// src/services/aiService.server.ts
/**
 * @file This file contains all server-side functions that directly interact with the Google AI (Gemini) API.
 * It is intended to be used only by the backend (e.g., server.ts) and should never be imported into client-side code.
 * The `.server.ts` naming convention helps enforce this separation.
 */

import { GoogleGenAI, type GenerateContentResponse, type Content, type Tool } from '@google/genai';
import fsPromises from 'node:fs/promises';
import type { Logger } from 'pino';
import { z } from 'zod';
import { pRateLimit } from 'p-ratelimit';
import type { FlyerItem, MasterGroceryItem, ExtractedFlyerItem } from '../types';

// Helper for consistent required string validation (handles missing/null/empty)
const requiredString = (message: string) =>
  z.preprocess((val) => val ?? '', z.string().min(1, message));

// --- Zod Schemas for AI Response Validation (exported for the transformer) ---
const ExtractedFlyerItemSchema = z.object({
  item: z.string(),
  price_display: z.string(),
  price_in_cents: z.number().nullable(),
  quantity: z.string(),
  category_name: z.string(),
  master_item_id: z.number().nullish(), // .nullish() allows null or undefined
});

export const AiFlyerDataSchema = z.object({
  store_name: requiredString('Store name cannot be empty'),
  valid_from: z.string().nullable(),
  valid_to: z.string().nullable(),
  store_address: z.string().nullable(),
  items: z.array(ExtractedFlyerItemSchema),
});

/**
 * Defines the contract for a file system utility. This interface allows for
 * dependency injection, making the AIService testable without hitting the real file system.
 */
interface IFileSystem {
  readFile(path: string): Promise<Buffer>;
}

/**
 * Defines the contract for an AI model client. This allows for dependency injection,
 * making the AIService testable without making real API calls to Google.
 */
interface IAiClient {
  generateContent(request: {
    contents: Content[];
    tools?: Tool[];
  }): Promise<GenerateContentResponse>;
}

/**
 * Defines the shape of a single flyer item as returned by the AI.
 * This type is intentionally loose to accommodate potential null/undefined values
 * from the AI before they are cleaned and normalized.
 */
type RawFlyerItem = {
  item: string;
  price_display: string | null | undefined;
  price_in_cents: number | null;
  quantity: string | null | undefined;
  category_name: string | null | undefined;
  master_item_id?: number | null | undefined;
};

export class AIService {
  private aiClient: IAiClient;
  private fs: IFileSystem;
  private rateLimiter: <T>(fn: () => Promise<T>) => Promise<T>;
  private logger: Logger;

  constructor(logger: Logger, aiClient?: IAiClient, fs?: IFileSystem) {
    this.logger = logger;
    this.logger.info('---------------- [AIService] Constructor Start ----------------');
    if (aiClient) {
      this.logger.info(
        '[AIService Constructor] Using provided mock AI client. This indicates a TEST environment.',
      );
      this.aiClient = aiClient;
    } else {
      this.logger.info(
        '[AIService Constructor] No mock client provided. Initializing Google GenAI client for PRODUCTION-LIKE environment.',
      );
      // Determine if we are in any kind of test environment.
      // VITEST_POOL_ID is reliably set by Vitest during test runs.
      const isTestEnvironment = process.env.NODE_ENV === 'test' || !!process.env.VITEST_POOL_ID;
      this.logger.info(
        {
          isTestEnvironment,
          nodeEnv: process.env.NODE_ENV,
          vitestPoolId: process.env.VITEST_POOL_ID,
          hasApiKey: !!process.env.GEMINI_API_KEY,
        },
        '[AIService Constructor] Environment check',
      );

      const apiKey = process.env.GEMINI_API_KEY;
      if (!apiKey) {
        this.logger.warn('[AIService] GEMINI_API_KEY is not set.');
        // Allow initialization without key in test/build environments if strictly needed
        if (!isTestEnvironment) {
          this.logger.error('[AIService] GEMINI_API_KEY is required in non-test environments.');
          throw new Error('GEMINI_API_KEY environment variable not set for server-side AI calls.');
        } else {
          this.logger.warn(
            '[AIService Constructor] GEMINI_API_KEY is missing, but this is a test environment, so proceeding.',
          );
        }
      }
      // In test mode without injected client, we might not have a key.
      // The stubs below protect against calling the undefined client.
      // This is the correct modern SDK pattern. We instantiate the main client.
      const genAI = apiKey ? new GoogleGenAI({ apiKey }) : null;
      if (!genAI) {
        this.logger.warn(
          '[AIService] GoogleGenAI client could not be initialized (likely missing API key in test environment). Using mock placeholder.',
        );
      }

      // do not change "gemini-2.5-flash" - this is correct
      const modelName = 'gemini-2.5-flash';

      // We create a shim/adapter that matches the old structure but uses the new SDK call pattern.
      // This preserves the dependency injection pattern used throughout the class.
      this.aiClient = genAI
        ? {
            generateContent: async (request) => {
              // The model name is now injected here, into every call, as the new SDK requires.
              // Architectural guard clause: All requests from this service must have content.
              // This prevents sending invalid requests to the API and satisfies TypeScript's strictness.
              if (!request.contents || request.contents.length === 0) {
                this.logger.error(
                  { request },
                  '[AIService Adapter] generateContent called with no content, which is invalid.',
                );
                throw new Error('AIService.generateContent requires at least one content element.');
              }

              // Architectural Fix: After the guard clause, assign the guaranteed-to-exist element
              // to a new constant. This provides a definitive type-safe variable for the compiler.
              const firstContent = request.contents[0];
              this.logger.debug(
                { modelName, requestParts: firstContent.parts?.length ?? 0 },
                '[AIService] Calling actual generateContent via adapter.',
              );
              return genAI.models.generateContent({ model: modelName, ...request });
            },
          }
        : {
            // This is the updated mock for testing, matching the new response shape.
            generateContent: async () => {
              this.logger.warn(
                '[AIService] Mock generateContent called. This should only happen in tests when no API key is available.',
              );
              return { text: '[]' } as unknown as GenerateContentResponse;
            },
          };
    }

    this.fs = fs || fsPromises;

    if (aiClient) {
      this.logger.warn(
        '[AIService Constructor] Mock client detected. Rate limiter is DISABLED for testing.',
      );
      this.rateLimiter = <T>(fn: () => Promise<T>) => fn(); // Pass-through function
    } else {
      const requestsPerMinute = parseInt(process.env.GEMINI_RPM || '5', 10);
      this.logger.info(
        `[AIService Constructor] Initializing production rate limiter to ${requestsPerMinute} RPM.`,
      );
      this.rateLimiter = pRateLimit({
        interval: 60 * 1000,
        rate: requestsPerMinute,
        concurrency: requestsPerMinute,
      });
    }
    this.logger.info('---------------- [AIService] Constructor End ----------------');
  }

  private async serverFileToGenerativePart(path: string, mimeType: string) {
    const fileData = await this.fs.readFile(path);
    return {
      inlineData: {
        data: fileData.toString('base64'),
        mimeType,
      },
    };
  }

  /**
   * Constructs the detailed prompt for the AI to extract flyer data.
   * @param masterItems A list of known grocery items to aid in matching.
   * @param submitterIp The IP address of the user who submitted the flyer.
   * @param userProfileAddress The profile address of the user.
   * @returns A formatted string to be used as the AI prompt.
   */
  private _buildFlyerExtractionPrompt(
    masterItems: MasterGroceryItem[],
    submitterIp?: string,
    userProfileAddress?: string,
  ): string {
    let locationHint = '';
    if (userProfileAddress) {
      locationHint = `The user who uploaded this flyer has a profile address of "${userProfileAddress}". Use this as a strong hint for the store's location.`;
    } else if (submitterIp) {
      locationHint = `The user uploaded this flyer from an IP address that suggests a location. Use this as a general hint for the store's region.`;
    }

    // Optimization: Instead of sending the whole masterItems object, send only the necessary fields.
    // This significantly reduces the number of tokens used in the prompt.
    const simplifiedMasterList = masterItems.map((item) => ({
      id: item.master_grocery_item_id,
      name: item.name,
    }));

    return `
      # TASK
      Analyze the provided flyer image(s) and extract key information into a single, valid JSON object.

      # RULES
      1.  Extract the following top-level details for the flyer:
          - "store_name": The name of the grocery store (e.g., "Walmart", "No Frills").
          - "valid_from": The start date of the sale in YYYY-MM-DD format. Use null if not present.
          - "valid_to": The end date of the sale in YYYY-MM-DD format. Use null if not present.
          - "store_address": The physical address of the store. Use null if not present. ${locationHint}

      2.  Extract each individual sale item into an "items" array. For each item, provide:
          - "item": The name of the product (e.g., "Coca-Cola Classic").
          - "price_display": The exact sale price as a string (e.g., "$2.99", "2 for $5.00"). If no price is visible, use an empty string "".
          - "price_in_cents": The primary numeric price in cents. For "$2.99", use 299. For "2 for $5.00", use 500. If no price is visible, you MUST use null.
          - "quantity": A string describing the quantity or weight (e.g., "12x355mL", "500g", "each"). If no quantity is visible, use an empty string "".
          - "master_item_id": Find the best matching item from the MASTER LIST provided below and return its "id". If no good match is found, you MUST use null.
          - "category_name": The most appropriate category (e.g., "Beverages", "Meat & Seafood"). If unsure, use "Other/Miscellaneous".

      3.  Your entire output MUST be a single JSON object. Do not include any other text, explanations, or markdown formatting like \`\`\`json.

      # EXAMPLES
      - For an item "Red Seedless Grapes" on sale for "$1.99 /lb" that matches master item ID 45:
        { "item": "Red Seedless Grapes", "price_display": "$1.99 /lb", "price_in_cents": 199, "quantity": "/lb", "master_item_id": 45, "category_name": "Produce" }
      - For an item "PC Cola 2L" on sale "3 for $5.00" that has no master item match:
        { "item": "PC Cola 2L", "price_display": "3 for $5.00", "price_in_cents": 500, "quantity": "2L", "master_item_id": null, "category_name": "Beverages" }
      - For an item "Store-made Muffins" with no price listed:
        { "item": "Store-made Muffins", "price_display": "", "price_in_cents": null, "quantity": "6 pack", "master_item_id": 123, "category_name": "Bakery" }

      # MASTER LIST
      ${JSON.stringify(simplifiedMasterList)}

      # JSON OUTPUT
      `;
  }

  /**
   * Safely parses a JSON object from a string, typically from an AI response.
   * @param responseText The raw text response from the AI.
   * @returns The parsed JSON object, or null if parsing fails.
   */
  private _parseJsonFromAiResponse<T>(responseText: string | undefined, logger: Logger): T | null {
    // --- START HYPER-DIAGNOSTIC LOGGING ---
    console.log('\n--- DIAGNOSING _parseJsonFromAiResponse ---');
    console.log(
      `1. Initial responseText (Type: ${typeof responseText}):`,
      JSON.stringify(responseText),
    );
    // --- END HYPER-DIAGNOSTIC LOGGING ---

    if (!responseText) {
      logger.warn(
        '[_parseJsonFromAiResponse] Response text is empty or undefined. Returning null.',
      );
      console.log('2. responseText is falsy. ABORTING.');
      console.log('--- END DIAGNOSIS ---\n');
      return null;
    }

    // Find the start of the JSON, which can be inside a markdown block
    const markdownRegex = /```(json)?\s*([\s\S]*?)\s*```/;
    const markdownMatch = responseText.match(markdownRegex);
    console.log('2. Regex Result (markdownMatch):', markdownMatch);

    let jsonString;
    if (markdownMatch && markdownMatch[2] !== undefined) {
      // Check for capture group
      console.log('3. Regex matched. Processing Captured Group.');
      console.log(
        `   - Captured content (Type: ${typeof markdownMatch[2]}, Length: ${markdownMatch[2].length}):`,
        JSON.stringify(markdownMatch[2]),
      );
      logger.debug(
        { rawCapture: markdownMatch[2] },
        '[_parseJsonFromAiResponse] Found JSON content within markdown code block.',
      );

      jsonString = markdownMatch[2].trim();
      console.log(
        `4. After trimming, jsonString is (Type: ${typeof jsonString}, Length: ${jsonString.length}):`,
        JSON.stringify(jsonString),
      );
      logger.debug(
        { trimmedJsonString: jsonString },
        '[_parseJsonFromAiResponse] Trimmed extracted JSON string.',
      );
    } else {
      console.log(
        '3. Regex did NOT match or capture group 2 is undefined. Will attempt to parse entire responseText.',
      );
      jsonString = responseText;
    }

    // Find the first '{' or '[' and the last '}' or ']' to isolate the JSON object.
    const firstBrace = jsonString.indexOf('{');
    const firstBracket = jsonString.indexOf('[');
    console.log(
      `5. Index search on jsonString: firstBrace=${firstBrace}, firstBracket=${firstBracket}`,
    );

    // Determine the starting point of the JSON content
    const startIndex =
      firstBrace === -1 || (firstBracket !== -1 && firstBracket < firstBrace)
        ? firstBracket
        : firstBrace;
    console.log('6. Calculated startIndex:', startIndex);

    if (startIndex === -1) {
      logger.error(
        { responseText },
        "[_parseJsonFromAiResponse] Could not find starting '{' or '[' in response.",
      );
      console.log('7. startIndex is -1. ABORTING.');
      console.log('--- END DIAGNOSIS ---\n');
      return null;
    }

    const jsonSlice = jsonString.substring(startIndex);
    console.log(
      `8. Sliced string to be parsed (jsonSlice) (Length: ${jsonSlice.length}):`,
      JSON.stringify(jsonSlice),
    );

    try {
      console.log('9. Attempting JSON.parse on jsonSlice...');
      const parsed = JSON.parse(jsonSlice) as T;
      console.log('10. SUCCESS: JSON.parse succeeded.');
      console.log('--- END DIAGNOSIS (SUCCESS) ---\n');
      return parsed;
    } catch (e) {
      logger.error(
        { jsonSlice, error: e, errorMessage: (e as Error).message, stack: (e as Error).stack },
        '[_parseJsonFromAiResponse] Failed to parse JSON slice.',
      );
      console.error('10. FAILURE: JSON.parse FAILED. Error:', e);
      console.log('--- END DIAGNOSIS (FAILURE) ---\n');
      return null;
    }
  }

  async extractItemsFromReceiptImage(
    imagePath: string,
    imageMimeType: string,
    logger: Logger = this.logger,
  ): Promise<{ raw_item_description: string; price_paid_cents: number }[] | null> {
    const prompt = `
      Analyze the provided receipt image. Extract all purchased line items.
      For each item, identify its description and total price.
      Return the data as a valid JSON array of objects. Each object should have two keys:
      1. "raw_item_description": a string containing the item's name as written on the receipt.
      2. "price_paid_cents": an integer representing the total price for that line item in cents (do not include currency symbols).
  
      Example format:
      [
        { "raw_item_description": "ORGANIC BANANAS", "price_paid_cents": 129 },
        { "raw_item_description": "AVOCADO", "price_paid_cents": 299 }
      ]
  
      Only output the JSON array. Do not include any other text, explanations, or markdown formatting.
    `;

    const imagePart = await this.serverFileToGenerativePart(imagePath, imageMimeType);

    logger.info('[extractItemsFromReceiptImage] Entering method.');
    try {
      logger.debug('[extractItemsFromReceiptImage] PRE-RATE-LIMITER: Preparing to call AI.');
      // Wrap the AI call with the rate limiter.
      const result = await this.rateLimiter(() =>
        this.aiClient.generateContent({
          contents: [{ parts: [{ text: prompt }, imagePart] }],
        }),
      );
      logger.debug(
        '[extractItemsFromReceiptImage] POST-RATE-LIMITER: AI call successful, parsing response.',
      );

      // The response from the SDK is structured, we need to access the text part.
      const text = result.text;
      logger.debug(
        { rawText: text?.substring(0, 100) },
        '[extractItemsFromReceiptImage] Raw text from AI.',
      );
      const parsedJson = this._parseJsonFromAiResponse<
        { raw_item_description: string; price_paid_cents: number }[]
      >(text, logger);

      if (!parsedJson) {
        logger.error(
          { responseText: text },
          '[extractItemsFromReceiptImage] Failed to parse valid JSON from response.',
        );
        throw new Error('AI response did not contain a valid JSON array.');
      }

      logger.info('[extractItemsFromReceiptImage] Successfully extracted items. Exiting method.');
      return parsedJson;
    } catch (apiError) {
      logger.error(
        { err: apiError },
        '[extractItemsFromReceiptImage] An error occurred during the process.',
      );
      throw apiError;
    }
  }

  async extractCoreDataFromFlyerImage(
    imagePaths: { path: string; mimetype: string }[],
    masterItems: MasterGroceryItem[],
    submitterIp?: string,
    userProfileAddress?: string,
    logger: Logger = this.logger,
  ): Promise<{
    store_name: string;
    valid_from: string | null;
    valid_to: string | null;
    store_address: string | null;
    items: ExtractedFlyerItem[];
  }> {
    logger.info(
      `[extractCoreDataFromFlyerImage] Entering method with ${imagePaths.length} image(s).`,
    );
    const prompt = this._buildFlyerExtractionPrompt(masterItems, submitterIp, userProfileAddress);

    const imageParts = await Promise.all(
      imagePaths.map((file) => this.serverFileToGenerativePart(file.path, file.mimetype)),
    );

    const totalImageSize = imageParts.reduce((acc, part) => acc + part.inlineData.data.length, 0);
    logger.info(
      `[aiService.server] Total base64 image data size for Gemini: ${(totalImageSize / (1024 * 1024)).toFixed(2)} MB`,
    );

    try {
      logger.debug(
        `[extractCoreDataFromFlyerImage] PRE-RATE-LIMITER: Preparing to call Gemini API.`,
      );
      const geminiCallStartTime = process.hrtime.bigint();

      // Wrap the AI call with the rate limiter.
      const result = await this.rateLimiter(() => {
        logger.debug(
          '[extractCoreDataFromFlyerImage] INSIDE-RATE-LIMITER: Executing generateContent call.',
        );
        return this.aiClient.generateContent({
          contents: [{ parts: [{ text: prompt }, ...imageParts] }],
        });
      });
      logger.debug('[extractCoreDataFromFlyerImage] POST-RATE-LIMITER: AI call completed.');

      const geminiCallEndTime = process.hrtime.bigint();
      const durationMs = Number(geminiCallEndTime - geminiCallStartTime) / 1_000_000;
      logger.info(
        `[aiService.server] Gemini API call for flyer processing completed in ${durationMs.toFixed(2)} ms.`,
      );

      const text = result.text;
      logger.debug(
        `[aiService.server] Raw Gemini response text (first 500 chars): ${text?.substring(0, 500)}`,
      );

      const extractedData = this._parseJsonFromAiResponse<z.infer<typeof AiFlyerDataSchema>>(
        text,
        logger,
      );

      if (!extractedData) {
        logger.error(
          { responseText: text },
          '[extractCoreDataFromFlyerImage] AI response did not contain a valid JSON object after parsing.',
        );
        throw new Error('AI response did not contain a valid JSON object.');
      }

      // Normalize the items to create a clean data structure.
      logger.debug('[extractCoreDataFromFlyerImage] Normalizing extracted items.');
      const normalizedItems = Array.isArray(extractedData.items)
        ? this._normalizeExtractedItems(extractedData.items)
        : [];

      logger.info(
        `[extractCoreDataFromFlyerImage] Successfully processed flyer data for store: ${extractedData.store_name}. Exiting method.`,
      );
      return { ...extractedData, items: normalizedItems };
    } catch (apiError) {
      logger.error({ err: apiError }, '[extractCoreDataFromFlyerImage] The entire process failed.');
      throw apiError;
    }
  }

  /**
   * Normalizes the raw items returned by the AI, ensuring fields are in the correct format.
   * @param items An array of raw flyer items from the AI.
   * @returns A normalized array of flyer items.
   */
  private _normalizeExtractedItems(items: RawFlyerItem[]): ExtractedFlyerItem[] {
    return items.map((item: RawFlyerItem) => ({
      ...item,
      price_display:
        item.price_display === null || item.price_display === undefined
          ? ''
          : String(item.price_display),
      quantity: item.quantity === null || item.quantity === undefined ? '' : String(item.quantity),
      category_name:
        item.category_name === null || item.category_name === undefined
          ? 'Other/Miscellaneous'
          : String(item.category_name),
      master_item_id: item.master_item_id ?? undefined,
    }));
  }

  /**
   * SERVER-SIDE FUNCTION
   * Extracts a specific piece of text from a cropped area of an image.
   * @param imagePath The path to the original image file on the server.
   * @param cropArea The coordinates and dimensions { x, y, width, height } to crop.
   * @param extractionType The type of data to extract, which determines the AI prompt.
   * @returns A promise that resolves to the extracted text.
   */
  async extractTextFromImageArea(
    imagePath: string,
    imageMimeType: string,
    cropArea: { x: number; y: number; width: number; height: number },
    extractionType: 'store_name' | 'dates' | 'item_details',
    logger: Logger = this.logger,
  ): Promise<{ text: string | undefined }> {
    logger.info(
      `[extractTextFromImageArea] Entering method for extraction type: ${extractionType}.`,
    );
    // 1. Define prompts based on the extraction type
    const prompts = {
      store_name: 'What is the store name in this image? Respond with only the name.',
      dates:
        'What are the sale dates in this image? Respond with the date range as text (e.g., "Jan 1 - Jan 7").',
      item_details:
        'Extract the item name, price, and quantity from this image. Respond with the text as seen.',
    };

    const prompt = prompts[extractionType] || 'Extract the text from this image.';

    // 2. Crop the image using sharp
    logger.debug('[extractTextFromImageArea] Cropping image with sharp.');
    const sharp = (await import('sharp')).default;
    const croppedImageBuffer = await sharp(imagePath)
      .extract({
        left: Math.round(cropArea.x),
        top: Math.round(cropArea.y),
        width: Math.round(cropArea.width),
        height: Math.round(cropArea.height),
      })
      .toBuffer();

    // 3. Convert cropped buffer to GenerativePart
    const imagePart = {
      inlineData: {
        data: croppedImageBuffer.toString('base64'),
        mimeType: imageMimeType,
      },
    };

    // 4. Call the AI model
    try {
      logger.debug(`[extractTextFromImageArea] PRE-RATE-LIMITER: Preparing to call AI.`);
      // Wrap the AI call with the rate limiter.
      const result = await this.rateLimiter(() => {
        logger.debug(`[extractTextFromImageArea] INSIDE-RATE-LIMITER: Executing generateContent.`);
        return this.aiClient.generateContent({
          contents: [{ parts: [{ text: prompt }, imagePart] }],
        });
      });
      logger.debug('[extractTextFromImageArea] POST-RATE-LIMITER: AI call completed.');

      const text = result.text?.trim();
      logger.info(
        `[extractTextFromImageArea] Gemini rescan completed. Extracted text: "${text}". Exiting method.`,
      );
      return { text };
    } catch (apiError) {
      logger.error(
        { err: apiError },
        `[extractTextFromImageArea] An error occurred for type ${extractionType}.`,
      );
      throw apiError;
    }
  }

  /**
   * SERVER-SIDE FUNCTION
   * Uses Google Maps grounding to find nearby stores and plan a shopping trip.
   * @param items The items from the flyer.
   * @param store The store associated with the flyer.
   * @param userLocation The user's current geographic coordinates.
   * @returns A text response with trip planning advice and a list of map sources.
   */
  async planTripWithMaps(
    items: FlyerItem[],
    store: { name: string } | undefined,
    userLocation: GeolocationCoordinates,
    logger: Logger = this.logger,
  ): Promise<{ text: string; sources: { uri: string; title: string }[] }> {
    // Return a 501 Not Implemented error as this feature is disabled.
    logger.warn('[AIService] planTripWithMaps called, but feature is disabled. Throwing error.');
    throw new Error("The 'planTripWithMaps' feature is currently disabled due to API costs.");

    /* const topItems = items.slice(0, 5).map(i => i.item).join(', ');
      const storeName = store?.name || 'the grocery store';
      
      try {
          // Wrap the AI call with the rate limiter.
          const result = await this.rateLimiter(() => this.aiClient.generateContent({
              contents: [{ parts: [{ text: `My current location is latitude ${userLocation.latitude}, longitude ${userLocation.longitude}.
              I have a shopping list with items like ${topItems}. Find the nearest ${storeName} to me and suggest the best route.
              Also, are there any other specialty stores nearby (like a bakery or butcher) that might have good deals on related items?`}]}],
               tools: [{ "googleSearch": {} }],
          }));
  
          // In a real implementation, you would render the map URLs from the sources.
          // The new SDK provides the search queries used, not a direct list of web attributions.
          // We will transform these queries into searchable links to fulfill the contract of the function.
          const searchQueries = result.candidates?.[0]?.groundingMetadata?.webSearchQueries || [];
          const sources = searchQueries.map((query: string) => ({
              uri: `https://www.google.com/search?q=${encodeURIComponent(query)}`,
              title: query
          }));

          return { text: result.text ?? '', sources };
        } catch (apiError) {
          logger.error({ err: apiError }, "Google GenAI API call failed in planTripWithMaps");
          throw apiError;
      }
      */
  }
}

// Export a singleton instance of the service for use throughout the application.
import { logger } from './logger.server';
export const aiService = new AIService(logger);