implemented a simple AI-model integration pipeline

app/imageSearch/page.tsx

@@ -0,0 +1,49 @@
+// app/imageSearch/page.tsx
+"use client";
+
+import React, { useState } from "react";
+import { getImageEmbeddings } from "../lib/modelClient";
+
+export default function ImageSearchPage() {
+  const [imageFile, setImageFile] = useState<File | null>(null);
+  const [results, setResults] = useState<number[] | null>(null);
+  const [loading, setLoading] = useState(false);
+
+  async function handleFileChange(e: React.ChangeEvent<HTMLInputElement>) {
+    if (!e.target.files || e.target.files.length === 0) return;
+    setImageFile(e.target.files[0]);
+  }
+
+  async function handleSearch() {
+    if (!imageFile) return;
+    setLoading(true);
+    try {
+      // For "image search," we get the embeddings from the local pipeline
+      const embeddingResult = await getImageEmbeddings(imageFile);
+      setResults(embeddingResult);
+      // In a real scenario, we'd compare these embeddings against 
+      // known embeddings in our dataset to find the closest match.
+    } catch (error) {
+      console.error("Error running inference:", error);
+    } finally {
+      setLoading(false);
+    }
+  }
+
+  return (
+    <main style={{ padding: 20 }}>
+      <h1>Local Image Search (CLIP Embeddings)</h1>
+      <input type="file" accept="image/*" onChange={handleFileChange} />
+      <button onClick={handleSearch} disabled={!imageFile}>
+        {loading ? "Searching..." : "Get Image Embeddings"}
+      </button>
+
+      {results && (
+        <div>
+          <h2>Embedding Vector:</h2>
+          <pre>{JSON.stringify(results, null, 2)}</pre>
+        </div>
+      )}
+    </main>
+  );
+}

app/lib/modelClient.ts

@@ -0,0 +1,33 @@
+// lib/modelClient.ts
+"use client";
+
+import { pipeline } from "@xenova/transformers";
+
+/** Singleton references to loaded pipelines */
+let imageEmbedder: any = null;
+
+/**
+ * loading a CLIP embedding pipeline for image search (example).
+ * this will make it easy to use other models
+ */
+export async function loadEmbeddingPipeline() {
+  if (!imageEmbedder) {
+    // Using CLIP for image embeddings, just as an example
+    imageEmbedder = await pipeline(
+      "feature-extraction",
+      "Xenova/clip-vit-base-patch32"
+    );
+  }
+  return imageEmbedder;
+}
+
+/**
+ * Extract embeddings from an image. 
+ * Returns a vector (array of floats) we can then compare with our dataset.
+ */
+export async function getImageEmbeddings(image: Blob | string) {
+  const embedder = await loadEmbeddingPipeline();
+  // The pipeline returns a nested array. We'll flatten or keep it nested as needed.
+  const result = await embedder(image);
+  return result; 
+}

package.json

@@ -14,6 +14,7 @@
     "@capacitor/cli": "^7.0.1",
     "@capacitor/core": "^7.0.1",
     "@capacitor/ios": "^7.0.1",
+    "@xenova/transformers": "^2.17.2",
     "next": "^14.2.23",
     "react": "^18",
     "react-dom": "^18"