Delete src/app.py

src/app.py

@@ -1,1668 +0,0 @@
-import warnings
-import io
-import zipfile
-from datetime import datetime
-
-warnings.filterwarnings("ignore")
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import streamlit as st
-from matplotlib.ticker import FuncFormatter
-from scipy import stats
-
-# ==================== PAGE CONFIG ====================
-st.set_page_config(
-    page_title="Seaborn & Matplotlib Visual Lab",
-    page_icon="📊",
-    layout="wide",
-    initial_sidebar_state="expanded",
-)
-
-# ==================== GLOBAL STYLE ====================
-st.markdown(
-    """
-<style>
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&display=swap');
-
-    * {
-        font-family: 'Inter', sans-serif;
-    }
-
-    .stApp {
-        background: radial-gradient(circle at 0% 0%, #020617 0, #020617 45%, #020617 100%);
-        color: #e5e7eb;
-    }
-
-    .block-container {
-        padding-top: 1.4rem;
-        padding-bottom: 3rem;
-    }
-
-    .main-header {
-        font-size: 3.1rem;
-        font-weight: 800;
-        background: linear-gradient(135deg, #38bdf8 0%, #6366f1 40%, #a855f7 100%);
-        -webkit-background-clip: text;
-        -webkit-text-fill-color: transparent;
-        margin-bottom: 0.2rem;
-    }
-
-    .subtitle {
-        font-size: 1.05rem;
-        color: #9ca3af;
-        margin-bottom: 1.1rem;
-    }
-
-    .metric-row {
-        display: flex;
-        flex-wrap: wrap;
-        gap: 0.9rem;
-        margin-top: 0.3rem;
-        margin-bottom: 1.0rem;
-    }
-
-    .metric-card {
-        background: radial-gradient(circle at 0% 0%, rgba(56, 189, 248, 0.18), rgba(15, 23, 42, 0.96));
-        padding: 0.9rem 1.2rem;
-        border-radius: 14px;
-        color: #e5e7eb;
-        box-shadow:
-            0 14px 40px rgba(15, 23, 42, 0.9),
-            0 0 0 1px rgba(148, 163, 184, 0.45);
-        min-width: 160px;
-    }
-
-    .metric-card-label {
-        font-size: 0.75rem;
-        letter-spacing: 0.08em;
-        text-transform: uppercase;
-        color: #9ca3af;
-    }
-
-    .metric-card-value {
-        font-size: 1.45rem;
-        font-weight: 700;
-        margin-top: 0.15rem;
-    }
-
-    .info-box {
-        background: radial-gradient(circle at 0% 0%, rgba(45, 212, 191, 0.25), rgba(56, 189, 248, 0.10));
-        padding: 0.9rem 1.2rem;
-        border-radius: 14px;
-        border-left: 4px solid #22d3ee;
-        margin: 0.7rem 0 1.0rem 0;
-        color: #e0f2fe;
-        box-shadow: 0 16px 40px rgba(15, 23, 42, 0.9);
-        backdrop-filter: blur(18px);
-    }
-
-    .tip-box {
-        background: linear-gradient(135deg, rgba(250, 204, 21, 0.12), rgba(251, 191, 36, 0.04));
-        padding: 0.75rem 1rem;
-        border-radius: 10px;
-        border-left: 3px solid rgba(250, 204, 21, 0.7);
-        margin: 0.4rem 0;
-        font-size: 0.9rem;
-        color: #facc15;
-    }
-
-    .code-box {
-        background: #020617;
-        color: #e5e7eb;
-        padding: 0.9rem 1rem;
-        border-radius: 12px;
-        border: 1px solid rgba(148, 163, 184, 0.5);
-        margin: 0.5rem 0;
-        font-size: 0.9rem;
-        box-shadow: 0 14px 36px rgba(15, 23, 42, 0.9);
-    }
-
-    .plot-container {
-        background: radial-gradient(circle at 0% 0%, rgba(148, 163, 184, 0.16), rgba(15, 23, 42, 0.96));
-        padding: 1.4rem 1.5rem;
-        border-radius: 18px;
-        box-shadow:
-            0 20px 50px rgba(15, 23, 42, 0.95),
-            0 0 0 1px rgba(148, 163, 184, 0.4);
-        margin: 1.0rem 0 1.4rem 0;
-        border: 1px solid rgba(148, 163, 184, 0.35);
-    }
-
-    .control-panel {
-        background: linear-gradient(145deg, rgba(15, 23, 42, 0.98), rgba(30, 64, 175, 0.85));
-        padding: 0.8rem 1.1rem;
-        border-radius: 999px;
-        box-shadow:
-            0 16px 40px rgba(15, 23, 42, 0.9),
-            0 0 0 1px rgba(129, 140, 248, 0.7);
-        color: #e5e7eb;
-        margin-bottom: 0.7rem;
-    }
-
-    .control-panel-header {
-        font-size: 0.85rem;
-        font-weight: 700;
-        text-transform: uppercase;
-        letter-spacing: 0.12em;
-        margin: 0;
-        color: #e5e7eb;
-        opacity: 0.98;
-    }
-
-    .stTabs [data-baseweb="tab-list"] {
-        gap: 0.7rem;
-        background: transparent;
-        padding: 0.4rem 0 0.8rem 0;
-        border-radius: 0;
-        border-bottom: 1px solid rgba(148, 163, 184, 0.35);
-    }
-
-    .stTabs [data-baseweb="tab"] {
-        height: 3.3rem;
-        padding: 0 1.8rem;
-        font-weight: 600;
-        border-radius: 999px;
-        background: rgba(15, 23, 42, 0.86);
-        border: 1px solid rgba(148, 163, 184, 0.5);
-        color: #e5e7eb;
-        transition: transform 0.18s ease, background 0.18s ease,
-                    border-color 0.18s ease, box-shadow 0.18s ease;
-    }
-
-    .stTabs [data-baseweb="tab"]:hover {
-        transform: translateY(-1px);
-        border-color: rgba(129, 140, 248, 0.9);
-        box-shadow: 0 10px 22px rgba(15, 23, 42, 0.85);
-    }
-
-    .stTabs [data-baseweb="tab"][aria-selected="true"] {
-        background: linear-gradient(135deg, #6366f1, #ec4899);
-        color: #ffffff;
-        border-color: transparent;
-        box-shadow:
-            0 0 0 1px rgba(15, 23, 42, 0.9),
-            0 14px 30px rgba(15, 23, 42, 0.95);
-    }
-</style>
-""",
-    unsafe_allow_html=True,
-)
-
-# ==================== SESSION STATE ====================
-if "gallery" not in st.session_state:
-    st.session_state["gallery"] = []
-
-if "export_dpi" not in st.session_state:
-    st.session_state["export_dpi"] = 300
-
-# ==================== HELPERS ====================
-def use_theme(context: str = "notebook", style: str = "whitegrid", palette: str = "deep") -> None:
-    sns.set_theme(context=context, style=style)
-    sns.set_palette(palette)
-    plt.rcParams.update(
-        {
-            "figure.figsize": (10, 6),
-            "savefig.dpi": 300,
-            "figure.dpi": 150,
-            "axes.spines.top": False,
-            "axes.spines.right": False,
-            "figure.autolayout": True,
-            "grid.alpha": 0.3,
-            "grid.linestyle": "--",
-            "font.size": 10,
-            "axes.labelsize": 11,
-            "axes.titlesize": 13,
-            "legend.fontsize": 9,
-        }
-    )
-
-
-def apply_dark(fig: plt.Figure, dark: bool = False) -> None:
-    if not dark:
-        return
-    fig.patch.set_facecolor("#020617")
-    for ax in fig.get_axes():
-        ax.set_facecolor("#020617")
-        ax.tick_params(colors="#e5e7eb")
-        for spine in ax.spines.values():
-            spine.set_color("#4b5563")
-        for item in [ax.title, ax.xaxis.label, ax.yaxis.label]:
-            if item:
-                item.set_color("#e5e7eb")
-        for t in ax.get_xticklabels() + ax.get_yticklabels():
-            t.set_color("#e5e7eb")
-        legend = ax.get_legend()
-        if legend:
-            legend.get_frame().set_facecolor("#020617")
-            for text in legend.get_texts():
-                text.set_color("#e5e7eb")
-
-
-@st.cache_data
-def load_builtin_data() -> dict:
-    return {
-        "Tips": sns.load_dataset("tips"),
-        "Penguins": sns.load_dataset("penguins").dropna(),
-        "Flights": sns.load_dataset("flights"),
-        "Iris": sns.load_dataset("iris"),
-        "Diamonds (1K sample)": sns.load_dataset("diamonds").sample(1000, random_state=42),
-        "Titanic": sns.load_dataset("titanic"),
-        "Car Crashes": sns.load_dataset("car_crashes"),
-    }
-
-
-def save_to_gallery(fig: plt.Figure, name: str, description: str) -> None:
-    buf = io.BytesIO()
-    dpi = st.session_state.get("export_dpi", 300)
-    fig.savefig(
-        buf,
-        dpi=dpi,
-        bbox_inches="tight",
-        format="png",
-        facecolor=fig.get_facecolor(),
-    )
-    buf.seek(0)
-    st.session_state["gallery"].append(
-        {
-            "name": name,
-            "description": description,
-            "image": buf.getvalue(),
-            "timestamp": datetime.now(),
-        }
-    )
-
-
-def show_code_example(code: str, description: str = "") -> None:
-    if description:
-        st.markdown(
-            f'<div class="tip-box"><strong>Tip:</strong> {description}</div>',
-            unsafe_allow_html=True,
-        )
-    st.markdown('<div class="code-box">', unsafe_allow_html=True)
-    st.code(code, language="python")
-    st.markdown("</div>", unsafe_allow_html=True)
-
-# ==================== HEADER ====================
-st.markdown(
-    '<h1 class="main-header">Seaborn & Matplotlib Visual Lab</h1>',
-    unsafe_allow_html=True,
-)
-st.markdown(
-    '<p class="subtitle">Interactive environment to explore, compare, and export visualizations with Seaborn and Matplotlib.</p>',
-    unsafe_allow_html=True,
-)
-
-# ==================== SIDEBAR ====================
-with st.sidebar:
-    st.markdown("### Data settings")
-
-    # Built-in datasets only
-    builtin = load_builtin_data()
-    dataset_label = st.selectbox(
-        "Built-in only",
-        list(builtin.keys()),
-        key="sb_dataset",
-    )
-    df = builtin[dataset_label]
-
-    st.markdown("---")
-
-    with st.expander("Visual theme", expanded=False):
-        context = st.selectbox(
-            "Seaborn context",
-            ["notebook", "paper", "talk", "poster"],
-            index=0,
-            key="sb_context",
-        )
-        style = st.selectbox(
-            "Seaborn style",
-            ["whitegrid", "darkgrid", "white", "dark", "ticks"],
-            index=0,
-            key="sb_style",
-        )
-        palette = st.selectbox(
-            "Color palette",
-            ["deep", "muted", "bright", "pastel", "dark", "colorblind", "Set2", "husl"],
-            index=0,
-            key="sb_palette",
-        )
-        use_theme(context, style, palette)
-
-        theme_mode = st.radio(
-            "Figure mode",
-            ["Light", "Dark"],
-            index=1,
-            horizontal=True,
-            key="sb_theme_mode",
-        )
-        DARK = theme_mode == "Dark"
-
-    st.markdown("---")
-
-    st.markdown("### Export settings")
-    dpi = st.slider(
-        "Image quality (DPI)",
-        72,
-        600,
-        300,
-        step=50,
-        key="sb_dpi",
-    )
-    st.session_state["export_dpi"] = dpi
-
-    if st.session_state["gallery"]:
-        st.success(f"{len(st.session_state['gallery'])} plots in gallery")
-        if st.button("Clear gallery", key="sb_clear_gallery"):
-            st.session_state["gallery"] = []
-            st.rerun()
-
-# fallback
-if df is None:
-    df = builtin["Tips"]
-    dataset_label = "Tips"
-
-numeric_cols_all = df.select_dtypes(include=[np.number]).columns.tolist()
-categorical_cols_all = df.select_dtypes(include=["object", "category"]).columns.tolist()
-missing_ratio = float(df.isna().mean().mean() * 100)
-
-# ==================== TOP METRICS ====================
-st.markdown(
-    f"""
-<div class="metric-row">
-  <div class="metric-card">
-    <div class="metric-card-label">Dataset</div>
-    <div class="metric-card-value">{dataset_label}</div>
-  </div>
-  <div class="metric-card">
-    <div class="metric-card-label">Rows</div>
-    <div class="metric-card-value">{len(df):,}</div>
-  </div>
-  <div class="metric-card">
-    <div class="metric-card-label">Columns</div>
-    <div class="metric-card-value">{len(df.columns):,}</div>
-  </div>
-  <div class="metric-card">
-    <div class="metric-card-label">Numeric features</div>
-    <div class="metric-card-value">{len(numeric_cols_all)}</div>
-  </div>
-  <div class="metric-card">
-    <div class="metric-card-label">Categorical features</div>
-    <div class="metric-card-value">{len(categorical_cols_all)}</div>
-  </div>
-  <div class="metric-card">
-    <div class="metric-card-label">Missing ratio</div>
-    <div class="metric-card-value">{missing_ratio:.1f}%</div>
-  </div>
-</div>
-""",
-    unsafe_allow_html=True,
-)
-
-# ==================== TABS ====================
-tab_overview, tab_seaborn, tab_mpl, tab_compare, tab_gallery = st.tabs(
-    [
-        "Overview",
-        "Seaborn builder",
-        "Matplotlib builder",
-        "Compare",
-        "Gallery",
-    ]
-)
-
-# ==================== TAB: OVERVIEW ====================
-with tab_overview:
-    st.markdown("## Overview")
-    st.markdown(
-        '<div class="info-box"><strong>Goal:</strong> Quick health check of the current dataset and a first look at its distributions.</div>',
-        unsafe_allow_html=True,
-    )
-
-    col_left, col_right = st.columns([2, 1])
-
-    with col_left:
-        st.markdown("### Sample")
-        st.dataframe(df.head(10), use_container_width=True)
-
-        if numeric_cols_all:
-            st.markdown("### Quick distribution")
-            dist_col = st.selectbox(
-                "Numeric column",
-                numeric_cols_all,
-                key="ov_dist_col",
-            )
-            fig, ax = plt.subplots(figsize=(10, 4))
-            sns.histplot(df, x=dist_col, bins=30, kde=True, ax=ax)
-            ax.set_title(f"{dist_col} distribution", fontsize=13, fontweight="bold")
-            apply_dark(fig, DARK)
-            st.pyplot(fig)
-
-    with col_right:
-        st.markdown("### Types & missing")
-        schema_data = {
-            "column": df.columns,
-            "dtype": df.dtypes.astype(str),
-            "missing_%": (df.isna().mean() * 100).round(1),
-        }
-        schema_df = pd.DataFrame(schema_data)
-        st.dataframe(schema_df, height=260, use_container_width=True)
-
-        if len(numeric_cols_all) >= 2:
-            st.markdown("### Small correlation view")
-            cols_small = numeric_cols_all[: min(4, len(numeric_cols_all))]
-            corr = df[cols_small].corr()
-            fig2, ax2 = plt.subplots(figsize=(4, 4))
-            sns.heatmap(
-                corr,
-                annot=True,
-                fmt=".2f",
-                cmap="vlag",
-                center=0,
-                square=True,
-                cbar=False,
-                ax=ax2,
-            )
-            ax2.set_title("Correlation (subset)", fontsize=11, fontweight="bold")
-            apply_dark(fig2, DARK)
-            st.pyplot(fig2)
-
-# ==================== TAB: SEABORN BUILDER ====================
-with tab_seaborn:
-    st.markdown("## Seaborn builder")
-    st.markdown(
-        '<div class="info-box"><strong>Goal:</strong> Build Seaborn plots by selecting columns and options. The code snippet updates automatically.</div>',
-        unsafe_allow_html=True,
-    )
-
-    if df.empty:
-        st.warning("No data loaded.")
-    else:
-        col_plot, col_ctrl = st.columns([7, 3])
-
-        with col_ctrl:
-            # Pill with header INSIDE
-            st.markdown(
-                """
-<div class="control-panel">
-  <div class="control-panel-header">PLOT SETUP</div>
-</div>
-""",
-                unsafe_allow_html=True,
-            )
-
-            family = st.selectbox(
-                "Plot family",
-                [
-                    "Distribution",
-                    "Relationship",
-                    "Category",
-                    "Matrix / Heatmap",
-                    "Multi-variable",
-                ],
-                key="sb_family",
-            )
-
-            code_str = ""
-            description = ""
-            fig_seaborn = None
-
-            if family == "Distribution":
-                kind = st.selectbox(
-                    "Plot type",
-                    [
-                        "Histogram",
-                        "KDE",
-                        "Histogram + KDE",
-                        "Box",
-                        "Violin",
-                        "ECDF",
-                    ],
-                    key="sb_dist_kind",
-                )
-                if not numeric_cols_all:
-                    num_col = None
-                    st.error("No numeric columns in this dataset.")
-                else:
-                    num_col = st.selectbox(
-                        "Numeric column",
-                        numeric_cols_all,
-                        key="sb_dist_num",
-                    )
-
-                hue_col = None
-                if categorical_cols_all and kind in ["Histogram", "KDE", "Histogram + KDE", "ECDF"]:
-                    use_hue_dist = st.checkbox(
-                        "Color by category",
-                        value=False,
-                        key="sb_dist_use_hue",
-                    )
-                    if use_hue_dist:
-                        hue_col = st.selectbox(
-                            "Hue",
-                            categorical_cols_all,
-                            key="sb_dist_hue",
-                        )
-                bins = st.slider(
-                    "Bins (for histogram)",
-                    5,
-                    80,
-                    30,
-                    key="sb_dist_bins",
-                )
-                log_scale = st.checkbox(
-                    "Log scale on x",
-                    value=False,
-                    key="sb_dist_log",
-                )
-
-            elif family == "Relationship":
-                rel_kind = st.selectbox(
-                    "Plot type",
-                    [
-                        "Scatter",
-                        "Regression",
-                        "Line",
-                    ],
-                    key="sb_rel_kind",
-                )
-                if len(numeric_cols_all) < 2:
-                    x_rel = y_rel = None
-                    st.error("Need at least two numeric columns.")
-                else:
-                    x_rel = st.selectbox(
-                        "X variable",
-                        numeric_cols_all,
-                        key="sb_rel_x",
-                    )
-                    y_rel = st.selectbox(
-                        "Y variable",
-                        [c for c in numeric_cols_all if c != x_rel],
-                        key="sb_rel_y",
-                    )
-                hue_rel = None
-                if categorical_cols_all and rel_kind in ["Scatter", "Line"]:
-                    use_hue_rel = st.checkbox(
-                        "Color by category",
-                        value=False,
-                        key="sb_rel_use_hue",
-                    )
-                    if use_hue_rel:
-                        hue_rel = st.selectbox(
-                            "Hue",
-                            categorical_cols_all,
-                            key="sb_rel_hue",
-                        )
-                alpha_rel = st.slider(
-                    "Point transparency",
-                    0.1,
-                    1.0,
-                    0.7,
-                    0.05,
-                    key="sb_rel_alpha",
-                )
-
-            elif family == "Category":
-                if not categorical_cols_all:
-                    st.error("No categorical columns in this dataset.")
-                    cat_var = num_cat = None
-                else:
-                    cat_var = st.selectbox(
-                        "Category",
-                        categorical_cols_all,
-                        key="sb_cat_var",
-                    )
-                cat_kind = st.selectbox(
-                    "Plot type",
-                    [
-                        "Count",
-                        "Bar (mean)",
-                        "Box",
-                        "Violin",
-                    ],
-                    key="sb_cat_kind",
-                )
-                num_cat = None
-                if cat_kind in ["Bar (mean)", "Box", "Violin"]:
-                    if not numeric_cols_all:
-                        st.error("No numeric columns for this plot type.")
-                    else:
-                        num_cat = st.selectbox(
-                            "Numeric column",
-                            numeric_cols_all,
-                            key="sb_cat_num",
-                        )
-
-                if cat_var is not None:
-                    order_top = st.slider(
-                        "Top categories",
-                        3,
-                        min(15, df[cat_var].nunique()),
-                        min(8, df[cat_var].nunique()),
-                        key="sb_cat_top",
-                    )
-
-            elif family == "Matrix / Heatmap":
-                if len(numeric_cols_all) < 2:
-                    st.error("Need at least two numeric columns.")
-                    selected_hm = []
-                else:
-                    selected_hm = st.multiselect(
-                        "Numeric variables",
-                        numeric_cols_all,
-                        default=numeric_cols_all[: min(6, len(numeric_cols_all))],
-                        key="sb_hm_vars",
-                    )
-                annot_hm = st.checkbox(
-                    "Show values",
-                    value=True,
-                    key="sb_hm_annot",
-                )
-                center_zero = st.checkbox(
-                    "Center at zero",
-                    value=True,
-                    key="sb_hm_center",
-                )
-
-            else:  # Multi-variable
-                if len(numeric_cols_all) < 2:
-                    st.error("Need at least two numeric columns.")
-                    multi_vars = []
-                else:
-                    multi_vars = st.multiselect(
-                        "Numeric variables",
-                        numeric_cols_all,
-                        default=numeric_cols_all[: min(4, len(numeric_cols_all))],
-                        key="sb_multi_vars",
-                    )
-                sample_n = st.slider(
-                    "Sample rows",
-                    100,
-                    min(len(df), 1000),
-                    min(400, len(df)),
-                    key="sb_multi_sample",
-                )
-                hue_multi = None
-                if categorical_cols_all:
-                    use_hue_multi = st.checkbox(
-                        "Color by category",
-                        value=False,
-                        key="sb_multi_use_hue",
-                    )
-                    if use_hue_multi:
-                        hue_multi = st.selectbox(
-                            "Hue",
-                            categorical_cols_all,
-                            key="sb_multi_hue",
-                        )
-
-        with col_plot:
-            st.markdown('<div class="plot-container">', unsafe_allow_html=True)
-
-            # ------- Distribution -------
-            if family == "Distribution" and numeric_cols_all and num_col is not None:
-                fig_seaborn, ax = plt.subplots(figsize=(10, 5))
-
-                if kind == "Histogram":
-                    sns.histplot(
-                        data=df,
-                        x=num_col,
-                        bins=bins,
-                        hue=hue_col,
-                        kde=False,
-                        ax=ax,
-                        log_scale=log_scale,
-                    )
-                elif kind == "KDE":
-                    sns.kdeplot(
-                        data=df,
-                        x=num_col,
-                        hue=hue_col,
-                        fill=True,
-                        ax=ax,
-                        log_scale=log_scale,
-                    )
-                elif kind == "Histogram + KDE":
-                    sns.histplot(
-                        data=df,
-                        x=num_col,
-                        bins=bins,
-                        hue=hue_col,
-                        kde=True,
-                        ax=ax,
-                        log_scale=log_scale,
-                    )
-                elif kind == "Box":
-                    sns.boxplot(
-                        data=df,
-                        x=num_col,
-                        ax=ax,
-                    )
-                elif kind == "Violin":
-                    sns.violinplot(
-                        data=df,
-                        x=num_col,
-                        ax=ax,
-                    )
-                else:  # ECDF
-                    sns.ecdfplot(
-                        data=df,
-                        x=num_col,
-                        hue=hue_col,
-                        ax=ax,
-                    )
-                    ax.yaxis.set_major_formatter(
-                        FuncFormatter(lambda y, _: f"{y:.0%}")
-                    )
-
-                ax.set_title(f"{kind} for {num_col}", fontsize=13, fontweight="bold")
-                apply_dark(fig_seaborn, DARK)
-                st.pyplot(fig_seaborn)
-
-                hue_part = f', hue="{hue_col}"' if hue_col else ""
-                extra_kwargs = ""
-                if kind in ["Histogram", "Histogram + KDE"]:
-                    extra_kwargs = f", bins={bins}"
-                    if log_scale:
-                        extra_kwargs += ", log_scale=True"
-                if kind in ["KDE", "ECDF"] and log_scale:
-                    extra_kwargs = ", log_scale=True"
-                if kind == "Histogram + KDE":
-                    fn = "histplot"
-                    extra_kwargs = f", bins={bins}, kde=True"
-                elif kind == "Histogram":
-                    fn = "histplot"
-                elif kind == "KDE":
-                    fn = "kdeplot"
-                elif kind == "Box":
-                    fn = "boxplot"
-                elif kind == "Violin":
-                    fn = "violinplot"
-                else:
-                    fn = "ecdfplot"
-
-                code_str = f"""fig, ax = plt.subplots(figsize=(10, 5))
-sns.{fn}(data=df, x="{num_col}"{hue_part}{extra_kwargs}, ax=ax)
-ax.set_title("{kind} for {num_col}")
-plt.show()"""
-                description = "Distribution pattern: shape, spread, and tails of a single numeric column."
-
-            # ------- Relationship -------
-            elif family == "Relationship" and len(numeric_cols_all) >= 2 and x_rel is not None:
-                fig_seaborn, ax = plt.subplots(figsize=(10, 5))
-
-                if rel_kind == "Scatter":
-                    sns.scatterplot(
-                        data=df,
-                        x=x_rel,
-                        y=y_rel,
-                        hue=hue_rel,
-                        alpha=alpha_rel,
-                        s=70,
-                        ax=ax,
-                    )
-                elif rel_kind == "Line":
-                    sns.lineplot(
-                        data=df,
-                        x=x_rel,
-                        y=y_rel,
-                        hue=hue_rel,
-                        ax=ax,
-                    )
-                else:  # Regression
-                    sns.regplot(
-                        data=df,
-                        x=x_rel,
-                        y=y_rel,
-                        ax=ax,
-                        scatter_kws={"alpha": alpha_rel, "s": 60},
-                        line_kws={"linewidth": 2},
-                    )
-
-                ax.set_title(
-                    f"{rel_kind}: {y_rel} vs {x_rel}",
-                    fontsize=13,
-                    fontweight="bold",
-                )
-                apply_dark(fig_seaborn, DARK)
-                st.pyplot(fig_seaborn)
-
-                if rel_kind == "Scatter":
-                    hue_part = f', hue="{hue_rel}"' if hue_rel else ""
-                    code_str = f"""fig, ax = plt.subplots(figsize=(10, 5))
-sns.scatterplot(
-    data=df,
-    x="{x_rel}",
-    y="{y_rel}"{hue_part},
-    alpha=0.7,
-    s=70,
-    ax=ax,
-)
-ax.set_title("Scatter: {y_rel} vs {x_rel}")
-plt.show()"""
-                elif rel_kind == "Line":
-                    hue_part = f', hue="{hue_rel}"' if hue_rel else ""
-                    code_str = f"""fig, ax = plt.subplots(figsize=(10, 5))
-sns.lineplot(
-    data=df,
-    x="{x_rel}",
-    y="{y_rel}"{hue_part},
-    ax=ax,
-)
-ax.set_title("Line: {y_rel} vs {x_rel}")
-plt.show()"""
-                else:
-                    code_str = f"""fig, ax = plt.subplots(figsize=(10, 5))
-sns.regplot(
-    data=df,
-    x="{x_rel}",
-    y="{y_rel}",
-    scatter_kws={{"alpha": 0.7, "s": 60}},
-    line_kws={{"linewidth": 2}},
-    ax=ax,
-)
-ax.set_title("Regression: {y_rel} vs {x_rel}")
-plt.show()"""
-                description = "Relationship pattern: how two numeric variables move together."
-
-            # ------- Category -------
-            elif family == "Category" and categorical_cols_all and cat_var is not None:
-                fig_seaborn, ax = plt.subplots(figsize=(10, 5))
-
-                df_tmp = df.copy()
-                top_cats = (
-                    df_tmp[cat_var]
-                    .value_counts()
-                    .head(order_top)
-                    .index
-                )
-                df_tmp = df_tmp[df_tmp[cat_var].isin(top_cats)]
-
-                if cat_kind == "Count":
-                    sns.countplot(
-                        data=df_tmp,
-                        y=cat_var,
-                        order=top_cats,
-                        ax=ax,
-                    )
-                    for container in ax.containers:
-                        ax.bar_label(container, padding=3)
-                elif cat_kind == "Bar (mean)":
-                    sns.barplot(
-                        data=df_tmp,
-                        y=cat_var,
-                        x=num_cat,
-                        order=top_cats,
-                        ax=ax,
-                        ci=95,
-                    )
-                elif cat_kind == "Box":
-                    sns.boxplot(
-                        data=df_tmp,
-                        y=cat_var,
-                        x=num_cat,
-                        order=top_cats,
-                        ax=ax,
-                    )
-                else:  # Violin
-                    sns.violinplot(
-                        data=df_tmp,
-                        y=cat_var,
-                        x=num_cat,
-                        order=top_cats,
-                        ax=ax,
-                    )
-
-                ax.set_title(
-                    f"{cat_kind} for {cat_var}",
-                    fontsize=13,
-                    fontweight="bold",
-                )
-                apply_dark(fig_seaborn, DARK)
-                st.pyplot(fig_seaborn)
-
-                if cat_kind == "Count":
-                    code_str = f"""fig, ax = plt.subplots(figsize=(10, 5))
-sns.countplot(
-    data=df,
-    y="{cat_var}",
-    ax=ax,
-)
-ax.set_title("Count for {cat_var}")
-plt.show()"""
-                elif cat_kind == "Bar (mean)":
-                    code_str = f"""fig, ax = plt.subplots(figsize=(10, 5))
-sns.barplot(
-    data=df,
-    y="{cat_var}",
-    x="{num_cat}",
-    ci=95,
-    ax=ax,
-)
-ax.set_title("Mean {num_cat} by {cat_var}")
-plt.show()"""
-                elif cat_kind == "Box":
-                    code_str = f"""fig, ax = plt.subplots(figsize=(10, 5))
-sns.boxplot(
-    data=df,
-    y="{cat_var}",
-    x="{num_cat}",
-    ax=ax,
-)
-ax.set_title("Box: {num_cat} by {cat_var}")
-plt.show()"""
-                else:
-                    code_str = f"""fig, ax = plt.subplots(figsize=(10, 5))
-sns.violinplot(
-    data=df,
-    y="{cat_var}",
-    x="{num_cat}",
-    ax=ax,
-)
-ax.set_title("Violin: {num_cat} by {cat_var}")
-plt.show()"""
-                description = "Category pattern: compare distributions or means across groups."
-
-            # ------- Matrix / Heatmap -------
-            elif family == "Matrix / Heatmap" and selected_hm:
-                corr = df[selected_hm].corr()
-                fig_seaborn, ax = plt.subplots(figsize=(7, 6))
-                sns.heatmap(
-                    corr,
-                    annot=annot_hm,
-                    fmt=".2f",
-                    cmap="vlag",
-                    center=0 if center_zero else None,
-                    square=True,
-                    linewidths=1,
-                    cbar_kws={"shrink": 0.8},
-                    ax=ax,
-                )
-                ax.set_title("Correlation heatmap", fontsize=13, fontweight="bold")
-                apply_dark(fig_seaborn, DARK)
-                st.pyplot(fig_seaborn)
-
-                center_value = "0" if center_zero else "None"
-                code_str = f"""corr = df[{selected_hm}].corr()
-fig, ax = plt.subplots(figsize=(7, 6))
-sns.heatmap(
-    corr,
-    annot={annot_hm},
-    fmt=".2f",
-    cmap="vlag",
-    center={center_value},
-    square=True,
-    linewidths=1,
-    cbar_kws={{"shrink": 0.8}},
-    ax=ax,
-)
-ax.set_title("Correlation heatmap")
-plt.show()"""
-                description = "Matrix pattern: scan many pairwise relationships at once."
-
-            # ------- Multi-variable (pairplot) -------
-            elif family == "Multi-variable" and multi_vars:
-                sample_size = min(sample_n, len(df))
-                cols_to_use = multi_vars + ([hue_multi] if hue_multi else [])
-                df_sample = df[cols_to_use].dropna().sample(sample_size, random_state=42)
-
-                with st.spinner("Building pairplot..."):
-                    g = sns.pairplot(
-                        df_sample,
-                        vars=multi_vars,
-                        hue=hue_multi,
-                        corner=True,
-                        diag_kind="kde",
-                        plot_kws={"alpha": 0.6},
-                        diag_kws={"alpha": 0.7},
-                    )
-                    g.fig.suptitle("Pairplot", y=1.01, fontweight="bold")
-                    fig_seaborn = g.fig
-                    apply_dark(fig_seaborn, DARK)
-                    st.pyplot(fig_seaborn)
-
-                code_str = f"""sample = df[{multi_vars + ([hue_multi] if hue_multi else [])}].dropna().sample({sample_n}, random_state=42)
-g = sns.pairplot(
-    sample,
-    vars={multi_vars},
-    hue={repr(hue_multi)},
-    corner=True,
-    diag_kind="kde",
-    plot_kws={{"alpha": 0.6}},
-)
-g.fig.suptitle("Pairplot", y=1.01)
-plt.show()"""
-                description = "Multi-variable view: every pair of variables in one grid."
-
-            st.markdown("</div>", unsafe_allow_html=True)
-
-        st.markdown("### Code preview")
-        if code_str:
-            show_code_example(code_str, description)
-
-        if "fig_seaborn" in locals() and fig_seaborn is not None:
-            if st.button("Save last Seaborn plot to gallery", key="sb_save_gallery"):
-                save_to_gallery(fig_seaborn, f"Seaborn: {family}", "Seaborn builder plot")
-                st.success("Saved to gallery.")
-
-# ==================== TAB: MATPLOTLIB BUILDER ====================
-with tab_mpl:
-    st.markdown("## Matplotlib builder")
-    st.markdown(
-        '<div class="info-box"><strong>Goal:</strong> Build Matplotlib plots with fine-grained control on axes and layouts.</div>',
-        unsafe_allow_html=True,
-    )
-
-    if df.empty:
-        st.warning("No data loaded.")
-    else:
-        col_plot, col_ctrl = st.columns([7, 3])
-
-        with col_ctrl:
-            st.markdown(
-                """
-<div class="control-panel">
-  <div class="control-panel-header">PLOT SETUP</div>
-</div>
-""",
-                unsafe_allow_html=True,
-            )
-
-            mpl_type = st.selectbox(
-                "Plot type",
-                [
-                    "Line",
-                    "Scatter",
-                    "Bar",
-                    "Histogram",
-                    "Box",
-                    "Subplots overview",
-                ],
-                key="mpl_type",
-            )
-
-            code_mpl = ""
-            fig_mpl = None
-
-            if mpl_type == "Line":
-                x_line = st.selectbox(
-                    "X (numeric or index)",
-                    ["index"] + numeric_cols_all,
-                    key="mpl_line_x",
-                )
-                y_line = st.selectbox(
-                    "Y (numeric)",
-                    numeric_cols_all,
-                    key="mpl_line_y",
-                )
-                marker = st.selectbox(
-                    "Marker",
-                    ["o", "s", "None"],
-                    index=0,
-                    key="mpl_line_marker",
-                )
-                use_grid = st.checkbox(
-                    "Show grid",
-                    value=True,
-                    key="mpl_line_grid",
-                )
-
-            elif mpl_type == "Scatter":
-                if len(numeric_cols_all) < 2:
-                    st.error("Need at least two numeric columns for scatter.")
-                x_sc = st.selectbox(
-                    "X (numeric)",
-                    numeric_cols_all,
-                    key="mpl_sc_x",
-                )
-                y_sc = st.selectbox(
-                    "Y (numeric)",
-                    [c for c in numeric_cols_all if c != x_sc],
-                    key="mpl_sc_y",
-                )
-                color_by = None
-                if categorical_cols_all:
-                    use_color = st.checkbox(
-                        "Color by category",
-                        value=False,
-                        key="mpl_sc_use_color",
-                    )
-                    if use_color:
-                        color_by = st.selectbox(
-                            "Category",
-                            categorical_cols_all,
-                            key="mpl_sc_color_by",
-                        )
-                alpha_sc = st.slider(
-                    "Point transparency",
-                    0.1,
-                    1.0,
-                    0.7,
-                    0.05,
-                    key="mpl_sc_alpha",
-                )
-                size_sc = st.slider(
-                    "Point size",
-                    20,
-                    200,
-                    70,
-                    key="mpl_sc_size",
-                )
-
-            elif mpl_type == "Bar":
-                cat_for_bar = None
-                if categorical_cols_all:
-                    cat_for_bar = st.selectbox(
-                        "Category",
-                        categorical_cols_all,
-                        key="mpl_bar_cat",
-                    )
-                else:
-                    st.error("Need a categorical column for bar plot.")
-                num_for_bar = st.selectbox(
-                    "Value",
-                    numeric_cols_all,
-                    key="mpl_bar_num",
-                )
-                agg_bar = st.selectbox(
-                    "Aggregation",
-                    ["mean", "sum", "count"],
-                    key="mpl_bar_agg",
-                )
-                horiz = st.checkbox(
-                    "Horizontal bars",
-                    value=True,
-                    key="mpl_bar_horiz",
-                )
-
-            elif mpl_type == "Histogram":
-                num_hist = st.selectbox(
-                    "Numeric column",
-                    numeric_cols_all,
-                    key="mpl_hist_num",
-                )
-                bins_hist = st.slider(
-                    "Bins",
-                    5,
-                    80,
-                    30,
-                    key="mpl_hist_bins",
-                )
-                density_hist = st.checkbox(
-                    "Show density instead of counts",
-                    value=False,
-                    key="mpl_hist_density",
-                )
-
-            elif mpl_type == "Box":
-                nums_box = st.multiselect(
-                    "Numeric columns",
-                    numeric_cols_all,
-                    default=numeric_cols_all[: min(4, len(numeric_cols_all))],
-                    key="mpl_box_nums",
-                )
-
-            else:  # Subplots overview
-                nums_over = st.multiselect(
-                    "Numeric columns",
-                    numeric_cols_all,
-                    default=numeric_cols_all[: min(3, len(numeric_cols_all))],
-                    key="mpl_over_nums",
-                )
-                use_kde = st.checkbox(
-                    "Overlay KDE on histograms",
-                    value=True,
-                    key="mpl_over_kde",
-                )
-
-        with col_plot:
-            st.markdown('<div class="plot-container">', unsafe_allow_html=True)
-
-            if mpl_type == "Line":
-                if not numeric_cols_all:
-                    st.error("No numeric columns for line plot.")
-                else:
-                    if x_line == "index":
-                        x_vals = np.arange(len(df))
-                        x_label = "Index"
-                    else:
-                        x_vals = df[x_line].values
-                        x_label = x_line
-                    y_vals = df[y_line].values
-
-                    fig_mpl, ax = plt.subplots(figsize=(10, 5))
-                    line_marker = None if marker == "None" else marker
-                    ax.plot(x_vals, y_vals, marker=line_marker, lw=2)
-                    ax.set_title(f"Line: {y_line} over {x_label}", fontsize=13, fontweight="bold")
-                    ax.set_xlabel(x_label)
-                    ax.set_ylabel(y_line)
-                    if use_grid:
-                        ax.grid(alpha=0.3)
-                    apply_dark(fig_mpl, DARK)
-                    st.pyplot(fig_mpl)
-
-                    code_mpl = f"""fig, ax = plt.subplots(figsize=(10, 5))
-ax.plot(
-    { 'np.arange(len(df))' if x_line == "index" else f'df["{x_line}"]' },
-    df["{y_line}"],
-    marker={'None' if marker == "None" else repr(marker)},
-    lw=2,
-)
-ax.set_title("Line: {y_line} over {x_label}")
-ax.set_xlabel("{x_label}")
-ax.set_ylabel("{y_line}")
-ax.grid(alpha=0.3)
-plt.show()"""
-
-            elif mpl_type == "Scatter":
-                if len(numeric_cols_all) < 2:
-                    st.error("No numeric columns for scatter plot.")
-                else:
-                    fig_mpl, ax = plt.subplots(figsize=(10, 5))
-                    if color_by:
-                        unique_vals = df[color_by].dropna().unique()
-                        cmap = plt.get_cmap("tab10")
-                        for idx, val in enumerate(unique_vals):
-                            mask = df[color_by] == val
-                            ax.scatter(
-                                df.loc[mask, x_sc],
-                                df.loc[mask, y_sc],
-                                alpha=alpha_sc,
-                                s=size_sc,
-                                label=str(val),
-                                color=cmap(idx % 10),
-                            )
-                        ax.legend(title=color_by)
-                    else:
-                        ax.scatter(
-                            df[x_sc],
-                            df[y_sc],
-                            alpha=alpha_sc,
-                            s=size_sc,
-                        )
-                    ax.set_title(f"Scatter: {y_sc} vs {x_sc}", fontsize=13, fontweight="bold")
-                    ax.set_xlabel(x_sc)
-                    ax.set_ylabel(y_sc)
-                    ax.grid(alpha=0.3)
-                    apply_dark(fig_mpl, DARK)
-                    st.pyplot(fig_mpl)
-
-                    code_mpl = f"""fig, ax = plt.subplots(figsize=(10, 5))
-ax.scatter(
-    df["{x_sc}"],
-    df["{y_sc}"],
-    alpha={alpha_sc},
-    s={size_sc},
-)
-ax.set_title("Scatter: {y_sc} vs {x_sc}")
-ax.set_xlabel("{x_sc}")
-ax.set_ylabel("{y_sc}")
-ax.grid(alpha=0.3)
-plt.show()"""
-
-            elif mpl_type == "Bar":
-                if cat_for_bar is None:
-                    st.error("Select a categorical column for the bar plot.")
-                else:
-                    grouped = getattr(df.groupby(cat_for_bar)[num_for_bar], agg_bar)()
-                    grouped = grouped.sort_values(ascending=True)
-                    fig_mpl, ax = plt.subplots(figsize=(9, 5))
-                    if horiz:
-                        ax.barh(grouped.index, grouped.values)
-                        ax.set_xlabel(num_for_bar)
-                        ax.set_ylabel(cat_for_bar)
-                    else:
-                        ax.bar(grouped.index, grouped.values)
-                        ax.set_ylabel(num_for_bar)
-                        ax.set_xlabel(cat_for_bar)
-                        plt.setp(ax.get_xticklabels(), rotation=45, ha="right")
-                    ax.set_title(f"{agg_bar} of {num_for_bar} by {cat_for_bar}", fontsize=13, fontweight="bold")
-                    ax.grid(axis="x" if horiz else "y", alpha=0.3)
-                    apply_dark(fig_mpl, DARK)
-                    st.pyplot(fig_mpl)
-
-                    code_mpl = f"""grouped = df.groupby("{cat_for_bar}")["{num_for_bar}"].{agg_bar}().sort_values()
-fig, ax = plt.subplots(figsize=(9, 5))
-ax.barh(grouped.index, grouped.values) if {horiz} else ax.bar(grouped.index, grouped.values)
-ax.set_title("{agg_bar} of {num_for_bar} by {cat_for_bar}")
-plt.show()"""
-
-            elif mpl_type == "Histogram":
-                fig_mpl, ax = plt.subplots(figsize=(9, 5))
-                ax.hist(
-                    df[num_hist].dropna().values,
-                    bins=bins_hist,
-                    density=density_hist,
-                    alpha=0.85,
-                )
-                ax.set_title(f"Histogram of {num_hist}", fontsize=13, fontweight="bold")
-                ax.set_xlabel(num_hist)
-                ax.set_ylabel("Density" if density_hist else "Count")
-                ax.grid(alpha=0.3)
-                apply_dark(fig_mpl, DARK)
-                st.pyplot(fig_mpl)
-
-                code_mpl = f"""fig, ax = plt.subplots(figsize=(9, 5))
-ax.hist(
-    df["{num_hist}"].dropna().values,
-    bins={bins_hist},
-    density={density_hist},
-    alpha=0.85,
-)
-ax.set_title("Histogram of {num_hist}")
-ax.set_xlabel("{num_hist}")
-ax.set_ylabel("{'Density' if density_hist else 'Count'}")
-ax.grid(alpha=0.3)
-plt.show()"""
-
-            elif mpl_type == "Box":
-                if not nums_box:
-                    st.warning("Select at least one numeric column.")
-                else:
-                    fig_mpl, ax = plt.subplots(figsize=(10, 5))
-                    ax.boxplot(
-                        [df[c].dropna().values for c in nums_box],
-                        labels=nums_box,
-                        vert=True,
-                    )
-                    ax.set_title("Box plots", fontsize=13, fontweight="bold")
-                    ax.grid(alpha=0.3)
-                    apply_dark(fig_mpl, DARK)
-                    st.pyplot(fig_mpl)
-
-                    code_mpl = f"""fig, ax = plt.subplots(figsize=(10, 5))
-ax.boxplot(
-    [{', '.join([f'df["{c}"].dropna().values' for c in nums_box])}],
-    labels={nums_box},
-)
-ax.set_title("Box plots")
-ax.grid(alpha=0.3)
-plt.show()"""
-
-            else:  # Subplots overview
-                if not nums_over:
-                    st.warning("Select at least one numeric column.")
-                else:
-                    k = len(nums_over)
-                    fig_mpl, axes = plt.subplots(
-                        1,
-                        k,
-                        figsize=(4 * k, 4),
-                        squeeze=False,
-                    )
-                    for idx, col_name in enumerate(nums_over):
-                        ax = axes[0, idx]
-                        data = df[col_name].dropna().values
-                        ax.hist(data, bins=30, alpha=0.8, density=True)
-                        if use_kde and len(data) > 10:
-                            x_vals = np.linspace(data.min(), data.max(), 200)
-                            kde = stats.gaussian_kde(data)
-                            ax.plot(x_vals, kde(x_vals), lw=2)
-                        ax.set_title(col_name)
-                        ax.grid(alpha=0.3)
-                    fig_mpl.suptitle("Numeric overview", fontsize=13, fontweight="bold")
-                    plt.tight_layout()
-                    apply_dark(fig_mpl, DARK)
-                    st.pyplot(fig_mpl)
-
-                    code_mpl = """cols = {cols}
-fig, axes = plt.subplots(1, len(cols), figsize=(4 * len(cols), 4), squeeze=False)
-for idx, name in enumerate(cols):
-    ax = axes[0, idx]
-    data = df[name].dropna().values
-    ax.hist(data, bins=30, density=True, alpha=0.8)
-    ax.set_title(name)
-    ax.grid(alpha=0.3)
-plt.tight_layout()
-plt.show()""".format(
-                        cols=nums_over
-                    )
-
-            st.markdown("</div>", unsafe_allow_html=True)
-
-        st.markdown("### Code preview")
-        if code_mpl:
-            show_code_example(code_mpl, "Matplotlib commands that reproduce the current plot.")
-
-        if fig_mpl is not None:
-            if st.button("Save last Matplotlib plot to gallery", key="mpl_save_gallery"):
-                save_to_gallery(fig_mpl, f"Matplotlib: {mpl_type}", "Matplotlib builder plot")
-                st.success("Saved to gallery.")
-
-# ==================== TAB: COMPARE ====================
-with tab_compare:
-    st.markdown("## Compare Seaborn and Matplotlib")
-    st.markdown(
-        '<div class="info-box"><strong>Goal:</strong> See the same idea expressed once with Seaborn and once with Matplotlib.</div>',
-        unsafe_allow_html=True,
-    )
-
-    if df.empty or not numeric_cols_all:
-        st.warning("Need at least one numeric column in the dataset.")
-    else:
-        compare_kind = st.selectbox(
-            "Comparison pattern",
-            [
-                "Distribution (histogram + KDE)",
-                "Relationship (scatter)",
-            ],
-            key="cmp_kind",
-        )
-
-        if compare_kind == "Distribution (histogram + KDE)":
-            num_cmp = st.selectbox(
-                "Numeric column",
-                numeric_cols_all,
-                key="cmp_dist_num",
-            )
-            hue_cmp = None
-            if categorical_cols_all:
-                use_hue_cmp = st.checkbox(
-                    "Color by category (Seaborn only)",
-                    value=False,
-                    key="cmp_dist_use_hue",
-                )
-                if use_hue_cmp:
-                    hue_cmp = st.selectbox(
-                        "Hue",
-                        categorical_cols_all,
-                        key="cmp_dist_hue",
-                    )
-
-            col_s, col_m = st.columns(2)
-
-            with col_s:
-                st.markdown("### Seaborn view")
-                fig_s, ax_s = plt.subplots(figsize=(7, 4))
-                sns.histplot(
-                    data=df,
-                    x=num_cmp,
-                    hue=hue_cmp,
-                    kde=True,
-                    bins=30,
-                    ax=ax_s,
-                )
-                ax_s.set_title("Seaborn: histogram + KDE", fontsize=12, fontweight="bold")
-                apply_dark(fig_s, DARK)
-                st.pyplot(fig_s)
-
-            with col_m:
-                st.markdown("### Matplotlib view")
-                fig_m, ax_m = plt.subplots(figsize=(7, 4))
-                values = df[num_cmp].dropna().values
-                ax_m.hist(values, bins=30, alpha=0.85, density=True)
-                x_vals = np.linspace(values.min(), values.max(), 200)
-                kde = stats.gaussian_kde(values)
-                ax_m.plot(x_vals, kde(x_vals), lw=2)
-                ax_m.set_title("Matplotlib: histogram + KDE", fontsize=12, fontweight="bold")
-                ax_m.set_xlabel(num_cmp)
-                ax_m.set_ylabel("Density")
-                ax_m.grid(alpha=0.3)
-                apply_dark(fig_m, DARK)
-                st.pyplot(fig_m)
-
-            if st.button("Save Seaborn comparison plot to gallery", key="cmp_dist_save"):
-                save_to_gallery(fig_s, "Compare: Distribution", "Seaborn vs Matplotlib distribution")
-                st.success("Saved Seaborn figure to gallery.")
-
-        else:  # Relationship (scatter)
-            if len(numeric_cols_all) < 2:
-                st.warning("Need at least two numeric columns.")
-            else:
-                x_cmp = st.selectbox(
-                    "X",
-                    numeric_cols_all,
-                    key="cmp_rel_x",
-                )
-                y_cmp = st.selectbox(
-                    "Y",
-                    [c for c in numeric_cols_all if c != x_cmp],
-                    key="cmp_rel_y",
-                )
-                hue_cmp_rel = None
-                if categorical_cols_all:
-                    use_hue_cmp_rel = st.checkbox(
-                        "Color by category (Seaborn only)",
-                        value=False,
-                        key="cmp_rel_use_hue",
-                    )
-                    if use_hue_cmp_rel:
-                        hue_cmp_rel = st.selectbox(
-                            "Hue",
-                            categorical_cols_all,
-                            key="cmp_rel_hue",
-                        )
-
-                col_s2, col_m2 = st.columns(2)
-
-                with col_s2:
-                    st.markdown("### Seaborn view")
-                    fig_s2, ax_s2 = plt.subplots(figsize=(7, 4))
-                    sns.scatterplot(
-                        data=df,
-                        x=x_cmp,
-                        y=y_cmp,
-                        hue=hue_cmp_rel,
-                        alpha=0.7,
-                        s=70,
-                        ax=ax_s2,
-                    )
-                    ax_s2.set_title("Seaborn: scatterplot", fontsize=12, fontweight="bold")
-                    apply_dark(fig_s2, DARK)
-                    st.pyplot(fig_s2)
-
-                with col_m2:
-                    st.markdown("### Matplotlib view")
-                    fig_m2, ax_m2 = plt.subplots(figsize=(7, 4))
-                    ax_m2.scatter(df[x_cmp], df[y_cmp], alpha=0.7)
-                    ax_m2.set_title("Matplotlib: scatter", fontsize=12, fontweight="bold")
-                    ax_m2.set_xlabel(x_cmp)
-                    ax_m2.set_ylabel(y_cmp)
-                    ax_m2.grid(alpha=0.3)
-                    apply_dark(fig_m2, DARK)
-                    st.pyplot(fig_m2)
-
-                if st.button("Save Seaborn comparison plot to gallery", key="cmp_rel_save"):
-                    save_to_gallery(fig_s2, "Compare: Relationship", "Seaborn vs Matplotlib scatter")
-                    st.success("Saved Seaborn figure to gallery.")
-
-# ==================== TAB: GALLERY ====================
-with tab_gallery:
-    st.markdown("## Gallery")
-
-    if not st.session_state["gallery"]:
-        st.info("Gallery is empty. Build a plot in any tab and save it here.")
-        st.markdown(
-            """
-**How this gallery works**
-
-1. Create a visualization in one of the tabs
-2. Click the **Save to gallery** button
-3. Return here to review the saved visuals
-4. Download individual PNG files or a ZIP archive
-"""
-        )
-    else:
-        st.success(f"{len(st.session_state['gallery'])} visualizations stored.")
-
-        col_zip, col_clear, _ = st.columns([2, 2, 1])
-
-        with col_zip:
-            if st.button("Prepare ZIP archive", key="gal_zip_btn", use_container_width=True):
-                zip_buf = io.BytesIO()
-                with zipfile.ZipFile(zip_buf, "w", zipfile.ZIP_DEFLATED) as zf:
-                    for idx, item in enumerate(st.session_state["gallery"]):
-                        filename = f"{idx+1:02d}_{item['name'].replace(' ', '_')}.png"
-                        zf.writestr(filename, item["image"])
-
-                st.download_button(
-                    "Download ZIP",
-                    data=zip_buf.getvalue(),
-                    file_name=f"visual_lab_gallery_{datetime.now():%Y%m%d_%H%M%S}.zip",
-                    mime="application/zip",
-                    use_container_width=True,
-                    key="gal_zip_dl",
-                )
-
-        with col_clear:
-            if st.button("Clear gallery", key="gal_clear_btn", use_container_width=True):
-                st.session_state["gallery"] = []
-                st.rerun()
-
-        st.markdown("---")
-
-        cols_per_row = 2
-        for i in range(0, len(st.session_state["gallery"]), cols_per_row):
-            cols = st.columns(cols_per_row)
-            for j, c in enumerate(cols):
-                item_idx = i + j
-                if item_idx < len(st.session_state["gallery"]):
-                    item = st.session_state["gallery"][item_idx]
-                    with c:
-                        st.markdown('<div class="plot-container">', unsafe_allow_html=True)
-                        st.image(item["image"], use_container_width=True)
-                        st.markdown(f"**{item['name']}**")
-                        st.caption(item["description"])
-                        st.caption(
-                            f"Saved at {item['timestamp'].strftime('%Y-%m-%d %H:%M')}"
-                        )
-                        st.download_button(
-                            "Download PNG",
-                            data=item["image"],
-                            file_name=f"{item['name'].replace(' ', '_')}.png",
-                            mime="image/png",
-                            key=f"gal_dl_{item_idx}",
-                            use_container_width=True,
-                        )
-                        st.markdown("</div>", unsafe_allow_html=True)
-
-# ==================== FOOTER ====================
-st.markdown("---")
-st.markdown("### Quick reference")
-
-col_f1, col_f2, col_f3 = st.columns(3)
-with col_f1:
-    st.markdown(
-        """
-**Distribution**
-- Histogram / KDE / ECDF
-- Box / Violin
-"""
-    )
-with col_f2:
-    st.markdown(
-        """
-**Relationships & groups**
-- Scatter / Regression / Line
-- Category summaries
-"""
-    )
-with col_f3:
-    st.markdown(
-        """
-**Matrix & multi-view**
-- Correlation heatmaps
-- Pairplot grids
-"""
-    )