<?php
session_start();

require_once __DIR__ . '/includes/translations.php';
require_once __DIR__ . '/db/config.php';
require_once __DIR__ . '/ai/LocalAIApi.php';

// Set language from session or default to English
$lang = $_SESSION['lang'] ?? 'en';

// --- Helper Functions ---

function translate_to_english($text, $source_lang) {
    if ($source_lang === 'en') {
        return $text;
    }
    // In a real app, you would use a translation API.
    // For this demo, we'll simulate by calling our AI with a translation prompt.
    $prompt = "Translate the following text from '{$source_lang}' to English. Only return the translated text, with no extra commentary:

{$text}";
    $response = LocalAIApi::createResponse([
        'input' => [
            ['role' => 'system', 'content' => 'You are a translation assistant.'],
            ['role' => 'user', 'content' => $prompt],
        ],
    ]);

    if (!empty($response['success'])) {
        $decoded = LocalAIApi::decodeJsonFromResponse($response);
        return $decoded['choices'][0]['message']['content'] ?? $text;
    }
    return $text; // Fallback to original text on error
}

function translate_from_english($text, $target_lang) {
    if ($target_lang === 'en') {
        return $text;
    }
    // Simulate translation
    $prompt = "Translate the following text from English to '{$target_lang}'. Only return the translated text, with no extra commentary:

{$text}";
    $response = LocalAIApi::createResponse([
        'input' => [
            ['role' => 'system', 'content' => 'You are a translation assistant.'],
            ['role' => 'user', 'content' => $prompt],
        ],
    ]);

    if (!empty($response['success'])) {
        $decoded = LocalAIApi::decodeJsonFromResponse($response);
        return $decoded['choices'][0]['message']['content'] ?? $text;
    }
    return $text; // Fallback to original text on error
}

// --- Main Logic ---

$result_html = '';
$error_message = '';

if ($_SERVER['REQUEST_METHOD'] === 'POST') {
    // --- 1. Collect and Sanitize Inputs ---
    $state = htmlspecialchars($_POST['state'] ?? '');
    $size = htmlspecialchars($_POST['size'] ?? '');
    $material = htmlspecialchars($_POST['material'] ?? '');
    $brand = htmlspecialchars($_POST['brand'] ?? '');
    $length = htmlspecialchars($_POST['length'] ?? '');
    $chest_width = htmlspecialchars($_POST['chest_width'] ?? '');
    $waist_width = htmlspecialchars($_POST['waist_width'] ?? '');
    $hips_width = htmlspecialchars($_POST['hips_width'] ?? '');
    $additional_info = htmlspecialchars($_POST['additional_info'] ?? '');

    // --- 2. Validation ---
    if (empty($state) || empty($size)) {
        $_SESSION['generation_error'] = t('error_required_fields');
        header('Location: index.php#generator');
        exit();
    }

    // --- 3. Translation Pipeline (Input) ---
    $state_en = translate_to_english($state, $lang);
    $size_en = translate_to_english($size, $lang);
    $material_en = translate_to_english($material, $lang);
    $brand_en = translate_to_english($brand, $lang);
    $additional_info_en = translate_to_english($additional_info, $lang);

    // --- 4. Construct AI Prompt ---
    $prompt_parts = [
        "Generate a product listing for an online clothing store. The target audience is women aged 22-60.",
        "The output must be a JSON object with the following keys: 'title', 'short_description', 'description', 'measurements', 'hashtags'.",
        "The tone should be engaging, concise, and SEO-aware, incorporating trending keywords naturally.",
        "Do not add any extra commentary or editorializing. Stick to the facts provided."
    ];

    if ($state_en === 'used') {
        $prompt_parts[] = "Condition: Used (perfect condition).";
    } elseif ($state_en === 'visibly_used') {
        $prompt_parts[] = "Condition: Visibly used (may have some signs of use).";
    } else {
        $prompt_parts[] = "Condition: New.";
    }

    $prompt_parts[] = "Size: {$size_en}";
    if (!empty($material_en)) $prompt_parts[] = "Material: {$material_en}";
    if (!empty($brand_en)) $prompt_parts[] = "Brand: {$brand_en}";

    $measurements_parts = [];
    if (!empty($length)) $measurements_parts[] = "Length: {$length} cm";
    if (!empty($chest_width)) $measurements_parts[] = "Chest width: {$chest_width} cm";
    if (!empty($waist_width)) $measurements_parts[] = "Waist width: {$waist_width} cm";
    if (!empty($hips_width)) $measurements_parts[] = "Hips width: {$hips_width} cm";
    if (!empty($measurements_parts)) {
        $prompt_parts[] = "Measurements: " . implode(', ', $measurements_parts);
    }

    if (!empty($additional_info_en)) {
        $prompt_parts[] = "Additional Details: {$additional_info_en}";
    }
    
    $prompt_parts[] = "Generate the output in English.";
    $prompt = implode("
", $prompt_parts);

    // --- 4. Call AI API ---
    $ai_response = LocalAIApi::createResponse([
        'input' => [
            ['role' => 'system', 'content' => 'You are an expert copywriter for e-commerce fashion brands.'],
            ['role' => 'user', 'content' => $prompt],
        ],
        'model' => 'gpt-3.5-turbo', // Using a cost-effective model
    ]);

    if (!empty($ai_response['success'])) {
        $decoded_response = LocalAIApi::decodeJsonFromResponse($ai_response);
        $generated_text = $decoded_response['choices'][0]['message']['content'] ?? '';
        
        // Find the JSON part of the response
        $json_start = strpos($generated_text, '{');
        $json_end = strrpos($generated_text, '}');
        if ($json_start !== false && $json_end !== false) {
            $json_str = substr($generated_text, $json_start, $json_end - $json_start + 1);
            $output_data = json_decode($json_str, true);

            if ($output_data && json_last_error() === JSON_ERROR_NONE) {
                // --- 5. Translation Pipeline (Output) ---
                $title = translate_from_english(htmlspecialchars($output_data['title'] ?? ''), $lang);
                $short_desc = translate_from_english(htmlspecialchars($output_data['short_description'] ?? ''), $lang);
                $description = nl2br(translate_from_english(htmlspecialchars($output_data['description'] ?? ''), $lang));
                $measurements = translate_from_english(htmlspecialchars($output_data['measurements'] ?? ''), $lang);
                $hashtags = translate_from_english(htmlspecialchars($output_data['hashtags'] ?? ''), $lang);

                // --- 6. Save to DB if user is logged in ---
                if (isset($_SESSION['user_id'])) {
                    try {
                        $pdo = db();
                        $stmt = $pdo->prepare("INSERT INTO generations (user_id, prompt, description) VALUES (?, ?, ?)");
                        $stmt->execute([$_SESSION['user_id'], $prompt, $json_str]);
                    } catch (PDOException $e) {
                        // Optional: log error to a file
                        error_log("DB Error: " . $e->getMessage());
                    }
                }

                // --- 7. Format Output ---
                $result_html = "
                    <div class='result-container'>
                        <h3>" . t('generated_result') . "</h3>
                        <div class='result-item'>
                            <label>" . t('output_title') . "</label>
                            <p id='result-title'>{$title}</p>
                        </div>
                        <div class='result-item'>
                            <label>" . t('output_short_description') . "</label>
                            <p id='result-short-desc'>{$short_desc}</p>
                        </div>
                        <div class='result-item'>
                            <label>" . t('output_description') . "</label>
                            <p id='result-desc'>{$description}</p>
                        </div>
                        <div class='result-item'>
                            <label>" . t('output_measurements') . "</label>
                            <p id='result-measurements'>{$measurements}</p>
                        </div>
                        <div class='result-item'>
                            <label>" . t('output_hashtags') . "</label>
                            <p id='result-hashtags'>{$hashtags}</p>
                        </div>
                        <div class='result-actions'>
                            <button class='btn btn-secondary' id='copy-btn'><i class='ph ph-copy'></i> " . t('copy_to_clipboard') . "</button>
                            <button class='btn btn-primary' id='approve-btn'><i class='ph ph-check'></i> " . t('approve') . "</button>
                        </div>
                    </div>
                ";
            } else {
                $error_message = t('error_parsing_ai_response');
            }
        } else {
            $error_message = t('error_parsing_ai_response');
        }
    } else {
        $error_message = t('error_calling_ai_api') . ': ' . htmlspecialchars($ai_response['error'] ?? 'Unknown error');
    }
}

// Store result in session to display on the main page
$_SESSION['generation_result'] = $result_html;
$_SESSION['generation_error'] = $error_message;

// Redirect back to the main page
header('Location: index.php#generator');
exit();