"
        elif pd.api.types.is_numeric_dtype(s):
            type_map[col] = "numeric"
        else:
            # try parse datetime heuristic
            parsed = try_parse_datetime(s)
            if pd.api.types.is_datetime64_any_dtype(parsed):
                type_map[col] = "date"
            else:
                # if low cardinality, treat as categorical
                nunique = s.astype(str).nunique(dropna=True)
                type_map[col] = "categorical" if nunique <= max(50, len(s) * 0.05) else "text"
    return type_map


def dynamic_filters(df: pd.DataFrame, type_map: Dict[str, str]) -> pd.DataFrame:
    """Render dynamic filters for all columns and return the filtered DataFrame."""
    filtered = df.copy()
    st.sidebar.markdown("### 🔎 Filtres dynamiques")
    for col in filtered.columns:
        logical = type_map.get(col, "text")
        if logical == "numeric" and pd.api.types.is_numeric_dtype(filtered[col]):
            series_num = pd.to_numeric(filtered[col], errors="coerce")
            valid = series_num.dropna()
            if valid.empty:
                st.sidebar.caption(f"{col}: aucune valeur numérique exploitable")
                continue
            min_v = float(valid.min())
            max_v = float(valid.max())
            if min_v == max_v:
                st.sidebar.caption(f"{col}: valeur unique {min_v}")
                # Filtrage inutile car une seule valeur
                continue
            vmin, vmax = st.sidebar.slider(f"{col} (min-max)", min_value=min_v, max_value=max_v, value=(min_v, max_v))
            filtered = filtered[(series_num >= vmin) & (series_num <= vmax)]
        elif logical == "date":
            parsed = try_parse_datetime(filtered[col])
            if pd.api.types.is_datetime64_any_dtype(parsed):
                dmin = parsed.min()
                dmax = parsed.max()
                start_end = st.sidebar.date_input(f"{col} (période)", value=(dmin.date() if pd.notna(dmin) else date.today(), dmax.date() if pd.notna(dmax) else date.today()))
                if isinstance(start_end, tuple) and len(start_end) == 2:
                    start, end = start_end
                    mask = (parsed.dt.date >= start) & (parsed.dt.date <= end)
                    filtered = filtered[mask]
        else:
            # categorical or text -> multiselect of unique values (with limit)
            uniques = filtered[col].dropna().astype(str).unique().tolist()
            uniques = sorted(uniques)[:200]
            selected = st.sidebar.multiselect(f"{col}", options=uniques, default=[])
            if selected:
                filtered = filtered[filtered[col].astype(str).isin(selected)]
    return filtered


def apply_search(df: pd.DataFrame, query: str) -> pd.DataFrame:
    if not query:
        return df
    q = query.strip().lower()
    mask = pd.Series(False, index=df.index)
    for col in df.columns:
        col_values = df[col].astype(str).str.lower()
        mask = mask | col_values.str.contains(q, na=False)
    return df[mask]


def to_excel_bytes(df: pd.DataFrame) -> bytes:
    buffer = io.BytesIO()
    with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
        df.to_excel(writer, index=False, sheet_name="inscriptions")
    return buffer.getvalue()


def kpi_card(label: str, value: str):
    st.markdown(
        f"""
        <div class="card kpi">
            <div class="card-label">{label}</div>
            <div class="card-value">{value}</div>
        </div>
        """,
        unsafe_allow_html=True,
    )


def chart_card(title: str, fig):
    st.markdown(f"<div class=\"card\"><div class=\"card-title\">{title}</div>", unsafe_allow_html=True)
    st.plotly_chart(fig, use_container_width=True, theme=None)
    st.markdown("</div>", unsafe_allow_html=True)


def safe_format_template(template: str, row: Dict[str, object]) -> str:
    class SafeDict(dict):
        def __missing__(self, key):
            return ""

    flat = {str(k): ("" if v is None else str(v)) for k, v in row.items()}
    try:
        return template.format_map(SafeDict(flat))
    except Exception:
        return template


def send_email_smtp(
    smtp_host: str,
    smtp_port: int,
    sender_email: str,
    sender_password: str,
    use_tls: bool,
    to_email: str,
    subject: str,
    body_text: str,
    reply_to: Optional[str] = None,
) -> None:
    message = EmailMessage()
    message["From"] = sender_email
    message["To"] = to_email
    message["Subject"] = subject
    if reply_to:
        message["Reply-To"] = reply_to
    message.set_content(body_text)

    if use_tls:
        context = ssl.create_default_context()
        with smtplib.SMTP(smtp_host, smtp_port) as server:
            server.starttls(context=context)
            if sender_password:
                server.login(sender_email, sender_password)
            server.send_message(message)
    else:
        with smtplib.SMTP_SSL(smtp_host, smtp_port) as server:
            if sender_password:
                server.login(sender_email, sender_password)
            server.send_message(message)


def set_theme_variables(mode: str):
    # Adjust CSS variables for light/dark for cards and text; Plotly handled via template
    palette = {
        "light": {
            "--bg": "#f7f9fc",
            "--card": "#ffffff",
            "--text": "#0f172a",
            "--muted": "#64748b",
            "--primary": "#0ea5e9",
            "--accent": "#10b981",
            "--border": "#e5e7eb",
        },
        "dark": {
            "--bg": "#0b1220",
            "--card": "#111827",
            "--text": "#e5e7eb",
            "--muted": "#94a3b8",
            "--primary": "#38bdf8",
            "--accent": "#34d399",
            "--border": "#1f2937",
        },
    }
    colors = palette.get(mode, palette["light"])
    styles = ":root{" + ";".join([f"{k}:{v}" for k, v in colors.items()]) + "}"
    st.markdown(f"<style>{styles}</style>", unsafe_allow_html=True)


def get_plotly_template(mode: str) -> str:
    return "plotly_dark" if mode == "dark" else "plotly_white"


# -----------------------------
# Sidebar: Logo, Upload, Theme, Column mapping
# -----------------------------
def sidebar_controls() -> Tuple[Optional[pd.DataFrame], Dict[str, str], str, Dict[str, str], List[str]]:
    st.sidebar.markdown("## ⚙️ Contrôles")

    # Theme
    mode = st.sidebar.radio("Thème", options=["clair", "sombre"], horizontal=True, index=0)
    theme_mode = "dark" if mode == "sombre" else "light"
    set_theme_variables(theme_mode)

    # Gestion du logo (simple base64 si nécessaire)
    logo_base64 = None  # Remplacer par un string base64 si vous avez un logo
    if logo_base64:
        st.sidebar.markdown(f'<img src="data:image/png;base64,{logo_base64}" width="100%"/>', unsafe_allow_html=True)

    uploaded = st.sidebar.file_uploader("Importer un fichier Excel (.xlsx)", type=["xlsx"])

    df: Optional[pd.DataFrame] = None
    if uploaded is not None:
        try:
            # Read first sheet by default
            df = pd.read_excel(uploaded, sheet_name=0)
            # Strip column names
            df.columns = [str(c).strip() for c in df.columns]
            # Ensure unique column names
            if pd.Index(df.columns).has_duplicates:
                df.columns = make_unique_columns(list(df.columns))
            
            # Stocker dans session state pour les autres onglets
            st.session_state['df'] = df
            st.session_state['filtered_df'] = df.copy()
        except Exception as e:
            st.sidebar.error(f"Erreur de lecture du fichier: {e}")
    else:
        # Récupérer les données du session state si disponible
        if 'df' in st.session_state:
            df = st.session_state['df']

    logical_types: Dict[str, str] = {}
    coercions: Dict[str, str] = {}
    unique_keys: List[str] = []
    if df is not None and not df.empty:
        st.sidebar.markdown("---")
        st.sidebar.markdown("### 🧹 Nettoyage & types")
        # Global cleaning options
        trim_spaces = st.sidebar.checkbox("Supprimer les espaces autour du texte", value=True)
        lower_case = st.sidebar.checkbox("Mettre le texte en minuscules", value=False)
        drop_dupes = st.sidebar.checkbox("Supprimer les doublons", value=False)
        dedup_subset_cols: List[str] = []
        dedup_keep_choice = "first"
        if drop_dupes:
            dedup_subset_cols = st.sidebar.multiselect(
                "Colonnes à considérer (vide = toutes)", options=list(df.columns), help="Sélectionnez les colonnes sur lesquelles détecter les doublons."
            )
            dedup_keep_choice = st.sidebar.selectbox(
                "Conserver",
                options=["first", "last", "none"],
                index=0,
                help="Quelle occurrence conserver pour chaque doublon détecté",
            )
        fillna_blank = st.sidebar.checkbox("Remplacer NaN texte par vide", value=True)

        # Remove selected columns
        drop_columns = st.sidebar.multiselect(
            "Enlever des colonnes",
            options=list(df.columns),
            default=[],
            help="Supprimer des champs du jeu de données avant l'analyse",
            key="clean_drop_cols",
        )
        if drop_columns:
            df.drop(columns=drop_columns, inplace=True, errors="ignore")

        # Infer and allow override per column
        inferred = infer_pandas_types(df)
        for col in df.columns:
            logical_types[col] = st.sidebar.selectbox(
                f"Type pour {col}", options=["categorical", "numeric", "date", "text"], index=["categorical", "numeric", "date", "text"].index(inferred.get(col, "text"))
            )
            # Optional coercion
            if logical_types[col] in ("numeric", "date"):
                coercions[col] = logical_types[col]

        # Apply cleaning
        for col in df.columns:
            if df[col].dtype == object:
                if trim_spaces:
                    df[col] = df[col].astype(str).str.strip()
                if lower_case:
                    df[col] = df[col].astype(str).str.lower()
                if fillna_blank:
                    df[col] = df[col].replace({pd.NA: "", None: ""})
            # Coerce types
            if coercions.get(col) == "numeric":
                df[col] = pd.to_numeric(df[col], errors="coerce")
            elif coercions.get(col) == "date":
                df[col] = try_parse_datetime(df[col])

        if drop_dupes:
            keep_arg = None if dedup_keep_choice == "none" else dedup_keep_choice
            df.drop_duplicates(subset=(dedup_subset_cols if dedup_subset_cols else None), keep=keep_arg, inplace=True)

        # Unique person keys
        st.sidebar.markdown("---")
        st.sidebar.markdown("### 👤 Personne unique")
        # Heuristic suggestions
        hints = ["email", "e-mail", "mail", "id", "identifiant", "cin", "passport", "matricule", "phone", "téléphone", "telephone", "tel"]
        suggested = [c for c in df.columns if any(h in c.lower() for h in hints)]
        unique_keys = st.sidebar.multiselect(
            "Champs d'unicité (sélection multiple)", options=list(df.columns), default=suggested, help="Sélectionnez les champs qui identifient de façon unique une personne."
        )
        
        # Stocker les types et clés dans session state
        st.session_state['logical_types'] = logical_types
        st.session_state['unique_keys'] = unique_keys
        st.session_state['filtered_df'] = df.copy()

    return df, logical_types, theme_mode, coercions, unique_keys


# -----------------------------
# Page: Tableau de bord
# -----------------------------
def page_tableau_de_bord():
    st.markdown("<h2>📊 Tableau de bord</h2>", unsafe_allow_html=True)
    
    if 'df' not in st.session_state or st.session_state['df'] is None:
        st.markdown(
            """
            <div class="card">
                <div class="card-title">Bienvenue 👋</div>
                <p>Importez un fichier <b>.xlsx</b> contenant vos inscriptions pour commencer l'analyse.</p>
                <ul>
                    <li>Assurez-vous que les colonnes principales (pays, formation, statut, date) sont présentes.</li>
                    <li>Vous pourrez mapper les colonnes dans la barre latérale.</li>
                </ul>
            </div>
            """,
            unsafe_allow_html=True,
        )
        return

    df = st.session_state['df']
    type_map = st.session_state.get('logical_types', {})
    unique_keys = st.session_state.get('unique_keys', [])
    theme_mode = "dark" if st.session_state.get('theme_mode') == "dark" else "light"
    plotly_template = get_plotly_template(theme_mode)
    
    # Filters (dynamic for all columns)
    st.sidebar.markdown("---")
    filtered_df = dynamic_filters(df, type_map)

    # Optional unique-person filtering using selected keys
    st.sidebar.markdown("### 👤 Filtrer par personne unique")
    if unique_keys:
        person_filter = st.sidebar.checkbox("Activer le filtre d'unicité (drop_duplicates)", value=False, key="unique_filter_toggle")
        keep_strategy = st.sidebar.selectbox("Conserver", options=["first", "last"], index=0, key="unique_filter_keep")
        if person_filter:
            try:
                filtered_df = filtered_df.drop_duplicates(subset=unique_keys, keep=keep_strategy)
            except Exception:
                st.sidebar.warning("Impossible d'appliquer le filtre d'unicité. Vérifiez les champs choisis.")

    # Mettre à jour le dataframe filtré dans session state
    st.session_state['filtered_df'] = filtered_df

    # KPIs
    total_count = len(filtered_df)
    total_columns = filtered_df.shape[1]
    total_missing = int(filtered_df.isna().sum().sum())
    approx_dupes = int(filtered_df.duplicated().sum())

    c1, c2, c3, c4 = st.columns(4)
    with c1:
        kpi_card("Lignes", f"{total_count:,}")
    with c2:
        kpi_card("Colonnes", f"{total_columns:,}")
    with c3:
        kpi_card("Valeurs manquantes", f"{total_missing:,}")
    with c4:
        kpi_card("Doublons (approx)", f"{approx_dupes:,}")

    # Unique persons KPI (based on selected keys)
    if unique_keys:
        try:
            uniq = (
                filtered_df.dropna(subset=unique_keys)[unique_keys]
                .astype(str)
                .drop_duplicates()
                .shape[0]
            )
        except Exception:
            uniq = 0
        c5, _ = st.columns([1, 3])
        with c5:
            kpi_card("Personnes uniques", f"{uniq:,}")

    # Charts row 1: Program distribution, Country distribution
    st.markdown("<div class=\"card\"><div class=\"card-title\">Répartitions clés</div>", unsafe_allow_html=True)
    ctrl1, ctrl2, ctrl3 = st.columns([1,1,2])
    with ctrl1:
        topn = st.slider("Top N", min_value=3, max_value=50, value=10, step=1)
    with ctrl2:
        sort_dir = st.selectbox("Tri", options=["desc", "asc"], index=0)
    with ctrl3:
        st.caption("Appliqué aux graphiques de répartition ci-dessous")
    charts_row_1 = st.columns(2)
    # Choose any categorical column for distribution 1
    cat_cols_all = [c for c in filtered_df.columns if type_map.get(c) in ("categorical", "text")]
    if cat_cols_all and not filtered_df.empty:
        dim1 = st.selectbox("Dimension 1 (répartition)", options=cat_cols_all, key="rep_dim1")
        program_counts = (
            filtered_df.groupby(dim1).size().reset_index(name="count").sort_values("count", ascending=(sort_dir=="asc"))
            .head(topn)
        )
        fig_prog = px.bar(
            program_counts,
            x=dim1,
            y="count",
            template=plotly_template,
            color_continuous_scale="Blues",
        )
        fig_prog.update_layout(margin=dict(l=10, r=10, t=10, b=10))
        with charts_row_1[0]:
            chart_card("Répartition (dimension 1)", fig_prog)

    if cat_cols_all and not filtered_df.empty:
        dim2 = st.selectbox("Dimension 2 (répartition)", options=[c for c in cat_cols_all], index=min(1, len(cat_cols_all)-1), key="rep_dim2")
        country_counts = (
            filtered_df.groupby(dim2).size().reset_index(name="count").sort_values("count", ascending=(sort_dir=="asc"))
            .head(topn)
        )
        fig_country = px.pie(
            country_counts,
            names=dim2,
            values="count",
            template=plotly_template,
            hole=0.35,
        )
        fig_country.update_layout(margin=dict(l=10, r=10, t=10, b=10))
        with charts_row_1[1]:
            chart_card("Répartition (dimension 2)", fig_country)
    st.markdown("</div>", unsafe_allow_html=True)

    # Charts row 2: Status distribution
    charts_row_2 = st.columns(2)
    if cat_cols_all and not filtered_df.empty:
        dim3 = st.selectbox("Dimension 3", options=cat_cols_all, key="rep_dim3")
        status_counts = (
            filtered_df.groupby(dim3).size().reset_index(name="count").sort_values("count", ascending=False)
        )
        fig_status = px.bar(
            status_counts,
            x=dim3,
            y="count",
            template=plotly_template,
            color=dim3,
        )
        fig_status.update_layout(showlegend=False, margin=dict(l=10, r=10, t=10, b=10))
        with charts_row_2[0]:
            chart_card("Répartition (dimension 3)", fig_status)

    # Affichage des données
    search_query = st.text_input("Recherche globale", key="search_dashboard")
    df_searched = apply_search(filtered_df, search_query)
    st.dataframe(df_searched, use_container_width=True, hide_index=True)

    # Downloads
    csv_bytes = df_searched.to_csv(index=False).encode("utf-8-sig")
    xlsx_bytes = to_excel_bytes(df_searched)
    dc1, dc2 = st.columns(2)
    with dc1:
        st.download_button(
            "Télécharger CSV",
            data=csv_bytes,
            file_name="inscriptions_filtrees.csv",
            mime="text/csv",
            use_container_width=True,
        )
    with dc2:
        st.download_button(
            "Télécharger Excel",
            data=xlsx_bytes,
            file_name="inscriptions_filtrees.xlsx",
            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
            use_container_width=True,
        )


# -----------------------------
# Page: Zone d'analyse
# -----------------------------
def page_analyses():
    st.markdown("<h2>📋 Analyses avancées</h2>", unsafe_allow_html=True)
    
    if 'filtered_df' not in st.session_state or st.session_state['filtered_df'] is None:
        st.warning("Veuillez d'abord importer et configurer des données dans l'onglet Tableau de bord.")
        return
        
    filtered_df = st.session_state['filtered_df']
    type_map = st.session_state.get('logical_types', {})
    theme_mode = "dark" if st.session_state.get('theme_mode') == "dark" else "light"
    plotly_template = get_plotly_template(theme_mode)
    
    # Ad-hoc analysis builder
    st.markdown("<div class=\"card\"><div class=\"card-title\">Zone d'analyse</div>", unsafe_allow_html=True)
    cat_cols = [c for c in filtered_df.columns if type_map.get(c) in ("categorical", "text")]
    if cat_cols:
        ac1, ac2, ac3 = st.columns([2,1,1])
        with ac1:
            dim_col = st.selectbox("Dimension", options=cat_cols)
        with ac2:
            chart_type = st.selectbox("Type de graphique", options=["Barres", "Camembert"], index=0)
        with ac3:
            topn_dim = st.slider("Top N (dimension)", 3, 50, 10)

        agg = filtered_df.groupby(dim_col).size().reset_index(name="count").sort_values("count", ascending=False).head(topn_dim)
        if chart_type == "Barres":
            fig = px.bar(agg, x=dim_col, y="count", template=plotly_template)
        else:
            fig = px.pie(agg, names=dim_col, values="count", template=plotly_template, hole=0.35)
        st.plotly_chart(fig, use_container_width=True, theme=None)
    st.markdown("</div>", unsafe_allow_html=True)

    # Drilldown option (simple): filtrer sur une dimension/valeur
    st.markdown("<div class=\"card\"><div class=\"card-title\">Drilldown</div>", unsafe_allow_html=True)
    dd_cols = cat_cols
    dd1, dd2 = st.columns([1,2])
    with dd1:
        dd_dim = st.selectbox("Drilldown - dimension", options=[None] + dd_cols)
    
    drill_df = filtered_df.copy()
    if dd_dim:
        values = [x for x in filtered_df[dd_dim].dropna().astype(str).unique()]
        with dd2:
            dd_val = st.selectbox("Valeur", options=[None] + values)
        if dd_val:
            drill_df = filtered_df[filtered_df[dd_dim].astype(str) == dd_val]
            
    search_query = st.text_input("Recherche globale", key="search_analysis")
    df_searched = apply_search(drill_df, search_query)
    st.dataframe(df_searched, use_container_width=True, hide_index=True)
    st.markdown("</div>", unsafe_allow_html=True)

    # Decision Maker View (field-aware, optional)
    st.markdown("<div class=\"card\"><div class=\"card-title\">Vue Décideur (si champs disponibles)</div>", unsafe_allow_html=True)
    # Candidate fields based on provided list
    col_email = find_column(filtered_df, ["Email"]) or find_column(filtered_df, ["E-mail"])
    col_gender = find_column(filtered_df, ["Genre", "Autre genre (Veuillez préciser) : "])
    col_nat = find_column(filtered_df, ["Nationalité"])
    col_country = find_column(filtered_df, ["Pays de résidence"]) or find_column(filtered_df, ["D'où préférez-vous participer à l'événement ?"])
    col_role = find_column(filtered_df, ["Votre profession / statut", "Autre profession (veuillez préciser)"])
    col_aff = find_column(filtered_df, ["Affiliation", "Autre affiliation (Veuillez préciser) : "])
    col_particip = find_column(filtered_df, ["Avez-vous déjà participé à un événement Indaba X Togo ?"])
    col_mode_formation = find_column(filtered_df, ["Comment voulez-vous participer aux formations ?"])
    col_what_do = find_column(filtered_df, ["Que voulez-vous faire ?"])
    col_skills = {
        "Python": find_column(filtered_df, ["Quel est votre niveau en  [Python]", "Quel est votre niveau en [Python]"]),
        "Numpy": find_column(filtered_df, ["Quel est votre niveau en  [Numpy]", "Quel est votre niveau en [Numpy]"]),
        "Pandas": find_column(filtered_df, ["Quel est votre niveau en  [Pandas]", "Quel est votre niveau en [Pandas]"]),
        "Scikit Learn": find_column(filtered_df, ["Quel est votre niveau en  [Scikit Learn]", "Quel est votre niveau en [Scikit Learn]"]),
        "Pytorch": find_column(filtered_df, ["Quel est votre niveau en  [Pytorch]", "Quel est votre niveau en [Pytorch]"]),
        "Deep Learning": find_column(filtered_df, ["Quel est votre niveau en  [Deep Learning]", "Quel est votre niveau en [Deep Learning]"]),
    }

    # KPIs for decision maker
    kcols = st.columns(4)
    with kcols[0]:
        kpi_card("Inscriptions", f"{len(filtered_df):,}")
    with kcols[1]:
        if col_email:
            uniq_people = filtered_df[col_email].astype(str).str.strip().str.lower().dropna().nunique()
            kpi_card("Personnes uniques (email)", f"{uniq_people:,}")
        else:
            kpi_card("Personnes uniques", "-")
    with kcols[2]:
        if col_country and col_country in filtered_df.columns:
            kpi_card("Pays (distincts)", f"{filtered_df[col_country].astype(str).nunique():,}")
        else:
            kpi_card("Pays (distincts)", "-")
    with kcols[3]:
        if col_role and col_role in filtered_df.columns:
            kpi_card("Profils (distincts)", f"{filtered_df[col_role].astype(str).nunique():,}")
        else:
            kpi_card("Profils (distincts)", "-")

    # Row 1 charts: Gender, Country
    dm1 = st.columns(2)
    if col_gender and col_gender in filtered_df.columns and not filtered_df.empty:
        gcounts = filtered_df.groupby(col_gender).size().reset_index(name="count").sort_values("count", ascending=False)
        fig_g = px.pie(gcounts, names=col_gender, values="count", template=get_plotly_template(theme_mode), hole=0.35)
        with dm1[0]:
            chart_card("Répartition par genre", fig_g)
    if col_country and col_country in filtered_df.columns and not filtered_df.empty:
        ccounts = filtered_df.groupby(col_country).size().reset_index(name="count").sort_values("count", ascending=False).head(15)
        fig_c = px.bar(ccounts, x=col_country, y="count", template=get_plotly_template(theme_mode))
        with dm1[1]:
            chart_card("Top 15 pays de résidence", fig_c)

    # Row 2: Participation history and roles
    dm2 = st.columns(2)
    if col_particip and col_particip in filtered_df.columns and not filtered_df.empty:
        pcounts = filtered_df.groupby(col_particip).size().reset_index(name="count")
        fig_p = px.bar(pcounts, x=col_particip, y="count", template=get_plotly_template(theme_mode))
        with dm2[0]:
            chart_card("A déjà participé ?", fig_p)
    if col_role and col_role in filtered_df.columns and not filtered_df.empty:
        rcounts = filtered_df.groupby(col_role).size().reset_index(name="count").sort_values("count", ascending=False).head(15)
        fig_r = px.bar(rcounts, x=col_role, y="count", template=get_plotly_template(theme_mode))
        with dm2[1]:
            chart_card("Professions / Statuts (Top 15)", fig_r)
    
    st.markdown("</div>", unsafe_allow_html=True)


# -----------------------------
# Page: Constructeur de graphiques
# -----------------------------
def page_constructeur_graphiques():
    st.markdown("<h2>📈 Constructeur de graphiques</h2>", unsafe_allow_html=True)
    
    if 'filtered_df' not in st.session_state or st.session_state['filtered_df'] is None:
        st.warning("Veuillez d'abord importer et configurer des données dans l'onglet Tableau de bord.")
        return
    
    filtered_df = st.session_state['filtered_df']
    type_map = st.session_state.get('logical_types', {})
    theme_mode = "dark" if st.session_state.get('theme_mode') == "dark" else "light"
    plotly_template = get_plotly_template(theme_mode)
    
    # Universal Chart Builder
    st.markdown("<div class=\"card\"><div class=\"card-title\">Constructeur de graphiques</div>", unsafe_allow_html=True)
    chart_types = [
        "Barres",
        "Barres empilées",
        "Lignes",
        "Aires",
        "Camembert",
        "Histogramme",
        "Nuage de points",
        "Boîte (Box)",
        "Violon",
    ]
    cA, cB, cC = st.columns([1.2, 1, 1])
    with cA:
        chosen_chart = st.selectbox("Type de graphique", options=chart_types, key="ub_chart_type")
    with cB:
        agg_choice = st.selectbox("Agrégat", options=["count", "sum", "mean", "median", "min", "max"], index=0, key="ub_agg")
    with cC:
        topn_builder = st.number_input("Top N (optionnel)", min_value=0, value=0, step=1, help="0 pour désactiver")

    all_cols = list(filtered_df.columns)
    num_cols = [c for c in all_cols if pd.api.types.is_numeric_dtype(filtered_df[c])]
    date_cols_any = [c for c in all_cols if pd.api.types.is_datetime64_any_dtype(try_parse_datetime(filtered_df[c]))]
    cat_cols_any = [c for c in all_cols if c not in num_cols]

    def aggregate_df(df_src: pd.DataFrame, x_col: Optional[str], y_col: Optional[str], color_col: Optional[str]) -> pd.DataFrame:
        if agg_choice == "count":
            if x_col is not None and y_col is None:
                return df_src.groupby([x_col, color_col] if color_col else [x_col]).size().reset_index(name="value")
            elif x_col is None and y_col is not None:
                return df_src.groupby([y_col, color_col] if color_col else [y_col]).size().reset_index(name="value")
            elif x_col is not None and y_col is not None:
                return df_src.groupby([x_col, y_col]).size().reset_index(name="value")
            else:
                return pd.DataFrame({"value": [len(df_src)]})
        else:
            agg_func = agg_choice
            measure = y_col if (y_col in num_cols) else (x_col if (x_col in num_cols) else (num_cols[0] if num_cols else None))
            if measure is None:
                return df_src.groupby([x_col, color_col] if color_col else [x_col]).size().reset_index(name="value") if x_col else pd.DataFrame({"value": [len(df_src)]})
            group_keys = [k for k in [x_col, color_col] if k]
            out = df_src.groupby(group_keys, dropna=False)[measure].agg(agg_func).reset_index(name="value")
            return out

    if chosen_chart in ("Barres", "Barres empilées"):
        x = st.selectbox("Axe X (cat/date)", options=cat_cols_any, key="ub_bar_x")
        color = st.selectbox("Couleur (optionnel)", options=[None] + cat_cols_any, key="ub_bar_color")
        measure = st.selectbox("Mesure (numérique ou count)", options=["(count)"] + num_cols, key="ub_bar_measure")
        data = aggregate_df(filtered_df, x, None if measure == "(count)" else measure, color)
        if topn_builder and topn_builder > 0 and x in data.columns:
            data = data.sort_values("value", ascending=False).groupby(x).head(1).head(int(topn_builder))
        if chosen_chart == "Barres":
            fig = px.bar(data, x=x, y="value", color=color, template=plotly_template, barmode="group")
        else:
            fig = px.bar(data, x=x, y="value", color=color, template=plotly_template, barmode="relative")
        st.plotly_chart(fig, use_container_width=True, theme=None)
    elif chosen_chart in ("Lignes", "Aires"):
        x = st.selectbox("Axe X (date recommandé)", options=date_cols_any or cat_cols_any, key="ub_line_x")
        color = st.selectbox("Couleur (optionnel)", options=[None] + cat_cols_any, key="ub_line_color")
        measure = st.selectbox("Mesure (numérique ou count)", options=["(count)"] + num_cols, key="ub_line_measure")
        data = aggregate_df(filtered_df, x, None if measure == "(count)" else measure, color)
        if chosen_chart == "Lignes":
            fig = px.line(data, x=x, y="value", color=color, template=plotly_template)
        else:
            fig = px.area(data, x=x, y="value", color=color, template=plotly_template)
        st.plotly_chart(fig, use_container_width=True, theme=None)
    elif chosen_chart == "Camembert":
        names = st.selectbox("Noms (catégorie)", options=cat_cols_any, key="ub_pie_names")
        measure = st.selectbox("Mesure (numérique ou count)", options=["(count)"] + num_cols, key="ub_pie_measure")
        if measure == "(count)":
            data = filtered_df.groupby(names).size().reset_index(name="value")
        else:
            data = filtered_df.groupby(names)[measure].sum().reset_index(name="value")
        fig = px.pie(data, names=names, values="value", template=plotly_template, hole=0.35)
        st.plotly_chart(fig, use_container_width=True, theme=None)
    elif chosen_chart == "Histogramme":
        x = st.selectbox("Colonne numérique", options=num_cols, key="ub_hist_x")
        bins = st.slider("Nb de bacs (bins)", 5, 100, 30)
        fig = px.histogram(filtered_df, x=x, nbins=bins, template=plotly_template)
        st.plotly_chart(fig, use_container_width=True, theme=None)
    elif chosen_chart == "Nuage de points":
        x = st.selectbox("X (numérique)", options=num_cols, key="ub_scatter_x")
        y = st.selectbox("Y (numérique)", options=[c for c in num_cols if c != x], key="ub_scatter_y")
        color = st.selectbox("Couleur (optionnel)", options=[None] + cat_cols_any, key="ub_scatter_color")
        fig = px.scatter(filtered_df, x=x, y=y, color=color, template=plotly_template)
        st.plotly_chart(fig, use_container_width=True, theme=None)
    elif chosen_chart == "Boîte (Box)":
        y = st.selectbox("Y (numérique)", options=num_cols, key="ub_box_y")
        x = st.selectbox("X (catégorie optionnel)", options=[None] + cat_cols_any, key="ub_box_x")
        fig = px.box(filtered_df, x=x, y=y, template=plotly_template)
        st.plotly_chart(fig, use_container_width=True, theme=None)
    elif chosen_chart == "Violon":
        y = st.selectbox("Y (numérique)", options=num_cols, key="ub_violin_y")
        x = st.selectbox("X (catégorie optionnel)", options=[None] + cat_cols_any, key="ub_violin_x")
        fig = px.violin(filtered_df, x=x, y=y, template=plotly_template, box=True, points="outliers")
        st.plotly_chart(fig, use_container_width=True, theme=None)
    st.markdown("</div>", unsafe_allow_html=True)


# -----------------------------
# Page: Envoi d'emails
# -----------------------------
def page_emails():
    st.markdown("<h2>✉️ Envoi d'emails</h2>", unsafe_allow_html=True)
    
    if 'filtered_df' not in st.session_state or st.session_state['filtered_df'] is None:
        st.warning("Veuillez d'abord importer et configurer des données dans l'onglet Tableau de bord.")
        return
    
    filtered_df = st.session_state['filtered_df']
    
    # Email Sender Section
    st.markdown("<div class=\"card\"><div class=\"card-title\">✉️ Envoi d'emails (CSV ou données filtrées)</div>", unsafe_allow_html=True)
    ecols1 = st.columns([1, 1])
    with ecols1[0]:
        st.caption("Source des destinataires")
        use_current = st.radio(
            "Choisir la source",
            options=["Données filtrées actuelles", "Importer un CSV/XLSX"],
            horizontal=False,
            index=0,
            key="email_source_choice",
        )
    with ecols1[1]:
        st.caption("Fichier (si import)")
        upload_mail = st.file_uploader("Importer un fichier", type=["csv", "xlsx"], key="email_upload_file")

    recipients_df: Optional[pd.DataFrame] = None
    if use_current == "Données filtrées actuelles":
        recipients_df = filtered_df.copy()
    else:
        if upload_mail is not None:
            try:
                if upload_mail.name.lower().endswith(".csv"):
                    recipients_df = pd.read_csv(upload_mail)
                else:
                    recipients_df = pd.read_excel(upload_mail)
                recipients_df.columns = [str(c).strip() for c in recipients_df.columns]
            except Exception as e:
                st.error(f"Erreur de lecture du fichier: {e}")

    if recipients_df is None or recipients_df.empty:
        st.info("Importez un fichier ou utilisez les données filtrées pour continuer.")
        st.markdown("</div>", unsafe_allow_html=True)
        return

    # Mapping email column
    email_col_guess = find_column(recipients_df, ["email", "e-mail", "mail"]) or ("Email" if "Email" in recipients_df.columns else None)
    email_col = st.selectbox(
        "Colonne email",
        options=list(recipients_df.columns),
        index=(list(recipients_df.columns).index(email_col_guess) if email_col_guess in recipients_df.columns else 0),
        help="Sélectionnez la colonne contenant les adresses email",
        key="email_col_select",
    )

    # SMTP settings
    st.markdown("<div class=\"card\" style=\"margin-top: 0.75rem;\"><div class=\"card-title\">Paramètres SMTP</div>", unsafe_allow_html=True)
    s1, s2, s3, s4 = st.columns([1.2, 0.8, 1, 1])
    with s1:
        smtp_host = st.text_input("Hôte SMTP", value=os.environ.get("SMTP_HOST", "smtp.gmail.com"))
    with s2:
        smtp_port = st.number_input("Port", min_value=1, max_value=65535, value=int(os.environ.get("SMTP_PORT", 587)))
    with s3:
        use_tls = st.selectbox("Sécurité", options=["STARTTLS", "SSL"], index=0) == "STARTTLS"
    with s4:
        reply_to = st.text_input("Reply-To (optionnel)", value=os.environ.get("SMTP_REPLY_TO", ""))
    s5, s6 = st.columns([1, 1])
    with s5:
        sender_email = st.text_input("Adresse expéditrice", value=os.environ.get("SMTP_SENDER", ""))
    with s6:
        sender_password = st.text_input("Mot de passe/clé appli", type="password", value=os.environ.get("SMTP_PASSWORD", ""))
    st.markdown("</div>", unsafe_allow_html=True)

    # Composition
    st.markdown("<div class=\"card\" style=\"margin-top: 0.75rem;\"><div class=\"card-title\">Composer le message</div>", unsafe_allow_html=True)
    placeholders = ", ".join([f"{{{c}}}" for c in recipients_df.columns])
    subj = st.text_input("Objet", placeholder="Objet de l'email. Vous pouvez utiliser des variables comme {Nom}")
    body = st.text_area(
        "Corps (texte)",
        height=180,
        placeholder="Bonjour {Prenom} {Nom},\n\nVotre statut: {Statut}\n...",
        help=f"Variables disponibles: {placeholders}",
    )
    st.caption("Astuce: utilisez {NomColonne} pour insérer des champs du CSV.")

    # Preview first recipient
    pv1, pv2 = st.columns([1, 1])
    with pv1:
        st.subheader("Aperçu des données (5)")
        st.dataframe(recipients_df.head(5), use_container_width=True, hide_index=True)
    with pv2:
        st.subheader("Aperçu email (1er destinataire)")
        try:
            if not recipients_df.empty:
                row0 = recipients_df.iloc[0].to_dict()
                st.write("À:", recipients_df[email_col].iloc[0])
                st.write("Objet:", safe_format_template(subj, row0))
                st.code(safe_format_template(body, row0))
        except Exception:
            st.caption("Impossible de générer l'aperçu.")
    st.markdown("</div>", unsafe_allow_html=True)

    # Sending controls
    st.markdown("<div class=\"card\" style=\"margin-top: 0.75rem;\"><div class=\"card-title\">Envoi</div>", unsafe_allow_html=True)
    c_left, c_mid, c_right = st.columns([1, 1, 1])
    with c_left:
        limit_send = st.number_input("Limiter (0 = tout)", min_value=0, value=0, help="Pour tester, limiter le nombre d'emails envoyés")
    with c_mid:
        start_at = st.number_input("Début à l'index", min_value=0, value=0)
    with c_right:
        confirm = st.checkbox("Je confirme vouloir envoyer ces emails", value=False)

    do_send = st.button("Envoyer", type="primary", use_container_width=True, disabled=not confirm)

    if do_send:
        if not sender_email or not smtp_host or not subj or not body:
            st.error("Veuillez remplir l'hôte SMTP, l'adresse expéditrice, l'objet et le corps.")
        else:
            total = len(recipients_df)
            indices = list(range(start_at, total))
            if limit_send and limit_send > 0:
                indices = indices[: int(limit_send)]
            progress = st.progress(0)
            sent_ok = 0
            log_container = st.container()
            for idx_i, i in enumerate(indices, start=1):
                try:
                    row = recipients_df.iloc[i]
                    to_addr = str(row[email_col]).strip()
                    if not to_addr or "@" not in to_addr:
                        raise ValueError("Adresse email invalide")
                    row_dict = row.to_dict()
                    subject_i = safe_format_template(subj, row_dict)
                    body_i = safe_format_template(body, row_dict)
                    send_email_smtp(
                        smtp_host=smtp_host,
                        smtp_port=int(smtp_port),
                        sender_email=sender_email,
                        sender_password=sender_password,
                        use_tls=use_tls,
                        to_email=to_addr,
                        subject=subject_i,
                        body_text=body_i,
                        reply_to=(reply_to or None),
                    )
                    sent_ok += 1
                    log_container.success(f"Envoyé à {to_addr}")
                except Exception as e:
                    log_container.error(f"Échec pour index {i}: {e}")
                progress.progress(int(idx_i * 100 / max(1, len(indices))))
            st.info(f"Terminé. Succès: {sent_ok}/{len(indices)}")
    st.markdown("</div>", unsafe_allow_html=True)


# -----------------------------
# Main App
# -----------------------------
def main():
    inject_base_css()

    # Header
    col_logo, col_title, col_right = st.columns([1, 3, 1])
    with col_title:
        st.markdown("<h1 style='text-align:center; margin-top: 0;'>Tableau de bord des inscriptions</h1>", unsafe_allow_html=True)
    with col_right:
        st.write("")

    # Charger les contrôles de la barre latérale
    # (ces contrôles sont partagés entre tous les onglets)
    df, type_map, theme_mode, _, unique_keys = sidebar_controls()
    
    # Stocker les types dans session_state pour les autres onglets
    if df is not None:
        st.session_state['logical_types'] = type_map
        st.session_state['unique_keys'] = unique_keys
        st.session_state['theme_mode'] = theme_mode
    
    # Onglets de l'application
    tab1, tab2, tab3, tab4 = st.tabs([
        "📊 Tableau de bord", 
        "📋 Analyses avancées", 
        "📈 Constructeur graphiques", 
        "✉️ Envoi emails"
    ])
    
    with tab1:
        page_tableau_de_bord()
    
    with tab2:
        page_analyses()
    
    with tab3:
        page_constructeur_graphiques()
        
    with tab4:
        page_emails()


if __name__ == "__main__":
    main()