using DramaLing.Api.Data;
using DramaLing.Api.Models.Configuration;
using DramaLing.Api.Models.Entities;
using DramaLing.Api.Services.Monitoring;
using DramaLing.Api.Services.AI.Gemini;
using Microsoft.EntityFrameworkCore;
using Microsoft.Extensions.Caching.Memory;
using Microsoft.Extensions.Options;
using System.Diagnostics;
using System.Text.Json;
using DramaLing.Api.Contracts.Services.Core;

namespace DramaLing.Api.Services;

/// <summary>
/// 選項詞彙庫服務實作
/// 提供基於 CEFR 等級、詞性和字數的智能選項生成
/// </summary>
public class OptionsVocabularyService : IOptionsVocabularyService
{
    private readonly DramaLingDbContext _context;
    private readonly IMemoryCache _cache;
    private readonly ILogger<OptionsVocabularyService> _logger;
    private readonly OptionsVocabularyOptions _options;
    private readonly OptionsVocabularyMetrics _metrics;
    private readonly IGeminiClient _geminiClient;

    public OptionsVocabularyService(
        DramaLingDbContext context,
        IMemoryCache cache,
        ILogger<OptionsVocabularyService> logger,
        IOptions<OptionsVocabularyOptions> options,
        OptionsVocabularyMetrics metrics,
        IGeminiClient geminiClient)
    {
        _context = context;
        _cache = cache;
        _logger = logger;
        _options = options.Value;
        _metrics = metrics;
        _geminiClient = geminiClient;
    }

    /// <summary>
    /// 生成智能干擾選項
    /// </summary>
    public async Task<List<string>> GenerateDistractorsAsync(
        string targetWord,
        string cefrLevel,
        string partOfSpeech,
        int count = 3)
    {
        var distractorsWithDetails = await GenerateDistractorsWithDetailsAsync(
            targetWord, cefrLevel, partOfSpeech, count);

        return distractorsWithDetails.Select(v => v.Word).ToList();
    }

    /// <summary>
    /// 生成智能干擾選項（含詳細資訊）
    /// </summary>
    public async Task<List<OptionsVocabulary>> GenerateDistractorsWithDetailsAsync(
        string targetWord,
        string cefrLevel,
        string partOfSpeech,
        int count = 3)
    {
        var stopwatch = Stopwatch.StartNew();

        try
        {
            // 記錄請求指標
            _metrics.RecordGenerationRequest(cefrLevel, partOfSpeech, count);

            _logger.LogInformation("Generating {Count} distractors for word '{Word}' (CEFR: {CEFR}, PartOfSpeech: {PartOfSpeech})",
                count, targetWord, cefrLevel, partOfSpeech);

            // 1. 首先檢查資料庫中是否已有該詞彙的混淆選項
            var cacheKey = $"quiz_options_{targetWord.ToLowerInvariant()}_{cefrLevel}_{partOfSpeech}";

            if (_cache.TryGetValue(cacheKey, out List<OptionsVocabulary>? cachedOptions) && cachedOptions != null)
            {
                _logger.LogInformation("✅ Using cached distractors for '{Word}': {Distractors}",
                    targetWord, string.Join(", ", cachedOptions.Select(d => d.Word)));
                return cachedOptions.Take(count).ToList();
            }

            // 2. 檢查資料庫中是否有現有選項
            var targetWordLower = targetWord.ToLower();
            var existingOptions = await _context.OptionsVocabularies
                .Where(ov => ov.CEFRLevel == cefrLevel &&
                            ov.PartOfSpeech == partOfSpeech &&
                            ov.IsActive &&
                            ov.Word.ToLower() != targetWordLower)
                .Take(count * 2) // 取更多選項以供隨機選擇
                .ToListAsync();

            if (existingOptions.Count >= count)
            {
                var selectedExisting = existingOptions
                    .OrderBy(x => Guid.NewGuid()) // 隨機排序
                    .Take(count)
                    .ToList();

                // 快取結果
                _cache.Set(cacheKey, selectedExisting, TimeSpan.FromHours(1));

                _logger.LogInformation("📚 Using existing database distractors for '{Word}': {Distractors}",
                    targetWord, string.Join(", ", selectedExisting.Select(d => d.Word)));
                return selectedExisting;
            }

            _logger.LogInformation("💡 Insufficient database options ({ExistingCount}/{RequiredCount}), proceeding to AI generation for '{Word}'",
                existingOptions.Count, count, targetWord);

            // 3. 使用 AI 生成新的混淆選項
            var aiGeneratedOptions = await GenerateOptionsWithAI(targetWord, cefrLevel, partOfSpeech, count);

            // 4. 保存新生成的選項到資料庫
            if (aiGeneratedOptions.Any())
            {
                await _context.OptionsVocabularies.AddRangeAsync(aiGeneratedOptions);
                await _context.SaveChangesAsync();

                // 快取結果
                _cache.Set(cacheKey, aiGeneratedOptions, TimeSpan.FromHours(1));
            }

            _logger.LogInformation("🤖 Successfully generated {Count} AI distractors for '{Word}': {Distractors}",
                aiGeneratedOptions.Count, targetWord,
                string.Join(", ", aiGeneratedOptions.Select(d => d.Word)));

            // 記錄生成完成指標
            stopwatch.Stop();
            _metrics.RecordGenerationDuration(stopwatch.Elapsed, aiGeneratedOptions.Count);

            return aiGeneratedOptions;
        }
        catch (Exception ex)
        {
            _logger.LogError(ex, "Error generating distractors for word '{Word}'", targetWord);
            _metrics.RecordError("generation_failed", "GenerateDistractorsWithDetailsAsync");

            // 降級到固定選項
            return GetFallbackDistractors(targetWord, cefrLevel, partOfSpeech, count);
        }
    }

    /// <summary>
    /// 檢查詞彙庫是否有足夠的詞彙支援選項生成
    /// </summary>
    public async Task<bool> HasSufficientVocabularyAsync(string cefrLevel, string partOfSpeech)
    {
        try
        {
            var allowedLevels = GetAllowedCEFRLevels(cefrLevel);

            var count = await _context.OptionsVocabularies
                .Where(v => v.IsActive &&
                           allowedLevels.Contains(v.CEFRLevel) &&
                           v.PartOfSpeech == partOfSpeech)
                .CountAsync();

            var hasSufficient = count >= _options.MinimumVocabularyThreshold;

            _logger.LogDebug("Vocabulary count for CEFR: {CEFR}, PartOfSpeech: {PartOfSpeech}: {Count} (sufficient: {HasSufficient})",
                cefrLevel, partOfSpeech, count, hasSufficient);

            return hasSufficient;
        }
        catch (Exception ex)
        {
            _logger.LogError(ex, "Error checking vocabulary sufficiency for CEFR: {CEFR}, PartOfSpeech: {PartOfSpeech}",
                cefrLevel, partOfSpeech);
            return false;
        }
    }

    /// <summary>
    /// 獲取允許的 CEFR 等級（包含相鄰等級）
    /// </summary>
    private static List<string> GetAllowedCEFRLevels(string targetLevel)
    {
        var levels = new[] { "A1", "A2", "B1", "B2", "C1", "C2" };
        var targetIndex = Array.IndexOf(levels, targetLevel);

        if (targetIndex == -1)
        {
            // 如果不是標準 CEFR 等級，只返回原等級
            return new List<string> { targetLevel };
        }

        var allowed = new List<string> { targetLevel };

        // 加入相鄰等級（允許難度稍有差異）
        if (targetIndex > 0)
            allowed.Add(levels[targetIndex - 1]);
        if (targetIndex < levels.Length - 1)
            allowed.Add(levels[targetIndex + 1]);

        return allowed;
    }

    /// <summary>
    /// 獲取允許的 CEFR 數字等級（包含相鄰等級）
    /// </summary>
    private static List<int> GetAllowedCEFRLevelsNumeric(int targetLevelNumeric)
    {
        var allowed = new List<int> { targetLevelNumeric };

        // 加入相鄰等級（允許難度稍有差異）
        if (targetLevelNumeric > 1)
            allowed.Add(targetLevelNumeric - 1);
        if (targetLevelNumeric < 6)
            allowed.Add(targetLevelNumeric + 1);

        return allowed;
    }

    /// <summary>
    /// 使用 AI 生成混淆選項
    /// </summary>
    private async Task<List<OptionsVocabulary>> GenerateOptionsWithAI(
        string targetWord,
        string cefrLevel,
        string partOfSpeech,
        int count)
    {
        try
        {
            var prompt = $@"Generate {count} vocabulary words that would be good wrong answers for a multiple choice quiz about the word ""{targetWord}"".

Requirements:
- CEFR level: {cefrLevel}
- Part of speech: {partOfSpeech}
- The words should be plausible but clearly different from ""{targetWord}""
- Avoid obviously wrong answers
- Make the words challenging but fair for language learners

Please respond with ONLY a JSON array of strings, like: [""word1"", ""word2"", ""word3""]";

            var response = await _geminiClient.CallGeminiAPIAsync(prompt);

            // 解析 AI 回應
            var cleanedResponse = ExtractJsonFromResponse(response);
            var generatedWords = JsonSerializer.Deserialize<string[]>(cleanedResponse);

            if (generatedWords == null || generatedWords.Length == 0)
            {
                _logger.LogWarning("AI returned empty or invalid response for word '{Word}'", targetWord);
                return GetFallbackDistractors(targetWord, cefrLevel, partOfSpeech, count);
            }

            // 轉換為 OptionsVocabulary 實體，並過濾掉與目標詞彙相同的詞彙
            var targetWordLower = targetWord.ToLower();
            var filteredWords = generatedWords
                .Where(word => !string.IsNullOrWhiteSpace(word) &&
                              word.Trim().ToLower() != targetWordLower)
                .Take(count);

            var options = filteredWords.Select(word => new OptionsVocabulary
            {
                Id = Guid.NewGuid(),
                Word = word.Trim(),
                CEFRLevel = cefrLevel,
                PartOfSpeech = partOfSpeech,
                IsActive = true,
                CreatedAt = DateTime.UtcNow,
                UpdatedAt = DateTime.UtcNow
            }).ToList();

            _logger.LogInformation("Filtered AI generated words from {OriginalCount} to {FilteredCount} options for '{TargetWord}'",
                generatedWords.Length, options.Count, targetWord);

            // 計算字數長度
            foreach (var option in options)
            {
                option.CalculateWordLength();
            }

            return options;
        }
        catch (Exception ex)
        {
            _logger.LogError(ex, "AI generation failed for word '{Word}', falling back to fixed options", targetWord);
            return GetFallbackDistractors(targetWord, cefrLevel, partOfSpeech, count);
        }
    }

    /// <summary>
    /// 降級固定選項（當 AI 生成失敗時使用）
    /// </summary>
    private List<OptionsVocabulary> GetFallbackDistractors(
        string targetWord,
        string cefrLevel,
        string partOfSpeech,
        int count)
    {
        // 根據詞性提供不同的固定選項（避免常見目標詞彙）
        var fallbackWords = partOfSpeech.ToLower() switch
        {
            "verb" => new[] { "accomplish", "construct", "transform", "establish", "generate", "implement", "maintain", "organize", "validate", "coordinate" },
            "noun" => new[] { "solution", "approach", "framework", "component", "structure", "mechanism", "principle", "strategy", "technique", "procedure" },
            "adjective" => new[] { "efficient", "comprehensive", "innovative", "sophisticated", "fundamental", "essential", "remarkable", "outstanding", "significant", "substantial" },
            "adverb" => new[] { "thoroughly", "efficiently", "precisely", "consistently", "systematically", "effectively", "accurately", "appropriately", "specifically", "particularly" },
            _ => new[] { "element", "aspect", "feature", "component", "factor", "criteria", "parameter", "attribute", "characteristic", "specification" }
        };

        var targetWordLower = targetWord.ToLower();
        var selectedWords = fallbackWords
            .Where(word => word.ToLower() != targetWordLower)
            .Take(count * 2)  // 取更多選項用於隨機選擇
            .OrderBy(x => Guid.NewGuid())  // 隨機排序
            .Take(count);

        _logger.LogInformation("Using fallback distractors for '{TargetWord}': {FallbackWords}",
            targetWord, string.Join(", ", selectedWords));

        return selectedWords
            .Select(word => new OptionsVocabulary
            {
                Id = Guid.NewGuid(),
                Word = word,
                CEFRLevel = cefrLevel,
                PartOfSpeech = partOfSpeech,
                IsActive = true,
                CreatedAt = DateTime.UtcNow,
                UpdatedAt = DateTime.UtcNow
            })
            .ToList();
    }

    /// <summary>
    /// 從 AI 回應中提取 JSON 內容
    /// </summary>
    private string ExtractJsonFromResponse(string response)
    {
        // 移除可能的 markdown 代碼塊標記
        var cleaned = response.Replace("```json", "").Replace("```", "").Trim();

        // 找到第一個 [ 和最後一個 ]
        var startIndex = cleaned.IndexOf('[');
        var endIndex = cleaned.LastIndexOf(']');

        if (startIndex >= 0 && endIndex >= 0 && endIndex > startIndex)
        {
            return cleaned.Substring(startIndex, endIndex - startIndex + 1);
        }

        return cleaned;
    }
}