aún tratando de que funcione, añadido servicio

Author: agarnung

package-lock.json

@@ -13,8 +13,8 @@
   "dependencies": {
     "@astrojs/svelte": "^7.0.4",
     "@astrojs/vercel": "^8.0.4",
+    "@huggingface/inference": "^1.8.0",
     "astro": "^5.1.9",
-    "dotenv": "^16.4.5",
-    "@huggingface/inference": "^1.0.0"
+    "dotenv": "^16.4.7"
   }
 }

package.json

@@ -36,6 +36,10 @@
         body: JSON.stringify({ word1: userInput, word2: riddle.solution }),
       });
 
+      if (!res.ok) {
+        throw new Error(`HTTP error! status: ${res.status}`);
+      }
+
       const data = await res.json();
 
       if (data.error) {

src/components/Riddles.svelte

@@ -0,0 +1,34 @@
+export async function getEmbeddings(text: string): Promise<number[]> {
+  try {
+    const response = await fetch('https://api-inference.huggingface.co/pipeline/feature-extraction/sentence-transformers/all-MiniLM-L6-v2', {
+      method: 'POST',
+      headers: {
+        Authorization: `Bearer ${process.env.HF_ACCESS_TOKEN}`,
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({ inputs: text }),
+    });
+
+	if (!response.ok) {
+	  const errorText = await response.text();
+	  console.error(`Error from Hugging Face API: ${response.status} ${response.statusText} - ${errorText}`);
+	  throw new Error(`API Error: ${response.status} ${response.statusText} - ${errorText}`);
+	}
+
+    const result = await response.json();
+
+    if (Array.isArray(result) && result.length > 0) {
+      if (Array.isArray(result[0]) && result[0].every((item) => typeof item === 'number')) {
+        return result[0];
+      }
+      if (result.every((item) => typeof item === 'number')) {
+        return result;
+      }
+    }
+
+    throw new Error("Unexpected response format. Full response: " + JSON.stringify(result));
+  } catch (error) {
+    console.error('Error extracting features:', error.message);
+    throw error;
+  }
+}

src/lib/embeddingService.ts

@@ -1,11 +1,10 @@
 import type { APIRoute } from 'astro';
 import dotenv from 'dotenv';
-import fetch from 'node-fetch';
+import { getEmbeddings } from '../../lib/embeddingService';
 
 dotenv.config();
 
-const hfApiUrl = 'https://api-inference.huggingface.co/pipeline/feature-extraction/sentence-transformers/all-MiniLM-L6-v2';
-const hfAccessToken = process.env.HUGGING_FACE_API_KEY;
+const HF_ACCESS_TOKEN = process.env.HF_ACCESS_TOKEN;
 
 function singleSoftmax(value: number): number {
   const scaledValue = (value - 0.5) * 10;
@@ -23,35 +22,6 @@ function cosineSimilarity(vectorA: number[], vectorB: number[]): number {
   return singleSoftmax(similarity) * 100;
 }
 
-async function extractFeatures(text: string): Promise<number[]> {
-  const response = await fetch(hfApiUrl, {
-    method: 'POST',
-    headers: {
-      Authorization: `Bearer ${hfAccessToken}`,
-      'Content-Type': 'application/json',
-    },
-    body: JSON.stringify({ inputs: text }),
-  });
-
-  if (!response.ok) {
-    const errorText = await response.text();
-    throw new Error(`API Error: ${response.status} ${response.statusText} - ${errorText}`);
-  }
-
-  const result = await response.json();
-
-  if (Array.isArray(result) && result.length > 0) {
-    if (Array.isArray(result[0]) && result[0].every((item) => typeof item === 'number')) {
-      return result[0];
-    }
-    if (result.every((item) => typeof item === 'number')) {
-      return result;
-    }
-  }
-
-  throw new Error("Unexpected response format. Full response: " + JSON.stringify(result));
-}
-
 export const post: APIRoute = async ({ request }) => {
   const { word1, word2 } = await request.json();
 
@@ -60,8 +30,8 @@ export const post: APIRoute = async ({ request }) => {
   }
 
   try {
-    const vectorA = await extractFeatures(word1);
-    const vectorB = await extractFeatures(word2);
+    const vectorA = await getEmbeddings(word1);
+    const vectorB = await getEmbeddings(word2);
 
     const cosineSim = cosineSimilarity(vectorA, vectorB);
 

src/pages/api/compare.ts

@@ -0,0 +1,5 @@
+import { vitePreprocess } from '@astrojs/svelte';
+
+export default {
+	preprocess: vitePreprocess(),
+}