# /// script
# dependencies = [
#     "diskcache==5.6.3",
#     "duckdb==1.4.4",
#     "marimo",
#     "matplotlib==3.10.8",
#     "numpy",
#     "openai",
#     "pandas==3.0.0",
#     "polars[pyarrow]==1.38.1",
#     "pydantic-ai==1.59.0",
#     "scikit-learn==1.8.0",
#     "sqlglot==28.10.1",
#     "tqdm",
# ]
# requires-python = ">=3.14"
# ///

import marimo

__generated_with = "0.20.1"
app = marimo.App()


@app.cell
def _():
    import duckdb
    import marimo as mo
    import matplotlib.pyplot as plt
    import numpy
    import polars as pl

    conn = duckdb.connect(database="bagaco.duckdb")
    return conn, mo, numpy, pl, plt


@app.cell
def _(bagaco, conn, mo):
    _df = mo.sql(
        f"""
    SELECT COUNT(*) AS total_docs FROM bagaco;
    """,
        engine=conn,
    )
    mo.vstack(items=[mo.md(text="### Total document count"), _df])
    return


@app.cell
def _(bagaco, conn, mo):
    _df = mo.sql(
        f"""
    SELECT
      regexp_replace(lower(regexp_extract(url, '^(?:https?://)?(?:[^@/\\n]+@)?([^:/?\\n]+)', 1)), '^www\\.', '') AS domain,
      COUNT(*) AS n_docs
    FROM bagaco
    WHERE url IS NOT NULL AND url <> ''
    GROUP BY 1
    ORDER BY n_docs DESC
    LIMIT 10;
    """,
        engine=conn,
    )
    mo.vstack(items=[mo.md(text="### Top 10 domains by document count"), _df])
    return


@app.cell
def _(bagaco, conn, mo):
    _df = mo.sql(
        f"""
    SELECT
        regexp_replace(lower(regexp_extract(url, '^(?:https?://)?(?:[^@/\\n]+@)?([^:/?\\n]+)', 1)), '^www\\.', '') AS domain,
        ROUND(AVG(educational_score), 2) AS avg_edu_score,
        COUNT(*) AS n_docs
    FROM bagaco
    WHERE url IS NOT NULL AND url <> '' AND educational_score IS NOT NULL
    GROUP BY 1
    HAVING COUNT(*) >= 100
    ORDER BY avg_edu_score DESC
    LIMIT 15;
    """,
        engine=conn,
    )
    mo.vstack(
        items=[
            mo.md(
                text="### Domains with highest avg educational score (min 100 docs)"
            ),
            _df,
        ]
    )
    return


@app.cell
def _(bagaco, conn, mo):
    _df = mo.sql(
        f"""
    SELECT
        regexp_replace(lower(regexp_extract(url, '^(?:https?://)?(?:[^@/\\n]+@)?([^:/?\\n]+)', 1)), '^www\\.', '') AS domain,
        ROUND(AVG(educational_score), 2) AS avg_edu_score,
        COUNT(*) AS n_docs
    FROM bagaco
    WHERE url IS NOT NULL AND url <> '' AND educational_score IS NOT NULL
    GROUP BY 1
    HAVING COUNT(*) >= 100
    ORDER BY avg_edu_score ASC
    LIMIT 15;
    """,
        engine=conn,
    )
    mo.vstack(
        items=[
            mo.md(
                text="### Domains with lowest avg educational score (min 100 docs)"
            ),
            _df,
        ]
    )
    return


@app.cell
def _(bagaco, conn, mo, plt):
    _domain_scatter_df = mo.sql(
        f"""
    WITH domain_stats AS (
        SELECT
            regexp_replace(lower(regexp_extract(url, '^(?:https?://)?(?:[^@/\\n]+@)?([^:/?\\n]+)', 1)), '^www\\.', '') AS domain,
            COUNT(*) AS total_docs,
            AVG(educational_score) AS avg_edu_score
        FROM bagaco
        WHERE url IS NOT NULL
          AND url <> ''
          AND educational_score IS NOT NULL
        GROUP BY 1
        HAVING COUNT(*) > 10
    ),
    domain_categories AS (
        SELECT
            domain,
            category,
            ROW_NUMBER() OVER (PARTITION BY domain ORDER BY n_docs DESC, category ASC) AS row_number
        FROM (
            SELECT
                regexp_replace(lower(regexp_extract(url, '^(?:https?://)?(?:[^@/\\n]+@)?([^:/?\\n]+)', 1)), '^www\\.', '') AS domain,
                category,
                COUNT(*) AS n_docs
            FROM bagaco
            WHERE url IS NOT NULL
              AND url <> ''
              AND category IS NOT NULL
            GROUP BY 1, 2
        )
    )
    SELECT
        domain_stats.domain,
        domain_stats.total_docs,
        ROUND(domain_stats.avg_edu_score, 3) AS avg_edu_score,
        COALESCE(domain_categories.category, 'Unknown') AS category
    FROM domain_stats
    LEFT JOIN domain_categories
        ON domain_stats.domain = domain_categories.domain
       AND domain_categories.row_number = 1
    ORDER BY domain_stats.total_docs DESC;
    """,
        engine=conn,
    )

    _categories = sorted(_domain_scatter_df["category"].unique().to_list())
    _colors = plt.cm.tab10.colors
    _category_colors = {
        _category: _colors[_index % len(_colors)]
        for _index, _category in enumerate(_categories)
    }

    _, _ax = plt.subplots(figsize=(12, 6))
    for _category in _categories:
        _subset = _domain_scatter_df.filter(
            _domain_scatter_df["category"] == _category
        )
        _ax.scatter(
            x=_subset["total_docs"].to_list(),
            y=_subset["avg_edu_score"].to_list(),
            s=16,
            alpha=0.65,
            color=_category_colors[_category],
            label=_category,
        )

    _ax.set_xscale(value="log")
    _ax.set_xlabel(xlabel="Total documents (log scale)")
    _ax.set_ylabel(ylabel="Average educational score")
    _ax.set_title(
        label="Domain Scatter: Total Documents (Log) vs Avg Educational Score",
        fontweight="bold",
    )
    _ax.grid(visible=True, axis="both", linestyle="-", linewidth=0.5, alpha=0.35)
    _ax.legend(loc="best", fontsize=8, ncols=2)
    plt.tight_layout()
    _ax
    return


@app.cell
def _(bagaco, conn, mo):
    _fast_total_words = mo.sql(
        f"""
    SELECT
        SUM(
            CASE
                WHEN text IS NULL OR text = '' THEN 0
                ELSE len(trim(text)) - len(replace(trim(text), ' ', '')) + 1
            END
        ) AS total_words_fast
    FROM bagaco;
    """,
        engine=conn,
    )
    mo.vstack(
        items=[
            mo.md(text="### Total words in dataset (fast computation)"),
            _fast_total_words,
        ]
    )
    return


@app.cell
def _(bagaco, conn, mo, plt):
    df_per_halfyear = mo.sql(
        f"""
    SELECT
        EXTRACT(YEAR FROM CAST(date AS TIMESTAMP)) || ' H' ||
        CASE WHEN EXTRACT(MONTH FROM CAST(date AS TIMESTAMP)) <= 6 THEN 1 ELSE 2 END AS year_half,
        COUNT(*) / 1000000.0 AS total_doc_count_millions,
        ROUND(AVG(educational_score), 3) AS avg_edu_score
    FROM bagaco
    WHERE date IS NOT NULL
    GROUP BY 1
    ORDER BY 1;
    """,
        engine=conn,
    )

    year_half = df_per_halfyear["year_half"].to_list()
    total_doc_count_millions = df_per_halfyear[
        "total_doc_count_millions"
    ].to_list()
    _avg_edu = df_per_halfyear["avg_edu_score"].to_list()
    _x_positions = list(range(len(year_half)))

    _fig, ax = plt.subplots(figsize=(12, 6))
    ax.bar(
        x=_x_positions,
        height=total_doc_count_millions,
        width=0.65,
        color="#449DE3",
        edgecolor="#4a4a4a",
        linewidth=0.6,
        alpha=0.85,
        label="Total Document Count (M)",
    )
    ax.set_xticks(ticks=_x_positions)
    ax.set_xticklabels(labels=year_half, rotation=45, ha="right")
    ax.set_xlabel(xlabel="Half-Year")
    ax.set_ylabel(ylabel="Total Document Count (Millions)")
    ax.grid(axis="y", linestyle="-", linewidth=0.5, alpha=0.35)

    _ax2 = ax.twinx()
    _ax2.plot(
        _x_positions,
        _avg_edu,
        color="#d73027",
        linewidth=2,
        marker="o",
        markersize=4,
        label="Avg Edu Score",
    )
    _ax2.set_ylabel(ylabel="Avg Educational Score", color="#d73027")
    _ax2.tick_params(axis="y", labelcolor="#d73027")

    _lines1, _labels1 = ax.get_legend_handles_labels()
    _lines2, _labels2 = _ax2.get_legend_handles_labels()
    ax.legend(
        handles=_lines1 + _lines2, labels=_labels1 + _labels2, loc="upper left"
    )
    ax.set_title(
        label="Document Volume & Avg Educational Score Over Time",
        fontweight="bold",
    )
    plt.tight_layout()
    _fig
    return


@app.cell
def _(bagaco, conn, mo, plt):
    df_word_count_distribution = mo.sql(
        f"""
    WITH wc AS (
        SELECT len(string_split(text, ' ')) AS word_count FROM bagaco WHERE text IS NOT NULL
    ), bucketed AS (
        SELECT
            CASE
                WHEN word_count <= 150 THEN 'Note (0-150)'
                WHEN word_count <= 600 THEN 'Short Article (151-600)'
                WHEN word_count <= 1500 THEN 'Standard Article (601-1500)'
                WHEN word_count <= 5000 THEN 'Longform (1501-5000)'
                WHEN word_count <= 20000 THEN 'Deep Report (5001-20000)'
                ELSE 'Book (20001+)'
            END AS length_band,
            COUNT(*) / 1000000.0 AS doc_count_millions
        FROM wc
        GROUP BY 1
    )
    SELECT
        CASE
            WHEN length_band = 'Note (0-150)' THEN 1
            WHEN length_band = 'Short Article (151-600)' THEN 2
            WHEN length_band = 'Standard Article (601-1500)' THEN 3
            WHEN length_band = 'Longform (1501-5000)' THEN 4
            WHEN length_band = 'Deep Report (5001-20000)' THEN 5
            ELSE 6
        END AS bucket_order,
        length_band,
        doc_count_millions
    FROM bucketed
    ORDER BY 1;
    """,
        engine=conn,
    )

    length_band_labels = df_word_count_distribution["length_band"].to_list()
    doc_count_millions = df_word_count_distribution["doc_count_millions"].to_list()
    y_positions = list(range(len(length_band_labels)))

    _, word_count_ax = plt.subplots(figsize=(12, 4.8))
    word_count_ax.barh(
        y=y_positions,
        width=doc_count_millions,
        color="#449DE3",
        edgecolor="#4a4a4a",
        linewidth=0.6,
        alpha=0.85,
        label="Total Document Count (M)",
    )
    word_count_ax.set_yticks(ticks=y_positions)
    word_count_ax.set_yticklabels(labels=length_band_labels)
    word_count_ax.set_xlabel(xlabel="Total Document Count (Millions)")
    word_count_ax.set_ylabel(ylabel="Word Count Band")
    word_count_ax.set_title(label="Word Count Distribution", fontweight="bold")
    word_count_ax.grid(
        visible=True, axis="x", linestyle="-", linewidth=0.5, alpha=0.35
    )
    word_count_ax.legend(loc="lower right")
    plt.tight_layout()
    word_count_ax
    return


@app.cell
def _(bagaco, conn, mo, numpy, pl, plt):
    _df_heatmap = mo.sql(
        f"""
    SELECT
        category,
        EXTRACT(YEAR FROM CAST(date AS TIMESTAMP)) || ' H' ||
        CASE WHEN EXTRACT(MONTH FROM CAST(date AS TIMESTAMP)) <= 6 THEN 1 ELSE 2 END AS year_half,
        ROUND(AVG(educational_score), 3) AS avg_edu_score
    FROM bagaco
    WHERE date IS NOT NULL AND category IS NOT NULL AND educational_score IS NOT NULL
    GROUP BY 1, 2
    ORDER BY 1, 2;
    """,
        engine=conn,
    )

    _categories = sorted(_df_heatmap["category"].unique().to_list())
    _periods = sorted(_df_heatmap["year_half"].unique().to_list())

    _grid = numpy.full(
        shape=(len(_categories), len(_periods)), fill_value=numpy.nan
    )
    for _i, _cat in enumerate(_categories):
        for _j, _period in enumerate(_periods):
            _match = _df_heatmap.filter(
                (pl.col("category") == _cat) & (pl.col("year_half") == _period)
            )
            if len(_match) > 0:
                _grid[_i, _j] = _match["avg_edu_score"][0]

    _fig, _ax = plt.subplots(figsize=(14, 6))
    _im = _ax.imshow(_grid, aspect="auto", cmap="RdYlGn", interpolation="nearest")
    plt.colorbar(mappable=_im, ax=_ax, label="Avg Educational Score")
    _ax.set_yticks(ticks=range(len(_categories)))
    _ax.set_yticklabels(labels=_categories)
    _ax.set_xticks(ticks=range(len(_periods)))
    _ax.set_xticklabels(labels=_periods, rotation=45, ha="right")
    _ax.set_title(
        label="Avg Educational Score by Category and Half-Year", fontweight="bold"
    )
    plt.tight_layout()
    _fig
    return


@app.cell
def _(bagaco, conn, mo):
    _category_summary_df = mo.sql(
        f"""
        SELECT
    category,
    COUNT(*) AS total_documents,
    ROUND(AVG(educational_score), 3) AS average_educational_score,
    ROUND(AVG(educational_score), 3) AS mean_educational_score,
    ROUND(
        SUM(CASE WHEN educational_score >= 3 THEN 1 ELSE 0 END) * 100.0 / COUNT(*),
        2
    ) AS pct_high_educational_score_documents,
    COUNT(
        DISTINCT regexp_replace(
            lower(regexp_extract(url, '^(?:https?://)?(?:[^@/\\n]+@)?([^:/?\\n]+)', 1)),
            '^www\\.',
            ''
        )
    ) AS total_different_domains
        FROM bagaco
        WHERE category IS NOT NULL
          AND educational_score IS NOT NULL
        GROUP BY 1
        ORDER BY total_documents DESC;
        """,
        engine=conn,
    )
    mo.vstack(items=[mo.md(text="### Category summary"), _category_summary_df])
    return


@app.cell
def _():
    return


if __name__ == "__main__":
    app.run()