#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Online Pharmacy & E-Pharmacy Regulation Dataset."""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os

SCENARIOS = ['licensed_e_pharmacy', 'social_media_marketplace', 'rogue_website_darknet']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'epharmacy_{sc}.csv')
        if os.path.exists(path):
            dfs[sc] = pd.read_csv(path)
    return dfs


def make_report(dfs, output='validation_report.png'):
    fig, axes = plt.subplots(4, 2, figsize=(16, 24))
    fig.suptitle(
        'Online Pharmacy & E-Pharmacy Regulation — Validation Report\n'
        '(Licensed E-Pharmacy → Social Media → Rogue/Darknet)',
        fontsize=15, fontweight='bold', y=0.99)
    colors = ['#2ecc71', '#f39c12', '#e74c3c']
    x = np.arange(len(SCENARIOS))
    labels = ['Licensed', 'Social Media', 'Rogue/Darknet']

    ax = axes[0, 0]
    sf = [dfs[sc]['quality_test_result'].eq('fail').mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, sf, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(sf):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('SF Rate (%)'); ax.set_title('SF Rate by Platform Type')

    ax = axes[0, 1]
    lic = [dfs[sc]['seller_licensed'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, lic, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(lic):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('Seller Licensed')

    ax = axes[1, 0]
    df = dfs.get('social_media_marketplace', list(dfs.values())[1])
    plat = df.groupby('platform_name')['quality_test_result'].apply(
        lambda x: (x == 'fail').mean()*100).sort_values()
    ax.barh(range(len(plat)), plat.values, color='#e74c3c', alpha=0.7)
    ax.set_yticks(range(len(plat)))
    ax.set_yticklabels([s.replace('_', ' ').title() for s in plat.index], fontsize=7)
    ax.set_xlabel('SF Rate (%)'); ax.set_title('SF by Platform (Social Media)')

    ax = axes[1, 1]
    cat = df.groupby('product_category')['quality_test_result'].apply(
        lambda x: (x == 'fail').mean()*100).sort_values()
    ax.barh(range(len(cat)), cat.values, color='#9b59b6', alpha=0.7)
    ax.set_yticks(range(len(cat)))
    ax.set_yticklabels([s.replace('_', ' ').title() for s in cat.index], fontsize=8)
    ax.set_xlabel('SF Rate (%)'); ax.set_title('SF by Product Category')

    ax = axes[2, 0]
    norx = [(1-dfs[sc]['prescription_verified'].mean())*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, norx, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(norx):
        ax.text(i, v+0.5, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('No Prescription Verified')

    ax = axes[2, 1]
    price = df['price_vs_reference'].values
    ax.hist(price, bins=30, color='#3498db', alpha=0.7, edgecolor='white')
    ax.axvline(x=1.0, color='red', linestyle='--', label='Reference price')
    ax.set_xlabel('Price vs Reference'); ax.set_title('Price Distribution (Social Media)')
    ax.legend(fontsize=8)

    ax = axes[3, 0]
    w = 0.35
    fals = [dfs[sc]['sf_classification'].eq('falsified').mean()*100 for sc in SCENARIOS if sc in dfs]
    subs = [dfs[sc]['sf_classification'].eq('substandard').mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x - w/2, subs, w, label='Substandard', color='#f39c12', alpha=0.8)
    ax.bar(x + w/2, fals, w, label='Falsified', color='#e74c3c', alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    ax.set_ylabel('Rate (%)'); ax.set_title('Substandard vs Falsified'); ax.legend(fontsize=8)

    ax = axes[3, 1]
    comp = [dfs[sc]['consumer_complaint_filed'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, comp, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(comp):
        ax.text(i, v+0.3, f'{v:.1f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('Consumer Complaints Filed')

    plt.tight_layout(rect=[0, 0, 1, 0.97])
    plt.savefig(output, dpi=150, bbox_inches='tight')
    print(f'Saved validation report to {output}')
    plt.close()


if __name__ == '__main__':
    dfs = load_scenarios()
    if dfs:
        make_report(dfs)