Sarabjit Singh Dhaliwal

// Bilder in Chargen verarbeiten
async function processBatch(images, batchSize = 5) {
  const results = [];

  for (let i = 0; i < images.length; i += batchSize) {
    const batch = images.slice(i, i + batchSize);

    // Batch parallel verarbeiten
    const batchResults = await Promise.all(
      batch.map(img => resizeImage(img))
    );

    results.push(...batchResults);
    updateProgress(i / images.length);
  }

  return results;
}

// Claude API Streaming
async function streamCompletion(prompt, onChunk) {
  const response = await fetch('/api/generate', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ prompt, stream: true })
  });

  const reader = response.body.getReader();
  const decoder = new TextDecoder();
  let fullText = '';

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    // Parse SSE format and update UI
    fullText += parseChunk(chunk);
    onChunk(fullText);
  }

  return fullText;
}

// Parallele PDF-Seiten-Renderung
async function convertPDF(pdfFile, concurrency = 4) {
  const pdf = await pdfjsLib.getDocument(pdfFile).promise;
  const pages = await Promise.all(
    Array.from({ length: pdf.numPages }, (_, i) =>
      pdf.getPage(i + 1)
    )
  );

  const results = [];
  for (let i = 0; i < pages.length; i += concurrency) {
    const batch = pages.slice(i, i + concurrency);
    const images = await Promise.all(
      batch.map(page => renderPage(page))
    );
    results.push(...images);
  }

  return results;
}

# Fast API Backend für Writola
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from anthropic import AsyncAnthropic

app = FastAPI(title="Writola API")
claude = AsyncAnthropic(api_key=settings.ANTHROPIC_API_KEY)

@app.post("/api/generate")
async def generate_article(request: GenerateRequest):
    """Artikel-Generierung mit Claude Streaming."""

    async def stream_generator():
        async with claude.messages.stream(
            model="claude-3-5-sonnet-20241022",
            max_tokens=4096,
            messages=[{"role": "user", "content": request.prompt}],
        ) as stream:
            async for text in stream.text_stream:
                # Server-Sent Events Format
                yield f"data: {json.dumps({'delta_text': text})}\n\n"

    return StreamingResponse(
        stream_generator(),
        media_type="text/event-stream"
    )