+-----------------------------------------------------------------------------------------+
|                  Cloud API vs On-Device In-Browser AI Architecture                      |
+-----------------------------------------------------------------------------------------+

CLOUD-HOSTED LLM ARCHITECTURE (High Latency, High Cost, Privacy Risk):
[Browser Client] ──(HTTP POST + Sensitive Data)──> [INTERNET] ──> [Centralized Cloud GPUs]
                                                                        │ ($0.03 / 1k Tokens)
                                                                        ▼
* Network Latency: 800ms; Privacy Compliance Violations; Monthly API Bill: $50,000+!

ON-DEVICE IN-BROWSER AI (2026 Standard - 100% Private, Zero API Costs):
[Browser Client (React 19)]
  │
  ├── 1. Model Weights cached in Browser IndexedDB (Downloaded ONCE!).
  │
  ▼ (Direct WebGPU Kernel Dispatch)
[LOCAL CLIENT GPU SILICON (Apple M4 / NVIDIA RTX / Qualcomm)]:
  ├── Executes 4-bit Quantized Matrix Multiplications in VRAM.
  ├── Streams tokens at 45+ Tokens/Second directly to UI!
  └── ZERO Network Packets Transmitted! $0.00 Cloud Invoices!

+-----------------------------------------------------------------------------------------+
|                  On-Device In-Browser AI Frameworks Matrix                              |
+-----------------------------------------------------------------------------------------+

WEBLLM (By MLC AI / Apache TVM)
- Specialization: Large Language Models (LLMs) and conversational text generation.
- Engine: Compiles models into native WGSL WebGPU compute shaders.
- Supported Models: Llama 3.2 (1B/3B), Gemma 2 (2B), Mistral 7B 4-bit, SmolLM2.
- Best for: Chatbots, writing assistants, private code review tools.

TRANSFORMERS.JS v3 (Hugging Face)
- Specialization: Multi-modal AI (Embeddings, Whisper ASR, Computer Vision, Translation).
- Engine: ONNX Runtime Web with WebGPU / Wasm execution providers.
- Supported Models: BGE-micro embeddings, Whisper-tiny, Xenova CLIP, Depth Anything.
- Best for: In-browser vector search, real-time voice-to-text, background removal.

// components/PrivateLocalAI.tsx
"use client";

import React, { useState, useEffect } from "react";
import * as webllm from "@mlc-ai/web-llm";

export function PrivateLocalAI() {
  const [engine, setEngine] = useState<webllm.MLCEngine | null>(null);
  const [loadProgress, setLoadProgress] = useState<string>("Initializing WebGPU...");
  const [isModelReady, setIsModelReady] = useState<boolean>(false);
  const [prompt, setPrompt] = useState<string>("");
  const [completion, setCompletion] = useState<string>("");
  const [isGenerating, setIsGenerating] = useState<boolean>(false);

  useEffect(() => {
    async function initModel() {
      // 1. Configure WebLLM Engine with Auto-IndexedDB Weight Caching!
      const initProgressCallback = (report: webllm.InitProgressReport) => {
        setLoadProgress(report.text);
      };

      // Select high-performance 4-bit quantized model (Only ~900MB download!)
      const selectedModel = "Llama-3.2-1B-Instruct-q4f16_1-MLC";
      
      const mlcEngine = await webllm.CreateMLCEngine(selectedModel, {
        initProgressCallback,
        logLevel: "WARN",
      });

      setEngine(mlcEngine);
      setIsModelReady(true);
    }

    initModel();
  }, []);

  const handleGenerate = async () => {
    if (!engine || !prompt || isGenerating) return;

    setIsGenerating(true);
    setCompletion("");

    // 2. OpenAI-Compatible Chat Streaming API
    const messages: webllm.ChatCompletionMessageParam[] = [
      { role: "system", content: "You are a private, ultra-fast on-device AI assistant running entirely inside the user's browser via WebGPU." },
      { role: "user", content: prompt },
    ];

    const chunks = await engine.chat.completions.create({
      messages,
      temperature: 0.7,
      max_tokens: 512,
      stream: true, // Hardware-accelerated token streaming!
    });

    for await (const chunk of chunks) {
      const delta = chunk.choices[0]?.delta?.content || "";
      setCompletion((prev) => prev + delta);
    }

    setIsGenerating(false);
  };

  return (
    <div className="p-6 bg-neutral-900 border border-neutral-800 rounded-xl text-white max-w-2xl mx-auto">
      <div className="flex items-center justify-between mb-4">
        <h2 className="text-xl font-bold">🔒 100% Private On-Device AI</h2>
        <span className={`px-2.5 py-1 text-xs font-semibold rounded-full ${isModelReady ? "bg-emerald-950 text-emerald-400 border border-emerald-800" : "bg-yellow-950 text-yellow-400"}`}>
          {isModelReady ? "WebGPU Engine Ready" : "Loading Model"}
        </span>
      </div>

      {!isModelReady ? (
        <div className="p-4 bg-neutral-800 rounded-lg text-sm text-neutral-300 font-mono">
          <p className="animate-pulse">{loadProgress}</p>
        </div>
      ) : (
        <div>
          <textarea
            value={prompt}
            onChange={(e) => setPrompt(e.target.value)}
            placeholder="Ask anything (Medical records, confidential code, financial calculations)..."
            className="w-full p-3 bg-neutral-800 border border-neutral-700 rounded-lg mb-3 text-sm focus:outline-none focus:border-red-600"
            rows={3}
          />
          <button
            onClick={handleGenerate}
            disabled={isGenerating}
            className="px-5 py-2 bg-red-600 hover:bg-red-700 disabled:opacity-50 rounded-lg font-semibold text-sm transition-colors"
          >
            {isGenerating ? "Generating on Local GPU..." : "Generate Offline"}
          </button>

          {completion && (
            <div className="mt-4 p-4 bg-neutral-950 border border-neutral-800 rounded-lg">
              <h4 className="text-xs font-bold text-neutral-500 uppercase tracking-wider mb-2">Local GPU Output</h4>
              <p className="text-sm text-neutral-200 leading-relaxed whitespace-pre-wrap">{completion}</p>
            </div>
          )}
        </div>
      )}
    </div>
  );
}

// services/localEmbedder.ts
import { pipeline, env } from "@huggingface/transformers";

// 1. Enable Hardware WebGPU Backend in ONNX Runtime Web
env.backends.onnx.wasm.numThreads = 4;
env.allowLocalModels = false;

export class LocalSemanticSearch {
  private static extractorInstance: any = null;

  static async getExtractor() {
    if (!this.extractorInstance) {
      // Load compact, ultra-fast embedding model with WebGPU acceleration
      this.extractorInstance = await pipeline("feature-extraction", "Xenova/all-MiniLM-L6-v2", {
        device: "webgpu",
        dtype: "fp16", // Half-precision floating point for 2x faster math!
      });
    }
    return this.extractorInstance;
  }

  static async generateEmbedding(text: string): Promise<Float32Array> {
    const extractor = await this.getExtractor();
    const output = await extractor(text, { pooling: "mean", normalize: true });
    return output.data as Float32Array;
  }
}

+-----------------------------------------------------------------------------------------+
|                  IndexedDB Model Weight Caching Lifecycle                               |
+-----------------------------------------------------------------------------------------+

FIRST VISIT:
[Browser] ──(Downloads 900MB Quantized Model)──> [Saves Shards to IndexedDB Storage]

SUBSEQUENT VISITS (100% OFFLINE!):
[Browser Opens] ──> [Loads Weights from Local SSD / Flash Storage in < 800ms!]
                ──> [Initializes WebGPU Engine Instantly with ZERO Internet Connection!]

       +-------------------------------------------------------------+
       |             Time to First Token (TTFT Latency - ms)         |
       +-------------------------------------------------------------+
 Cloud LLM API (Network Roundtrip)    | ==================================== [920.0 ms]
 On-Device WebGPU (Llama 3.2 1B M4)   | = [22.0 ms] (40x Faster Initial Response!)
                                      +-------------------------------------+
                                      0ms     250ms   500ms   750ms   1000ms

       +-------------------------------------------------------------+
       |             Monthly Cloud API Bill for 10M Inferences       |
       +-------------------------------------------------------------+
 Centralized Cloud LLM Endpoints      | ==================================== [$18,500.00]
 On-Device In-Browser AI (WebGPU)     | = [$0.00] (100% Free Scaling!)
                                      +-------------------------------------+
                                      $0      $5000   $10000  $15000  $20000