feat: improve AI resilience, add background enrichment loading states, and fix duplicate identifier in TagSelector
This commit is contained in:
207
src/app/actions/scan-label.ts
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207
src/app/actions/scan-label.ts
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'use server';
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import { GoogleGenerativeAI, SchemaType, HarmCategory, HarmBlockThreshold } from '@google/generative-ai';
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import { BottleMetadataSchema, AnalysisResponse } from '@/types/whisky';
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import { createClient } from '@/lib/supabase/server';
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import { createHash } from 'crypto';
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import { trackApiUsage } from '@/services/track-api-usage';
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import { checkCreditBalance, deductCredits } from '@/services/credit-service';
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// Native Schema Definition for Gemini API
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const metadataSchema = {
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description: "Technical metadata extracted from whisky label",
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type: SchemaType.OBJECT as const,
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properties: {
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name: { type: SchemaType.STRING, description: "Full whisky name including vintage/age", nullable: false },
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distillery: { type: SchemaType.STRING, description: "Distillery name", nullable: true },
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bottler: { type: SchemaType.STRING, description: "Independent bottler if applicable", nullable: true },
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category: { type: SchemaType.STRING, description: "Whisky category (e.g., Single Malt, Blended)", nullable: true },
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abv: { type: SchemaType.NUMBER, description: "Alcohol by volume percentage", nullable: true },
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age: { type: SchemaType.NUMBER, description: "Age statement in years", nullable: true },
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vintage: { type: SchemaType.STRING, description: "Vintage year", nullable: true },
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distilled_at: { type: SchemaType.STRING, description: "Distillation date", nullable: true },
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bottled_at: { type: SchemaType.STRING, description: "Bottling date", nullable: true },
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batch_info: { type: SchemaType.STRING, description: "Batch or cask information", nullable: true },
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bottleCode: { type: SchemaType.STRING, description: "Bottle code or serial number", nullable: true },
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is_whisky: { type: SchemaType.BOOLEAN, description: "Whether this is a whisky product", nullable: false },
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confidence: { type: SchemaType.NUMBER, description: "Confidence score 0-1", nullable: false },
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},
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required: ["name", "is_whisky", "confidence"],
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};
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export async function scanLabel(input: any): Promise<AnalysisResponse> {
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if (!process.env.GEMINI_API_KEY) {
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return { success: false, error: 'GEMINI_API_KEY is not configured.' };
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}
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let supabase;
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try {
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const getValue = (obj: any, key: string): any => {
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if (obj && typeof obj.get === 'function') return obj.get(key);
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if (obj && typeof obj[key] !== 'undefined') return obj[key];
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return null;
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};
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const file = getValue(input, 'file') as File;
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if (!file) {
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return { success: false, error: 'Kein Bild empfangen.' };
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}
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supabase = await createClient();
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const { data: { user } } = await supabase.auth.getUser();
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if (!user) {
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return { success: false, error: 'Nicht autorisiert.' };
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}
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const userId = user.id;
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const creditCheck = await checkCreditBalance(userId, 'gemini_ai');
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if (!creditCheck.allowed) {
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return {
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success: false,
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error: `Nicht genügend Credits. Benötigt: ${creditCheck.cost}, Verfügbar: ${creditCheck.balance}.`
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};
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}
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const perfTotal = performance.now();
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// Step 1: Image Preparation
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const startImagePrep = performance.now();
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const arrayBuffer = await file.arrayBuffer();
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const buffer = Buffer.from(arrayBuffer);
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const imageHash = createHash('sha256').update(buffer).digest('hex');
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const endImagePrep = performance.now();
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// Step 2: Cache Check
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const startCacheCheck = performance.now();
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const { data: cachedResult } = await supabase
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.from('vision_cache')
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.select('result')
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.eq('hash', imageHash)
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.maybeSingle();
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const endCacheCheck = performance.now();
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if (cachedResult) {
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return {
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success: true,
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data: cachedResult.result as any,
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perf: {
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imagePrep: endImagePrep - startImagePrep,
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cacheCheck: endCacheCheck - startCacheCheck,
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apiCall: 0,
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parsing: 0,
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validation: 0,
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dbOps: 0,
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uploadSize: buffer.length,
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total: performance.now() - perfTotal,
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cacheHit: true
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}
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};
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}
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// Step 3: Base64 Encoding
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const startEncoding = performance.now();
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const base64Data = buffer.toString('base64');
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const mimeType = file.type || 'image/webp';
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const uploadSize = buffer.length;
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const endEncoding = performance.now();
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// Step 4: Model Initialization & Step 5: API Call
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const startAiTotal = performance.now();
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let jsonData;
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let validatedData;
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try {
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const startModelInit = performance.now();
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const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
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const model = genAI.getGenerativeModel({
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model: 'gemini-2.5-flash',
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generationConfig: {
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responseMimeType: "application/json",
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responseSchema: metadataSchema as any,
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temperature: 0.1,
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},
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safetySettings: [
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{ category: HarmCategory.HARM_CATEGORY_HARASSMENT, threshold: HarmBlockThreshold.BLOCK_NONE },
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{ category: HarmCategory.HARM_CATEGORY_HATE_SPEECH, threshold: HarmBlockThreshold.BLOCK_NONE },
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{ category: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, threshold: HarmBlockThreshold.BLOCK_NONE },
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{ category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, threshold: HarmBlockThreshold.BLOCK_NONE },
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] as any,
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});
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const endModelInit = performance.now();
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const instruction = "Extract whisky label metadata.";
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const startApi = performance.now();
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const result = await model.generateContent([
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{ inlineData: { data: base64Data, mimeType: mimeType } },
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{ text: instruction },
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]);
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const endApi = performance.now();
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const startParse = performance.now();
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jsonData = JSON.parse(result.response.text());
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const endParse = performance.now();
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const startValidation = performance.now();
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validatedData = BottleMetadataSchema.parse(jsonData);
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const endValidation = performance.now();
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// Cache record
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await supabase.from('vision_cache').insert({ hash: imageHash, result: validatedData });
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await trackApiUsage({
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userId: userId,
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apiType: 'gemini_ai',
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endpoint: 'scanLabel',
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success: true
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});
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await deductCredits(userId, 'gemini_ai', 'Bottle OCR scan');
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const totalTime = performance.now() - perfTotal;
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return {
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success: true,
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data: validatedData,
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perf: {
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imagePrep: endImagePrep - startImagePrep,
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cacheCheck: endCacheCheck - startCacheCheck,
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encoding: endEncoding - startEncoding,
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modelInit: endModelInit - startModelInit,
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apiCall: endApi - startApi,
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parsing: endParse - startParse,
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validation: endValidation - startValidation,
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total: totalTime,
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cacheHit: false
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},
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raw: jsonData
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} as any;
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} catch (aiError: any) {
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console.warn('[ScanLabel] AI Analysis failed, providing fallback path:', aiError.message);
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// Track failure
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await trackApiUsage({
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userId: userId,
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apiType: 'gemini_ai',
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endpoint: 'scanLabel',
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success: false,
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errorMessage: aiError.message
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});
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// Return a specific structure that ScanAndTasteFlow can use to fallback to placeholder
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return {
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success: false,
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isAiError: true,
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error: aiError.message,
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imageHash: imageHash // Useful for local tracking
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} as any;
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}
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} catch (error) {
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console.error('Scan Label Global Error:', error);
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return {
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success: false,
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error: error instanceof Error ? error.message : 'Fehler bei der Label-Analyse.',
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};
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}
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}
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