add global server cache

src/routes/api/models/+server.ts

@@ -1,9 +1,39 @@
 import type { Model, ModelWithTokenizer } from "$lib/types.js";
 import { json } from "@sveltejs/kit";
 import type { RequestHandler } from "./$types.js";
-import { dev } from "$app/environment";
 
-let cache: ModelWithTokenizer[] | undefined;
+enum CacheStatus {
+	SUCCESS = "success",
+	PARTIAL = "partial",
+	ERROR = "error",
+}
+
+type Cache = {
+	data: ModelWithTokenizer[] | undefined;
+	timestamp: number;
+	status: CacheStatus;
+	// Track failed models to selectively refetch them
+	failedTokenizers: string[]; // Using array instead of Set for serialization compatibility
+	failedApiCalls: {
+		textGeneration: boolean;
+		imageTextToText: boolean;
+	};
+};
+
+const cache: Cache = {
+	data: undefined,
+	timestamp: 0,
+	status: CacheStatus.ERROR,
+	failedTokenizers: [],
+	failedApiCalls: {
+		textGeneration: false,
+		imageTextToText: false,
+	},
+};
+
+// The time between cache refreshes
+const FULL_CACHE_REFRESH = 1000 * 60 * 60; // 1 hour
+const PARTIAL_CACHE_REFRESH = 1000 * 60 * 15; // 15 minutes (shorter for partial results)
 
 const headers: HeadersInit = {
 	"Upgrade-Insecure-Requests": "1",
@@ -42,80 +72,228 @@ const baseUrl = "https://huggingface.co/api/models";
 
 function buildApiUrl(params: ApiQueryParams): string {
 	const url = new URL(baseUrl);
+
 	// Add simple params
 	Object.entries(params).forEach(([key, value]) => {
 		if (!Array.isArray(value)) {
 			url.searchParams.append(key, String(value));
 		}
 	});
+
 	// Handle array params specially
 	params.expand.forEach(item => {
 		url.searchParams.append("expand[]", item);
 	});
+
 	return url.toString();
 }
 
 export const GET: RequestHandler = async ({ fetch }) => {
-	if (cache?.length && dev) {
-		console.log("Skipping load, using in memory cache");
-		return json(cache);
+	const timestamp = Date.now();
+
+	// Determine if cache is valid
+	const elapsed = timestamp - cache.timestamp;
+	const cacheRefreshTime = cache.status === CacheStatus.SUCCESS ? FULL_CACHE_REFRESH : PARTIAL_CACHE_REFRESH;
+
+	// Use cache if it's still valid and has data
+	if (elapsed < cacheRefreshTime && cache.data?.length) {
+		console.log(`Using ${cache.status} cache (${Math.floor(elapsed / 1000 / 60)} min old)`);
+		return json(cache.data);
 	}
 
 	try {
-		// Fetch both types of models in parallel
-		const textGenPromise = fetch(buildApiUrl({ ...queryParams, pipeline_tag: "text-generation" }), requestInit);
-		const imgText2TextPromise = fetch(buildApiUrl({ ...queryParams, pipeline_tag: "image-text-to-text" }), requestInit);
-		const [textGenResponse, imgText2TextResponse] = await Promise.all([textGenPromise, imgText2TextPromise]);
+		// Determine which API calls we need to make based on cache status
+		const needTextGenFetch = elapsed >= FULL_CACHE_REFRESH || cache.failedApiCalls.textGeneration;
+		const needImgTextFetch = elapsed >= FULL_CACHE_REFRESH || cache.failedApiCalls.imageTextToText;
 
-		if (!textGenResponse.ok) {
-			console.error(`Error fetching text-generation models`, textGenResponse.status, textGenResponse.statusText);
+		// Track the existing models we'll keep
+		const existingModels = new Map<string, ModelWithTokenizer>();
+		if (cache.data) {
+			cache.data.forEach(model => {
+				existingModels.set(model.id, model);
+			});
+		}
+
+		// Initialize new tracking for failed requests
+		const newFailedTokenizers: string[] = [];
+		const newFailedApiCalls = {
+			textGeneration: false,
+			imageTextToText: false,
+		};
+
+		// Fetch models as needed
+		let textGenModels: Model[] = [];
+		let imgText2TextModels: Model[] = [];
+
+		// Make the needed API calls in parallel
+		const apiPromises: Promise<Response | void>[] = [];
+		if (needTextGenFetch) {
+			apiPromises.push(
+				fetch(buildApiUrl({ ...queryParams, pipeline_tag: "text-generation" }), requestInit).then(async response => {
+					if (!response.ok) {
+						console.error(`Error fetching text-generation models`, response.status, response.statusText);
+						newFailedApiCalls.textGeneration = true;
+					} else {
+						textGenModels = await response.json();
+					}
+				})
+			);
 		}
 
-		if (!imgText2TextResponse.ok) {
-			console.error(
-				`Error fetching image-text-to-text models`,
-				imgText2TextResponse.status,
-				imgText2TextResponse.statusText
+		if (needImgTextFetch) {
+			apiPromises.push(
+				fetch(buildApiUrl({ ...queryParams, pipeline_tag: "image-text-to-text" }), requestInit).then(async response => {
+					if (!response.ok) {
+						console.error(`Error fetching image-text-to-text models`, response.status, response.statusText);
+						newFailedApiCalls.imageTextToText = true;
+					} else {
+						imgText2TextModels = await response.json();
+					}
+				})
 			);
 		}
 
-		// Parse the responses
-		const textGenModels: Model[] = textGenResponse.ok ? await textGenResponse.json() : [];
-		const imgText2TextModels: Model[] = imgText2TextResponse.ok ? await imgText2TextResponse.json() : [];
+		await Promise.all(apiPromises);
 
-		// Combine the models
-		const compatibleModels: Model[] = [...textGenModels, ...imgText2TextModels];
+		// If both needed API calls failed and we have cached data, use it
+		if (
+			needTextGenFetch &&
+			newFailedApiCalls.textGeneration &&
+			needImgTextFetch &&
+			newFailedApiCalls.imageTextToText &&
+			cache.data?.length
+		) {
+			console.log("All API requests failed. Using existing cache as fallback.");
+			cache.status = CacheStatus.ERROR;
+			cache.timestamp = timestamp; // Update timestamp to avoid rapid retry loops
+			cache.failedApiCalls = newFailedApiCalls;
+			return json(cache.data);
+		}
 
-		// Sort the models
-		compatibleModels.sort((a, b) => a.id.toLowerCase().localeCompare(b.id.toLowerCase()));
+		// For API calls we didn't need to make, use cached models
+		if (!needTextGenFetch && cache.data) {
+			textGenModels = cache.data.filter(model => model.pipeline_tag === "text-generation").map(model => model as Model);
+		}
 
-		// Fetch tokenizer configs for each model
-		const promises = compatibleModels.map(async model => {
-			const configUrl = `https://huggingface.co/${model.id}/raw/main/tokenizer_config.json`;
-			const res = await fetch(configUrl, {
-				credentials: "include",
-				headers,
-				method: "GET",
-				mode: "cors",
-			});
+		if (!needImgTextFetch && cache.data) {
+			imgText2TextModels = cache.data
+				.filter(model => model.pipeline_tag === "image-text-to-text")
+				.map(model => model as Model);
+		}
 
-			if (!res.ok) {
-				// console.error(`Error fetching tokenizer file for ${model.id}`, res.status, res.statusText);
-				return null; // Ignore failed requests by returning null
+		const allModels: Model[] = [...textGenModels, ...imgText2TextModels];
+
+		const modelsNeedingTokenizer: Model[] = [];
+
+		// First, use existing model data when possible
+		allModels.forEach(model => {
+			const existingModel = existingModels.get(model.id);
+
+			// Only fetch tokenizer if:
+			// 1. We don't have this model yet, OR
+			// 2. It's in our failed tokenizers list AND we're doing a refresh, OR
+			// 3. We're doing a full refresh
+			if (
+				!existingModel ||
+				(cache.failedTokenizers.includes(model.id) && elapsed >= PARTIAL_CACHE_REFRESH) ||
+				elapsed >= FULL_CACHE_REFRESH
+			) {
+				modelsNeedingTokenizer.push(model);
 			}
+		});
+
+		console.log(`Total models: ${allModels.length}, Models needing tokenizer fetch: ${modelsNeedingTokenizer.length}`);
+
+		// Prepare result - start with existing models we want to keep
+		const models: ModelWithTokenizer[] = [];
 
-			const tokenizerConfig = await res.json();
-			return { ...model, tokenizerConfig } satisfies ModelWithTokenizer;
+		// Add models we're not re-fetching tokenizers for
+		allModels.forEach(model => {
+			const existingModel = existingModels.get(model.id);
+			if (existingModel && !modelsNeedingTokenizer.some(m => m.id === model.id)) {
+				models.push(existingModel);
+			}
 		});
 
-		const models: ModelWithTokenizer[] = (await Promise.all(promises)).filter(
-			(model): model is ModelWithTokenizer => model !== null
+		// Fetch tokenizer configs only for models that need it, with concurrency limit
+		const batchSize = 10; // Limit concurrent requests
+
+		for (let i = 0; i < modelsNeedingTokenizer.length; i += batchSize) {
+			const batch = modelsNeedingTokenizer.slice(i, i + batchSize);
+			const batchPromises = batch.map(async model => {
+				try {
+					const configUrl = `https://huggingface.co/${model.id}/raw/main/tokenizer_config.json`;
+					const res = await fetch(configUrl, {
+						credentials: "include",
+						headers,
+						method: "GET",
+						mode: "cors",
+					});
+
+					if (!res.ok) {
+						if (!newFailedTokenizers.includes(model.id)) {
+							newFailedTokenizers.push(model.id);
+						}
+						return null;
+					}
+
+					const tokenizerConfig = await res.json();
+					return { ...model, tokenizerConfig } satisfies ModelWithTokenizer;
+				} catch (error) {
+					console.error(`Error processing tokenizer for ${model.id}:`, error);
+					if (!newFailedTokenizers.includes(model.id)) {
+						newFailedTokenizers.push(model.id);
+					}
+					return null;
+				}
+			});
+
+			const batchResults = await Promise.all(batchPromises);
+			models.push(...batchResults.filter((model): model is ModelWithTokenizer => model !== null));
+		}
+
+		models.sort((a, b) => a.id.toLowerCase().localeCompare(b.id.toLowerCase()));
+
+		// Determine cache status based on failures
+		const hasApiFailures = newFailedApiCalls.textGeneration || newFailedApiCalls.imageTextToText;
+		const hasSignificantTokenizerFailures = newFailedTokenizers.length > modelsNeedingTokenizer.length * 0.2;
+
+		const cacheStatus = hasApiFailures || hasSignificantTokenizerFailures ? CacheStatus.PARTIAL : CacheStatus.SUCCESS;
+
+		cache.data = models;
+		cache.timestamp = timestamp;
+		cache.status = cacheStatus;
+		cache.failedTokenizers = newFailedTokenizers;
+		cache.failedApiCalls = newFailedApiCalls;
+
+		console.log(
+			`Cache updated: ${models.length} models, status: ${cacheStatus}, ` +
+				`failed tokenizers: ${newFailedTokenizers.length}, ` +
+				`API failures: text=${newFailedApiCalls.textGeneration}, img=${newFailedApiCalls.imageTextToText}`
 		);
-		cache = models;
 
-		return json(cache);
+		return json(models);
 	} catch (error) {
 		console.error("Error fetching models:", error);
+
+		// If we have cached data, use it as fallback
+		if (cache.data?.length) {
+			cache.status = CacheStatus.ERROR;
+			// Mark all API calls as failed so we retry them next time
+			cache.failedApiCalls = {
+				textGeneration: true,
+				imageTextToText: true,
+			};
+			return json(cache.data);
+		}
+
+		// No cache available, return empty array
+		cache.status = CacheStatus.ERROR;
+		cache.timestamp = timestamp;
+		cache.failedApiCalls = {
+			textGeneration: true,
+			imageTextToText: true,
+		};
 		return json([]);
 	}
 };