/**
 * Simplified OpenAI Responses module for Discord.
 * Listens to message events, sends chat queries to the OpenAI Responses API,
 * and handles text or image (function_call) outputs.
 */
// Removed local file fallback; prompt now comes exclusively from PocketBase via responsesPrompt module
import { OpenAI } from 'openai';
import axios from 'axios';
import { AttachmentBuilder, PermissionFlagsBits } from 'discord.js';
import fs from 'fs/promises';
import path from 'path';

// Discord message max length
const MAX_DISCORD_MSG_LENGTH = 2000;

/**
 * Split a long message into chunks of at most maxLength, preserving code fences.
 * @param {string} text - Full message text to split.
 * @param {number} maxLength - Maximum length per chunk.
 * @returns {string[]} Array of message chunks.
 */
function splitMessage(text, maxLength = MAX_DISCORD_MSG_LENGTH) {
  const lines = text.split(/\n/);
  const chunks = [];
  let chunk = '';
  let codeBlockOpen = false;
  let codeBlockFence = '```';
  for (let line of lines) {
    const trimmed = line.trim();
    const isFenceLine = trimmed.startsWith('```');
    if (isFenceLine) {
      if (!codeBlockOpen) {
        codeBlockOpen = true;
        codeBlockFence = trimmed;
      } else if (trimmed === '```') {
        // closing fence
        codeBlockOpen = false;
      }
    }
    // include the newline that was removed by split
    const segment = line + '\n';
    // if adding segment exceeds limit
    if (chunk.length + segment.length > maxLength) {
      if (chunk.length > 0) {
        // close open code block if needed
        if (codeBlockOpen) chunk += '\n```';
        chunks.push(chunk);
        // start new chunk, reopen code block if needed
        chunk = codeBlockOpen ? (codeBlockFence + '\n' + segment) : segment;
        continue;
      }
      // single segment too long, split it directly
      let rest = segment;
      while (rest.length > maxLength) {
        let part = rest.slice(0, maxLength);
        if (codeBlockOpen) part += '\n```';
        chunks.push(part);
        rest = codeBlockOpen ? (codeBlockFence + '\n' + rest.slice(maxLength)) : rest.slice(maxLength);
      }
      chunk = rest;
      continue;
    }
    chunk += segment;
  }
  if (chunk) {
    // close any unclosed code block
    if (codeBlockOpen) chunk += '\n```';
    chunks.push(chunk);
  }
  // remove trailing newline from each chunk
  return chunks.map(c => c.endsWith('\n') ? c.slice(0, -1) : c);
}


/**
 * Determine whether the bot should respond to a message.
 * Triggers when the bot is mentioned or when the message is a direct reply.
 * @param {Message} message - The incoming Discord message.
 * @param {string} botId - The bot user ID.
 * @param {object} logger - Logger for debugging.
 * @returns {Promise<boolean>} True if the bot should respond.
 */
async function shouldRespond(message, botId, logger) {
  if (message.author.bot || !botId) return false;
  const isMention = message.mentions.users.has(botId);
  let isReply = false;
  if (message.reference?.messageId) {
    try {
      const ref = await message.channel.messages.fetch(message.reference.messageId);
      isReply = ref.author.id === botId;
    } catch {}
  }
  logger.debug(`Trigger? mention=${isMention} reply=${isReply}`);
  return isMention || isReply;
}

/**
 * Cache the last AI response ID for a channel or thread, for context continuity.
 * @param {object} client - Discord client with pocketbase cache.
 * @param {string} key - Channel or thread identifier.
 * @param {string} id - The AI response ID to cache.
 * @param {number} ttlSeconds - Time-to-live for the cache entry in seconds.
 */
function cacheResponse(client, key, id, ttlSeconds) {
  client.pb?.cache?.set(key, id, ttlSeconds);
}

/**
 * Award token usage points to a user for AI output via the scorekeeper.
 * @param {object} client - Discord client with scorekeeper.
 * @param {string} guildId - ID of the guild (server).
 * @param {string} userId - ID of the user.
 * @param {number} amount - Number of tokens to award.
 */
function awardOutput(client, guildId, userId, amount) {
  if (client.scorekeeper && amount > 0) {
    client.scorekeeper.addOutput(guildId, userId, amount)
      .catch(err => client.logger.error(`Scorekeeper error: ${err.message}`));
  }
}

/**
 * Handle an OpenAI 'generate_image' function call response.
 * Downloads generated images, saves to disk, and replies with attachments.
 * @param {object} client - Discord client instance.
 * @param {Message} message - The original Discord message to reply to.
 * @param {object} resp - OpenAI Responses API response containing function_call.
 * @param {object} cfg - Module configuration for image generation.
 * @returns {Promise<boolean>} True if the function call was handled.
 */
async function handleImage(client, message, resp, cfg) {
  const calls = Array.isArray(resp.output) ? resp.output : [];
  const fn = calls.find(o => o.type === 'function_call' && o.name === 'generate_image');
  if (!fn?.arguments) return false;
  client.logger.debug(`Image function args: ${fn.arguments}`);
  let args;
  try { args = JSON.parse(fn.arguments); } catch { return false; }
  if (!args.prompt?.trim()) {
    await message.reply('Cannot generate image: empty prompt.');
    return true;
  }
  // Use image model defined in config
  const model = cfg.imageGeneration.defaultModel;
  const promptText = args.prompt;
  // Determine number of images (1-10); DALL·E-3 only supports 1
  let count = 1;
  if (args.n != null) {
    const nVal = typeof args.n === 'number' ? args.n : parseInt(args.n, 10);
    if (!Number.isNaN(nVal)) count = nVal;
  }
  // clamp between 1 and 10
  count = Math.max(1, Math.min(10, count));
  if (model === 'dall-e-3') count = 1;
  const size = args.size || 'auto';
  // Determine quality based on config and model constraints
  let quality = args.quality || cfg.imageGeneration.defaultQuality;
  if (model === 'gpt-image-1') {
    if (!['low', 'medium', 'high', 'auto'].includes(quality)) quality = 'auto';
  } else if (model === 'dall-e-2') {
    quality = 'standard';
  } else if (model === 'dall-e-3') {
    if (!['standard', 'hd', 'auto'].includes(quality)) quality = 'standard';
  }
  const background = args.background;
  const moderation = args.moderation;
  const outputFormat = args.output_format;
  const compression = args.output_compression;
  const style = args.style;
  const user = args.user || message.author.id;
  try {
    // Build generate parameters
    const genParams = { model, prompt: promptText, n: count, size, quality, user };
    // response_format supported for DALL·E models (not gpt-image-1)
    if (model !== 'gpt-image-1' && args.response_format) {
      genParams['response_format'] = args.response_format;
    }
    // gpt-image-1 supports background, moderation, output_format, and output_compression
    if (model === 'gpt-image-1') {
      if (background) genParams['background'] = background;
      if (moderation) genParams['moderation'] = moderation;
      if (outputFormat) {
        genParams['output_format'] = outputFormat;
        // only support compression for JPEG or WEBP formats
        if (['jpeg','webp'].includes(outputFormat) && typeof compression === 'number') {
          genParams['output_compression'] = compression;
        }
      }
    }
    // dall-e-3 supports style
    if (model === 'dall-e-3' && style) {
      genParams['style'] = style;
    }
    // Generate images via OpenAI Images API
    const imgRes = await client.openai.images.generate(genParams);
    const images = imgRes.data || [];
    if (!images.length) throw new Error('No images generated');
    // Ensure save directory exists
    const dir = cfg.imageGeneration?.imageSavePath || './images';
    await fs.mkdir(dir, { recursive: true });
    const attachments = [];
    const outputs = [];
    // Process each generated image
    for (let i = 0; i < images.length; i++) {
      const img = images[i];
      let buffer, ext = outputFormat || 'png';
      if (img.b64_json) {
        buffer = Buffer.from(img.b64_json, 'base64');
        outputs.push({ b64_json: img.b64_json });
      } else if (img.url) {
        const dl = await axios.get(img.url, { responseType: 'arraybuffer' });
        buffer = Buffer.from(dl.data);
        // derive extension from URL if possible
        const parsed = path.extname(img.url.split('?')[0]).replace(/^[.]/, '');
        if (parsed) ext = parsed;
        outputs.push({ url: img.url });
      } else {
        throw new Error('No image data');
      }
      const filename = `${message.author.id}-${Date.now()}-${i}.${ext}`;
      const filePath = path.join(dir, filename);
      await fs.writeFile(filePath, buffer);
      client.logger.info(`Saved image: ${filePath}`);
      attachments.push(new AttachmentBuilder(buffer, { name: filename }));
    }
    // Reply with attachments
    await message.reply({ content: promptText, files: attachments });
  } catch (err) {
    client.logger.error(`Image error: ${err.message}`);
    await message.reply(`Image generation error: ${err.message}`);
  }
  return true;
}

/**
 * Main message handler for Discord 'messageCreate' events.
 * Sends user messages to the AI, handles text or image responses, and awards tokens.
 * @param {object} client - Discord client instance.
 * @param {object} cfg - Module configuration.
 * @param {Message} message - Incoming Discord message.
 */
async function onMessage(client, cfg, message) {
  const logger = client.logger;
  const botId = client.user?.id;
  client.logger.debug(`[onMessage] Received message ${message.id} from ${message.author.id}`);
  if (!(await shouldRespond(message, botId, logger))) return;
  await message.channel.sendTyping();

  // Determine channel/thread key for context
  const key = message.thread?.id || message.channel.id;
  // Initialize per-channel lock map
  const lockMap = client._responseLockMap || (client._responseLockMap = new Map());
  // Get last pending promise for this key
  const last = lockMap.get(key) || Promise.resolve();
  // Handler to run in sequence
  const handler = async () => {
    try {
      // Previous response ID for context continuity
      const prev = client.pb?.cache?.get(key);
      // Enforce minimum score to use AI responses
      // Enforce minimum score to use AI responses if scorekeeper is enabled
      if (client.scorekeeper) {
        try {
          const isAdmin = message.member?.permissions?.has(PermissionFlagsBits.Administrator);
          const scoreData = await client.scorekeeper.getScore(message.guild.id, message.author.id);
          if (!isAdmin && scoreData.totalScore < cfg.minScore) {
            await message.reply(
              `You need an I/O score of at least ${cfg.minScore} to use AI responses. Your current I/O score is ${scoreData.totalScore.toFixed(2)}.`
            );
            return;
          }
        } catch (err) {
          client.logger.error(`Error checking score: ${err.message}`);
        }
      }
      // Build request body, including replied-to message context and mention of who spoke
      let referencePrefix = '';
      let referenceMessage = null;
      if (message.reference?.messageId) {
        try {
          const ref = await message.channel.messages.fetch(message.reference.messageId);
          referenceMessage = ref;
          const refContent = ref.content || '';
          if (ref.author.id === botId) {
            referencePrefix = `You said: ${refContent}`;
          } else {
            referencePrefix = `<@${ref.author.id}> said: ${refContent}`;
          }
        } catch {
          // ignore fetch errors
        }
      }
      const speakerMention = `<@${message.author.id}>`;
      const userInput = referencePrefix
        ? `${referencePrefix}\n${speakerMention} said to you: ${message.content}`
        : `${speakerMention} said to you: ${message.content}`;
      const body = {
        model: cfg.defaultModel,
        instructions: client.responsesPrompt,
        input: userInput,
        previous_response_id: prev,
        max_output_tokens: cfg.defaultMaxTokens,
        temperature: cfg.defaultTemperature,
      };
      // Assemble any enabled tools
      const tools = [];
      if (cfg.tools?.imageGeneration) {
        const model = cfg.imageGeneration.defaultModel;
        // Configure allowed sizes per model
        let sizeEnum;
        switch (model) {
          case 'gpt-image-1': sizeEnum = ['auto','1024x1024','1536x1024','1024x1536']; break;
          case 'dall-e-2': sizeEnum = ['256x256','512x512','1024x1024']; break;
          case 'dall-e-3': sizeEnum = ['auto','1024x1024','1792x1024','1024x1792']; break;
          default: sizeEnum = ['auto','1024x1024'];
        }
        // Configure quality options per model
        let qualityEnum;
        switch (model) {
          case 'gpt-image-1': qualityEnum = ['auto','low','medium','high']; break;
          case 'dall-e-2': qualityEnum = ['standard']; break;
          case 'dall-e-3': qualityEnum = ['auto','standard','hd']; break;
          default: qualityEnum = ['auto','standard'];
        }
        // Build schema properties dynamically
        const properties = {
          prompt: { type: 'string', description: 'Text description of desired image(s).' },
          n: { type: 'number', description: 'Number of images to generate.' },
          size: { type: 'string', enum: sizeEnum, description: 'Image size.' },
          quality: { type: 'string', enum: qualityEnum, description: 'Image quality.' },
          user: { type: 'string', description: 'Unique end-user identifier.' }
        };
        if (model !== 'gpt-image-1') {
          properties.response_format = { type: 'string', enum: ['url','b64_json'], description: 'Format of returned images.' };
        }
        if (model === 'gpt-image-1') {
          properties.background = { type: 'string', enum: ['transparent','opaque','auto'], description: 'Background transparency.' };
          properties.moderation = { type: 'string', enum: ['low','auto'], description: 'Content moderation level.' };
          properties.output_format = { type: 'string', enum: ['png','jpeg','webp'], description: 'Output image format.' };
          properties.output_compression = { type: 'number', description: 'Compression level (0-100).' };
        }
        if (model === 'dall-e-3') {
          properties.style = { type: 'string', enum: ['vivid','natural'], description: 'Style option for dall-e-3.' };
        }
        // Determine required fields
        const required = ['prompt','n','size','quality','user'];
        if (model !== 'gpt-image-1') required.push('response_format');
        if (model === 'gpt-image-1') required.push('background','moderation','output_format','output_compression');
        if (model === 'dall-e-3') required.push('style');
        // Register the function tool
        tools.push({
          type: 'function',
          name: 'generate_image',
          description: `Generate images using model ${model} with requested parameters.`,
          parameters: {
            type: 'object',
            properties,
            required,
            additionalProperties: false
          },
          strict: true
        });
      }
      if (cfg.tools?.webSearch) {
        tools.push({ type: 'web_search_preview' });
      }
      if (tools.length) {
        body.tools = tools;
      }

      // If there are image attachments in the referenced or current message, wrap text and images into a multimodal message
      const refImages = referenceMessage
        ? referenceMessage.attachments.filter(att => /\.(png|jpe?g|gif|webp)$/i.test(att.name || att.url))
        : new Map();
      const currImages = message.attachments.filter(att => /\.(png|jpe?g|gif|webp)$/i.test(att.name || att.url));
      if (refImages.size > 0 || currImages.size > 0) {
        // build ordered content items: text first, then referenced images, then current images
        const contentItems = [{ type: 'input_text', text: userInput }];
        for (const att of refImages.values()) {
          contentItems.push({ type: 'input_image', detail: 'auto', image_url: att.url });
        }
        for (const att of currImages.values()) {
          contentItems.push({ type: 'input_image', detail: 'auto', image_url: att.url });
        }
        body.input = [{ type: 'message', role: 'user', content: contentItems }];
      }

      // Call OpenAI Responses
      logger.debug(`Calling AI with body: ${JSON.stringify(body)}`);
      const resp = await client.openai.responses.create(body);
      logger.info(`AI response id=${resp.id}`);
      // Award tokens for the AI chat response
      const chatTokens = resp.usage?.total_tokens ?? resp.usage?.completion_tokens ?? 0;
      awardOutput(client, message.guild.id, message.author.id, chatTokens);

      // Cache response ID if not a function call
      const isFuncCall = Array.isArray(resp.output) && resp.output.some(o => o.type === 'function_call');
      if (!isFuncCall && resp.id && cfg.conversationExpiry) {
        cacheResponse(client, key, resp.id, Math.floor(cfg.conversationExpiry / 1000));
      }

      // Handle image function call if present
      if (await handleImage(client, message, resp, cfg)) return;

      // Otherwise reply with text
      const text = resp.output_text?.trim();
      if (text) {
        const parts = splitMessage(text, MAX_DISCORD_MSG_LENGTH);
        for (const part of parts) {
          await message.reply(part);
        }
      }
    } catch (err) {
      logger.error(`Queued onMessage error for ${key}: ${err.message}`);
    }
  };
  // Chain the handler to the last promise
  const next = last.then(handler).catch(err => logger.error(`[onMessage] Handler error: ${err.message}`));
  lockMap.set(key, next);
  // Queue enqueued; handler will send response when its turn arrives
  return;

  // Call OpenAI Responses
  let resp;
  try {
    logger.debug(`Calling AI with body: ${JSON.stringify(body)}`);
    resp = await client.openai.responses.create(body);
    logger.info(`AI response id=${resp.id}`);
    // Award tokens for the AI chat response immediately (captures token usage even if image follows)
    const chatTokens = resp.usage?.total_tokens ?? resp.usage?.completion_tokens ?? 0;
    awardOutput(client, message.guild.id, message.author.id, chatTokens);
  } catch (err) {
    logger.error(`AI error: ${err.message}`);
    return message.reply('Error generating response.');
  }

  // Cache for next turn only if this was a text response
  const isFuncCall = Array.isArray(resp.output) && resp.output.some(o => o.type === 'function_call');
  if (!isFuncCall && resp.id && cfg.conversationExpiry) {
    cacheResponse(client, key, resp.id, Math.floor(cfg.conversationExpiry / 1000));
  }

  // Handle image function call if present
  if (await handleImage(client, message, resp, cfg)) return;

  // Otherwise reply with text (split if over Discord limit)
  const text = resp.output_text?.trim();
  if (text) {
    const parts = splitMessage(text, MAX_DISCORD_MSG_LENGTH);
    for (const part of parts) {
      await message.reply(part);
    }
  }

}

/**
 * Send a standalone narrative via the Responses API to a Discord channel or thread.
 * @param {object} client - Discord client instance.
 * @param {object} cfg - Module configuration.
 * @param {string} channelId - Target channel or thread ID.
 * @param {string} text - Narrative prompt text.
 */
export async function sendNarrative(client, cfg, channelId, text) {
  const logger = client.logger;
  try {
    // Build the narrative instructions
    const instructions = `${client.responsesPrompt}\n\nGenerate the following as an engaging narrative:`;
    const body = {
      model: cfg.defaultModel,
      instructions,
      input: text,
      max_output_tokens: cfg.defaultMaxTokens,
      temperature: cfg.defaultTemperature,
    };
    logger.debug(`[sendNarrative] Calling AI with body: ${JSON.stringify(body).slice(0,1000)}`);
    const resp = await client.openai.responses.create(body);
    logger.info(`[sendNarrative] Received AI response id=${resp.id}`);
    // Fetch the target channel or thread
    const channel = await client.channels.fetch(channelId);
    if (!channel || typeof channel.send !== 'function') {
    logger.error(`[sendNarrative] Cannot send to channel ID ${channelId}`);
      return;
    }
    // Split the output and send
    const content = resp.output_text?.trim();
    if (content) {
      const parts = splitMessage(content, MAX_DISCORD_MSG_LENGTH);
      for (const part of parts) {
        await channel.send(part);
      }
    }
  } catch (err) {
    client.logger.error(`[sendNarrative] Error: ${err.message}`);
  }
}

/**
 * Initialize the Responses module:
 * - Load the system prompt.
 * - Configure the OpenAI client.
 * - Register the message event handler.
 * @param {object} client - Discord client instance.
 * @param {object} clientConfig - Full client configuration object.
 */
export async function init(client, clientConfig) {
  const cfg = clientConfig.responses;
  client.logger.info('[module:responses] Initializing Responses module');
  // Initialize prompt from responsesPrompt module (must be loaded before this)
  client.responsesPrompt = client.responsesPrompt ?? '';
  client.openai = new OpenAI({ apiKey: cfg.apiKey });
  client.on('messageCreate', m => onMessage(client, cfg, m));
  client.logger.info('[module:responses] Responses module ready');
}