"""Validate synthetic herbal & traditional medicine safety dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "traditional_healer_practice": "herbal_traditional_healer.csv",
    "herbal_retail_market": "herbal_retail_market.csv",
    "hospital_herb_drug_interaction": "herbal_hospital_interaction.csv",
}

COLORS = {"traditional_healer_practice": "#e6550d", "herbal_retail_market": "#756bb1", "hospital_herb_drug_interaction": "#31a354"}


def load_data() -> pd.DataFrame:
    frames = []
    for scenario, filename in SCENARIO_FILES.items():
        df = pd.read_csv(Path("data") / filename)
        frames.append(df)
    return pd.concat(frames, ignore_index=True)


def plot_validation(df: pd.DataFrame, output_path: Path) -> None:
    fig, axes = plt.subplots(4, 2, figsize=(14, 16))
    axes = axes.flatten()

    cont_cols = ["heavy_metal_contaminated", "microbial_contaminated", "adulterated", "pesticide_residue"]
    cont = df.groupby("scenario")[cont_cols].mean() * 100
    cont.plot(kind="bar", ax=axes[0])
    axes[0].set_title("Contamination & Adulteration (%)")
    axes[0].legend(fontsize=6)

    health_cols = ["hepatotoxicity", "nephrotoxicity", "gi_adverse", "hospitalisation", "death"]
    health = df.groupby("scenario")[health_cols].mean() * 100
    health.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Health Outcomes (%)")
    axes[1].legend(fontsize=6)

    prod = df.groupby(["scenario", "product_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    prod.unstack().plot(kind="bar", stacked=True, ax=axes[2])
    axes[2].set_title("Product Type Distribution")
    axes[2].legend(fontsize=5)

    ind = df.groupby(["scenario", "indication"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    ind.unstack().plot(kind="bar", stacked=True, ax=axes[3])
    axes[3].set_title("Indication Distribution")
    axes[3].legend(fontsize=4)

    int_cols = ["concurrent_conventional", "disclosed_to_doctor", "interaction_risk"]
    intc = df.groupby("scenario")[int_cols].mean() * 100
    intc.plot(kind="bar", ax=axes[4])
    axes[4].set_title("Herb-Drug Interaction Risk (%)")
    axes[4].legend(fontsize=7)

    hm_cols = ["lead_detected", "mercury_detected"]
    hm = df.groupby("scenario")[hm_cols].mean() * 100
    hm.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Heavy Metal Detection (%)")
    axes[5].legend(fontsize=7)

    reg_cols = ["registered_product", "label_present", "quality_tested"]
    reg = df.groupby("scenario")[reg_cols].mean() * 100
    reg.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Regulation & Quality (%)")
    axes[6].legend(fontsize=7)

    adul_cols = ["synthetic_drug_added"]
    adul = df.groupby("scenario")[adul_cols].mean() * 100
    adul.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Synthetic Drug Adulteration (%)")
    axes[7].legend(fontsize=7)

    plt.tight_layout()
    fig.savefig(output_path, dpi=200)
    plt.close(fig)


def main() -> None:
    df = load_data()
    plot_validation(df, Path("validation_report.png"))
    print("Saved validation_report.png")


if __name__ == "__main__":
    main()