unit tests fixin

src/services/aiService.server.ts

@@ -86,20 +86,64 @@ export const extractItemsFromReceiptImage = async (
  * @returns A promise that resolves to the extracted core data.
  */
 export const extractCoreDataFromFlyerImage = async (
-  _imagePaths: { path: string; mimetype: string }[],
-  _masterItems: MasterGroceryItem[]
+  imagePaths: { path: string; mimetype: string }[],
+  masterItems: MasterGroceryItem[]
 ): Promise<{
     store_name: string;
     valid_from: string | null;
     valid_to: string | null;
     items: Omit<FlyerItem, 'id' | 'created_at' | 'flyer_id'>[];
 }> => {
-    // This function's logic is now handled by the backend API endpoint in `src/routes/ai.ts`.
-    // The actual Gemini call logic is complex and has been moved there to keep this file clean
-    // and demonstrate the architectural separation. For the purpose of this fix, we can
-    // assume the backend correctly implements the Gemini call.
-    // This function is now a placeholder to show where the logic *would* live.
-    throw new Error("extractCoreDataFromFlyerImage is a server-side function and should be called from a backend route.");
+    // 1. Construct the detailed prompt for the AI.
+    const prompt = `
+    Analyze the provided flyer image(s). Your task is to extract key information and a list of all sale items.
+
+    First, identify the following core details for the entire flyer:
+    - "store_name": The name of the grocery store (e.g., "Walmart", "No Frills").
+    - "valid_from": The start date of the sale period in YYYY-MM-DD format. If not present, use null.
+    - "valid_to": The end date of the sale period in YYYY-MM-DD format. If not present, use null.
+
+    Second, extract each individual sale item. For each item, provide:
+    - "item": The name of the product (e.g., "Coca-Cola Classic").
+    - "price_display": The sale price as a string (e.g., "$2.99", "2 for $5.00").
+    - "price_in_cents": The primary numeric price converted to cents (e.g., for "$2.99", use 299). If a price is "2 for $5.00", use 500. If no price, use null.
+    - "quantity": A string describing the quantity or weight for the price (e.g., "12x355mL", "500g", "each").
+    - "master_item_id": From the provided master list, find the best matching item and return its ID. If no good match is found, use null.
+    - "category_name": The most appropriate category for the item (e.g., "Beverages", "Meat & Seafood").
+
+    Here is the master list of grocery items to help with matching:
+    ${JSON.stringify(masterItems)}
+
+    Return a single, valid JSON object with the keys "store_name", "valid_from", "valid_to", and "items". The "items" key should contain an array of the extracted item objects.
+    Do not include any other text, explanations, or markdown formatting.
+    `;
+
+    // 2. Convert all uploaded image files into the format required by the Gemini API.
+    const imageParts = await Promise.all(
+        imagePaths.map(file => serverFileToGenerativePart(file.path, file.mimetype))
+    );
+
+    // 3. Make the API call to Gemini.
+    const response = await model.generateContent({
+        model: 'gemini-1.5-flash',
+        contents: [{ parts: [{ text: prompt }, ...imageParts] }]
+    });
+
+    const text = response.text;
+
+    // 4. Clean and parse the AI's response.
+    const jsonMatch = text?.match(/\{[\s\S]*\}/);
+    if (!jsonMatch) {
+        logger.error("AI response for flyer processing did not contain a valid JSON object.", { responseText: text });
+        throw new Error('AI response did not contain a valid JSON object.');
+    }
+
+    try {
+        return JSON.parse(jsonMatch[0]);
+    } catch (e) {
+        logger.error("Failed to parse JSON from AI response in extractCoreDataFromFlyerImage", { responseText: text, error: e });
+        throw new Error('Failed to parse structured data from the AI response.');
+    }
 };
 
 /**