chore: lock AI providers and standardize bouquet images to 3:4

src/lib/server/flowerFlow/selectionMask.js

@@ -178,54 +178,14 @@ export function buildOpenAIEditMask(width, height, selection) {
 	return encodePng(width, height, rgba);
 }
 
-/**
- * Visual mask for Gemini: white polygon on black = edit region.
- * @param {number} width
- * @param {number} height
- * @param {Array<{ x: number, y: number }>} selection
- */
-export function buildGeminiEditMask(width, height, selection) {
-	const polygon = closePolygon(
-		selection.map((point) => ({
-			x: (point.x / 100) * width,
-			y: (point.y / 100) * height
-		}))
-	);
-
-	const rgba = new Uint8Array(width * height * 4);
-	for (let y = 0; y < height; y += 1) {
-		for (let x = 0; x < width; x += 1) {
-			const index = (y * width + x) * 4;
-			const inside = pointInPolygon(x + 0.5, y + 0.5, polygon);
-			if (inside) {
-				rgba[index] = 255;
-				rgba[index + 1] = 255;
-				rgba[index + 2] = 255;
-				rgba[index + 3] = 255;
-			} else {
-				rgba[index] = 0;
-				rgba[index + 1] = 0;
-				rgba[index + 2] = 0;
-				rgba[index + 3] = 255;
-			}
-		}
-	}
-
-	return encodePng(width, height, rgba);
-}
-
 /**
  * @param {{ base64: string, mimeType: string }} sourceImage
  * @param {Array<{ x: number, y: number }>} selection
- * @param {'openai' | 'gemini'} provider
  */
-export function buildAreaEditMask(sourceImage, selection, provider) {
+export function buildAreaEditMask(sourceImage, selection) {
 	const buffer = Buffer.from(sourceImage.base64, 'base64');
 	const { width, height } = readImageDimensions(buffer, sourceImage.mimeType);
-	const maskBuffer =
-		provider === 'gemini'
-			? buildGeminiEditMask(width, height, selection)
-			: buildOpenAIEditMask(width, height, selection);
+	const maskBuffer = buildOpenAIEditMask(width, height, selection);
 
 	return {
 		base64: maskBuffer.toString('base64'),

src/lib/server/gemini/client.js

@@ -31,12 +31,6 @@ export function getVisionModel() {
 	});
 }
 
-export function getImageModel() {
-	return getClient().getGenerativeModel({
-		model: env.GEMINI_IMAGE_MODEL || 'gemini-3.1-flash-image'
-	});
-}
-
 /**
  * @param {string} text
  */

src/lib/server/gemini/image.js

@@ -1,42 +1,11 @@
 /** @typedef {import('../flowerFlow/jobStore.js').GeneratedImage} GeneratedImage */
 
-import { env } from '$env/dynamic/private';
 import { BOUQUET_IMAGE_ASPECT_PROMPT } from '../../flowerFlow/bouquetImageFormat.js';
-import { getImageModel, isGeminiConfigured } from './client.js';
 import { mockGeneratedImage } from './mock.js';
 import { generateOpenAIImage, editOpenAIImage, isOpenAIConfigured } from '../openai/image.js';
 
-export function getImageProvider() {
-	const configured = env.IMAGE_PROVIDER?.trim().toLowerCase();
-	if (configured === 'mock' || configured === 'openai' || configured === 'gemini') {
-		return configured;
-	}
-	return isOpenAIConfigured() ? 'openai' : 'gemini';
-}
-
 export function isImageGenerationConfigured() {
-	const provider = getImageProvider();
-	if (provider === 'mock') return false;
-	return provider === 'openai' ? isOpenAIConfigured() : isGeminiConfigured();
-}
-
-/**
- * @param {import('@google/generative-ai').GenerateContentResult} result
- * @returns {GeneratedImage}
- */
-function imageFromGeminiResult(result) {
-	const parts = result.response.candidates?.[0]?.content?.parts ?? [];
-
-	for (const part of parts) {
-		if (part.inlineData?.data) {
-			return {
-				mimeType: part.inlineData.mimeType || 'image/png',
-				base64: part.inlineData.data
-			};
-		}
-	}
-
-	throw new Error('Gemini image model did not return image data');
+	return isOpenAIConfigured();
 }
 
 /**
@@ -47,27 +16,12 @@ function imageFromGeminiResult(result) {
 export async function generateBouquetImage(basePrompt) {
 	const suffix = `Generate one final bouquet image. ${BOUQUET_IMAGE_ASPECT_PROMPT} The STRICT RECIPE flower list above is mandatory: include every listed species and do not add any other flowers. Keep it realistic, orderable from a real florist, front-facing, and suitable for a customer preview.`;
 	const prompt = `${basePrompt}\n\n${suffix}`;
-	const provider = getImageProvider();
 
-	if (provider === 'mock') {
+	if (!isOpenAIConfigured()) {
 		return mockGeneratedImage();
 	}
 
-	if (provider === 'openai') {
-		if (!isOpenAIConfigured()) {
-			return mockGeneratedImage();
-		}
-
-		return generateOpenAIImage(prompt);
-	}
-
-	if (!isGeminiConfigured()) {
-		return mockGeneratedImage();
-	}
-
-	const model = getImageModel();
-	const result = await model.generateContent(prompt);
-	return imageFromGeminiResult(result);
+	return generateOpenAIImage(prompt);
 }
 
 /**
@@ -78,52 +32,11 @@ export async function generateBouquetImage(basePrompt) {
  * @returns {Promise<GeneratedImage>}
  */
 export async function editBouquetImage(sourceImage, editPrompt, options = {}) {
-	const provider = getImageProvider();
 	const mask = options.mask ?? null;
 
-	if (provider === 'mock' || sourceImage.mimeType === 'image/svg+xml') {
+	if (sourceImage.mimeType === 'image/svg+xml' || !isOpenAIConfigured()) {
 		return mockGeneratedImage('Edited bouquet');
 	}
 
-	if (provider === 'openai') {
-		if (!isOpenAIConfigured()) {
-			return mockGeneratedImage('Edited bouquet');
-		}
-
-		return editOpenAIImage(editPrompt, sourceImage, mask);
-	}
-
-	if (!isGeminiConfigured()) {
-		return mockGeneratedImage('Edited bouquet');
-	}
-
-	const model = getImageModel();
-	/** @type {import('@google/generative-ai').Part[]} */
-	const parts = [
-		{ text: editPrompt },
-		{
-			inlineData: {
-				data: sourceImage.base64,
-				mimeType: sourceImage.mimeType
-			}
-		}
-	];
-
-	if (mask) {
-		parts.push(
-			{
-				text: 'This mask marks the edit region. Modify the bouquet photo only where the mask is white. Keep black areas unchanged.'
-			},
-			{
-				inlineData: {
-					data: mask.base64,
-					mimeType: mask.mimeType
-				}
-			}
-		);
-	}
-
-	const result = await model.generateContent(parts);
-
-	return imageFromGeminiResult(result);
+	return editOpenAIImage(editPrompt, sourceImage, mask);
 }

src/lib/server/gemini/mock.js

@@ -66,7 +66,7 @@ export function mockGeneratedImage(label = 'Bouquet') {
 	const svg = `<svg xmlns="http://www.w3.org/2000/svg" width="768" height="1024" viewBox="0 0 768 1024">
 		<rect width="768" height="1024" fill="#f7f3ef"/>
 		<text x="50%" y="48%" text-anchor="middle" font-size="42" fill="#6b5b53" font-family="Arial">Mock ${label}</text>
-		<text x="50%" y="54%" text-anchor="middle" font-size="22" fill="#9a8d84" font-family="Arial">Set GEMINI_API_KEY for real images</text>
+		<text x="50%" y="54%" text-anchor="middle" font-size="22" fill="#9a8d84" font-family="Arial">Set OPENAI_API_KEY for real images</text>
 	</svg>`;
 
 	return {

src/lib/server/openai/bouquetImageFrame.js

@@ -0,0 +1,95 @@
+import sharp from 'sharp';
+
+/** Product bouquet output — 3:4 portrait (matches UI aspect-[3/4] and mock SVG). */
+export const BOUQUET_OUTPUT_WIDTH = 768;
+export const BOUQUET_OUTPUT_HEIGHT = 1024;
+export const BOUQUET_OUTPUT_SIZE = `${BOUQUET_OUTPUT_WIDTH}x${BOUQUET_OUTPUT_HEIGHT}`;
+
+/** Closest portrait size supported by gpt-image-1 (2:3). Cropped to 3:4 after generation. */
+export const OPENAI_REQUEST_WIDTH = 1024;
+export const OPENAI_REQUEST_HEIGHT = 1536;
+export const OPENAI_REQUEST_SIZE = `${OPENAI_REQUEST_WIDTH}x${OPENAI_REQUEST_HEIGHT}`;
+
+const PAD_LEFT = (OPENAI_REQUEST_WIDTH - BOUQUET_OUTPUT_WIDTH) / 2;
+const PAD_TOP = (OPENAI_REQUEST_HEIGHT - BOUQUET_OUTPUT_HEIGHT) / 2;
+
+/**
+ * Center-crop (and resize if needed) to exact 3:4 bouquet output.
+ * @param {Buffer} buffer
+ * @returns {Promise<Buffer>}
+ */
+export async function frameToBouquetOutput(buffer) {
+	const meta = await sharp(buffer).metadata();
+	const width = meta.width ?? OPENAI_REQUEST_WIDTH;
+	const height = meta.height ?? OPENAI_REQUEST_HEIGHT;
+
+	if (width === BOUQUET_OUTPUT_WIDTH && height === BOUQUET_OUTPUT_HEIGHT) {
+		return buffer;
+	}
+
+	const targetRatio = BOUQUET_OUTPUT_WIDTH / BOUQUET_OUTPUT_HEIGHT;
+	let cropWidth = width;
+	let cropHeight = height;
+
+	if (width / height > targetRatio) {
+		cropWidth = Math.round(height * targetRatio);
+	} else {
+		cropHeight = Math.round(width / targetRatio);
+	}
+
+	const left = Math.max(0, Math.round((width - cropWidth) / 2));
+	const top = Math.max(0, Math.round((height - cropHeight) / 2));
+
+	return sharp(buffer)
+		.extract({ left, top, width: cropWidth, height: cropHeight })
+		.resize(BOUQUET_OUTPUT_WIDTH, BOUQUET_OUTPUT_HEIGHT)
+		.png()
+		.toBuffer();
+}
+
+/**
+ * Pad a 3:4 bouquet image to OpenAI's 2:3 request size (white letterbox).
+ * @param {Buffer} buffer
+ * @returns {Promise<Buffer>}
+ */
+export async function padToOpenAIRequestSize(buffer) {
+	const meta = await sharp(buffer).metadata();
+	if (meta.width === OPENAI_REQUEST_WIDTH && meta.height === OPENAI_REQUEST_HEIGHT) {
+		return buffer;
+	}
+
+	return sharp(buffer)
+		.resize(BOUQUET_OUTPUT_WIDTH, BOUQUET_OUTPUT_HEIGHT, { fit: 'fill' })
+		.extend({
+			top: PAD_TOP,
+			bottom: PAD_TOP,
+			left: PAD_LEFT,
+			right: PAD_LEFT,
+			background: { r: 255, g: 255, b: 255, alpha: 1 }
+		})
+		.png()
+		.toBuffer();
+}
+
+/**
+ * Pad an OpenAI edit mask (transparent=edit, opaque=preserve) to the request canvas.
+ * @param {Buffer} maskBuffer
+ * @returns {Promise<Buffer>}
+ */
+export async function padMaskToOpenAIRequestSize(maskBuffer) {
+	const meta = await sharp(maskBuffer).metadata();
+	if (meta.width === OPENAI_REQUEST_WIDTH && meta.height === OPENAI_REQUEST_HEIGHT) {
+		return maskBuffer;
+	}
+
+	return sharp(maskBuffer)
+		.extend({
+			top: PAD_TOP,
+			bottom: PAD_TOP,
+			left: PAD_LEFT,
+			right: PAD_LEFT,
+			background: { r: 255, g: 255, b: 255, alpha: 255 }
+		})
+		.png()
+		.toBuffer();
+}

src/lib/server/openai/image.js

@@ -1,5 +1,11 @@
 import { env } from '$env/dynamic/private';
 import OpenAI, { toFile } from 'openai';
+import {
+	frameToBouquetOutput,
+	padMaskToOpenAIRequestSize,
+	padToOpenAIRequestSize,
+	OPENAI_REQUEST_SIZE
+} from './bouquetImageFrame.js';
 
 let client = null;
 
@@ -19,6 +25,25 @@ function getOpenAIClient() {
 	return client;
 }
 
+/**
+ * @param {import('openai').Images.ImagesResponse['data']} data
+ * @returns {Promise<Buffer>}
+ */
+async function readImageBytes(data) {
+	const image = data?.[0];
+
+	if (image?.b64_json) {
+		return Buffer.from(image.b64_json, 'base64');
+	}
+
+	if (image?.url) {
+		const imageResponse = await fetch(image.url);
+		return Buffer.from(await imageResponse.arrayBuffer());
+	}
+
+	throw new Error('OpenAI image model did not return image data');
+}
+
 /**
  * @param {string} prompt
  * @returns {Promise<import('../flowerFlow/jobStore.js').GeneratedImage>}
@@ -27,30 +52,16 @@ export async function generateOpenAIImage(prompt) {
 	const response = await getOpenAIClient().images.generate({
 		model: env.OPENAI_IMAGE_MODEL || 'gpt-image-1',
 		prompt,
-		size: env.OPENAI_IMAGE_SIZE || '1024x1536',
+		size: OPENAI_REQUEST_SIZE,
 		n: 1
 	});
 
-	const image = response.data?.[0];
+	const framed = await frameToBouquetOutput(await readImageBytes(response.data));
 
-	if (image?.b64_json) {
-		return {
-			mimeType: 'image/png',
-			base64: image.b64_json
-		};
-	}
-
-	if (image?.url) {
-		const imageResponse = await fetch(image.url);
-		const bytes = new Uint8Array(await imageResponse.arrayBuffer());
-
-		return {
-			mimeType: imageResponse.headers.get('content-type') || 'image/png',
-			base64: Buffer.from(bytes).toString('base64')
-		};
-	}
-
-	throw new Error('OpenAI image model did not return image data');
+	return {
+		mimeType: 'image/png',
+		base64: framed.toString('base64')
+	};
 }
 
 /**
@@ -60,45 +71,30 @@ export async function generateOpenAIImage(prompt) {
  * @returns {Promise<import('../flowerFlow/jobStore.js').GeneratedImage>}
  */
 export async function editOpenAIImage(prompt, sourceImage, mask = null) {
-	const buffer = Buffer.from(sourceImage.base64, 'base64');
-	const imageFile = await toFile(buffer, 'bouquet.png', { type: sourceImage.mimeType });
+	const paddedSource = await padToOpenAIRequestSize(
+		Buffer.from(sourceImage.base64, 'base64')
+	);
+	const imageFile = await toFile(paddedSource, 'bouquet.png', { type: 'image/png' });
 
 	/** @type {import('openai').default.Images.ImageEditParams} */
 	const params = {
 		model: env.OPENAI_IMAGE_MODEL || 'gpt-image-1',
 		image: imageFile,
 		prompt,
-		size: env.OPENAI_IMAGE_SIZE || '1024x1536',
+		size: OPENAI_REQUEST_SIZE,
 		n: 1
 	};
 
 	if (mask) {
-		const maskFile = await toFile(Buffer.from(mask.base64, 'base64'), 'mask.png', {
-			type: 'image/png'
-		});
-		params.mask = maskFile;
+		const paddedMask = await padMaskToOpenAIRequestSize(Buffer.from(mask.base64, 'base64'));
+		params.mask = await toFile(paddedMask, 'mask.png', { type: 'image/png' });
 	}
 
 	const response = await getOpenAIClient().images.edit(params);
+	const framed = await frameToBouquetOutput(await readImageBytes(response.data));
 
-	const image = response.data?.[0];
-
-	if (image?.b64_json) {
-		return {
-			mimeType: 'image/png',
-			base64: image.b64_json
-		};
-	}
-
-	if (image?.url) {
-		const imageResponse = await fetch(image.url);
-		const bytes = new Uint8Array(await imageResponse.arrayBuffer());
-
-		return {
-			mimeType: imageResponse.headers.get('content-type') || 'image/png',
-			base64: Buffer.from(bytes).toString('base64')
-		};
-	}
-
-	throw new Error('OpenAI image edit did not return image data');
+	return {
+		mimeType: 'image/png',
+		base64: framed.toString('base64')
+	};
 }

src/routes/api/flower-flow/edit-images/+server.js

@@ -4,11 +4,7 @@ import { buildAreaEditMask } from '$lib/server/flowerFlow/selectionMask.js';
 import { uploadGeneratedImages } from '$lib/server/flowerFlow/imageStorage.js';
 import { formatBouquetEditPrompt } from '$lib/flowerFlow/bouquetImageFormat.js';
 import { normalizeRecipeLists } from '$lib/flowerFlow/resolveRecipeFlowers.js';
-import {
-	editBouquetImage,
-	getImageProvider,
-	isImageGenerationConfigured
-} from '$lib/server/gemini/image.js';
+import { editBouquetImage, isImageGenerationConfigured } from '$lib/server/gemini/image.js';
 import { applyRecipeEdit } from '$lib/server/gemini/text.js';
 import { RATE_LIMITS } from '$lib/server/rateLimit.js';
 import { enforceRateLimit, json, readJsonBody, toErrorResponse } from '$lib/server/http.js';
@@ -58,18 +54,13 @@ function editForJob(jobId, job, instruction) {
 			recipeChanged
 		});
 
-		const provider = getImageProvider();
 		const mask =
 			instruction.mode === 'area' && instruction.selection.length >= 3
-				? buildAreaEditMask(
-						sourceImage,
-						instruction.selection,
-						provider === 'gemini' ? 'gemini' : 'openai'
-					)
+				? buildAreaEditMask(sourceImage, instruction.selection)
 				: null;
 
 		console.log(
-			`[flower-flow] edit-images job=${jobId.slice(0, 8)} provider=${provider} mode=${instruction.mode}${mask ? ' (masked)' : ''} → editing...`
+			`[flower-flow] edit-images job=${jobId.slice(0, 8)} mode=${instruction.mode}${mask ? ' (masked)' : ''} → editing...`
 		);
 		const generatedImage = await editBouquetImage(sourceImage, editPrompt, { mask });
 		const images = await uploadGeneratedImages(

src/routes/api/flower-flow/generate-images/+server.js

@@ -1,11 +1,7 @@
 import { requireJob, updateJob } from '$lib/server/flowerFlow/jobStore.js';
 import { normalizeRecipeLists } from '$lib/flowerFlow/resolveRecipeFlowers.js';
 import { buildImagePrompt } from '$lib/server/gemini/text.js';
-import {
-	generateBouquetImage,
-	getImageProvider,
-	isImageGenerationConfigured
-} from '$lib/server/gemini/image.js';
+import { generateBouquetImage, isImageGenerationConfigured } from '$lib/server/gemini/image.js';
 import { uploadGeneratedImages } from '$lib/server/flowerFlow/imageStorage.js';
 import { RATE_LIMITS } from '$lib/server/rateLimit.js';
 import { json, readJsonBody, enforceRateLimit, toErrorResponse } from '$lib/server/http.js';
@@ -80,9 +76,7 @@ export async function POST({ request, getClientAddress }) {
 			});
 		}
 
-		console.log(
-			`[flower-flow] generate-images job=${jobId.slice(0, 8)} provider=${getImageProvider()} → generating...`
-		);
+		console.log(`[flower-flow] generate-images job=${jobId.slice(0, 8)} → generating...`);
 		const { imagePrompt, images, recipe: savedRecipe } = await generateForJob(jobId, job.recipe);
 		console.log(
 			`[flower-flow] generate-images job=${jobId.slice(0, 8)} OK (mock=${!isImageGenerationConfigured()})`