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Building ChatGpMe: AI Integration with Cloudflare Workers

I built ChatGpMe as an experiment to explore Cloudflare’s AI capabilities. Here’s a detailed look at how I implemented both text and image generation features.

Chat Implementation

The chat system uses streaming responses for a better user experience. Here’s the core implementation:

import { Ai } from '@cloudflare/ai';

export async function onRequest({ request, env }) {
  const model = "@cf/meta/llama-3.3-70b-instruct-fp8-fast";
  const { messages, prompt } = await request.json();
  
  const response = await env.AI.run(
    model,
    { messages, stream: true },
    { gateway: { id: 'martingpt', skipCache: true } }
  );

  const stream = response
    .pipeThrough(new TextDecoderStream())
    .pipeThrough(new SSEToStream())
    .pipeThrough(new TextEncoderStream());

  return new Response(stream, {
    headers: { 'content-type': 'application/x-ndjson' },
  });
}

Image Generation

I implemented image generation using the Flux model from Black Forest Labs, available through Cloudflare Workers AI:

export async function generateImage({ request, env }) {
  const { prompt } = await request.json();
  const gateway_id = "martingpt";

  const response = await env.AI.run(
    "@cf/black-forest-labs/flux-1-schnell",
    { prompt },
    { gateway: { id: gateway_id, skipCache: true } }
  );

  // Response includes base64 encoded image
  const dataURI = `data:image/jpeg;charset=utf-8;base64,${response.image}`;
  return Response.json({ dataURI });
}

Frontend Integration

The chat interface uses React with TypeScript for type safety:

interface ChatMessage {
  role: 'user' | 'assistant';
  content: string;
}

function ChatInterface() {
  const [messages, setMessages] = useState<ChatMessage[]>([]);
  const [isLoading, setIsLoading] = useState(false);

  async function handleSubmit(userMessage: string) {
    setIsLoading(true);
    try {
      const response = await fetch('/api/chat', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          messages: [...messages, { role: 'user', content: userMessage }]
        }),
      });

      const reader = response.body?.getReader();
      let accumulatedResponse = '';

      while (true) {
        const { done, value } = await reader?.read();
        if (done) break;
        
        const chunk = new TextDecoder().decode(value);
        accumulatedResponse += chunk;
        
        setMessages(prev => [
          ...prev.slice(0, -1),
          { role: 'assistant', content: accumulatedResponse }
        ]);
      }
    } finally {
      setIsLoading(false);
    }
  }

  return (
    // Chat interface JSX
  );
}

Cloudflare AI Gateway

The application uses Cloudflare’s AI Gateway, which provides several built-in features:

  • Automatic request caching at the edge
  • Built-in rate limiting protection
  • Request queueing and load balancing
  • Usage analytics and monitoring

This allows me to focus on the application logic while the infrastructure handles scaling and protection.

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