chore: Speed up api through concurrency

[DaniilSmirnov]

Hi!
In this PR i refactor os using in api.py to async implementation
Also, i increase number of workers and threads for api and generation

[gemini-code-assist]

Summary of Changes

Hello @DaniilSmirnov, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on enhancing the overall performance and responsiveness of the API and frontend generation processes. By transitioning to asynchronous file operations, increasing API worker count, and boosting frontend concurrency, the system is now better equipped to handle higher loads and deliver a smoother user experience.

Highlights

• Asynchronous File Operations: Refactored all synchronous os and file I/O operations in api.py to use aiofiles for non-blocking asynchronous execution, significantly improving the API's ability to handle concurrent requests without I/O bottlenecks.
• Increased API Concurrency: Configured the Uvicorn server in api/main.py to utilize os.cpu_count() workers, allowing the API to leverage all available CPU cores for enhanced parallel processing.
• Frontend Generation Concurrency: Increased the MAX_CONCURRENT constant in src/app/[owner]/[repo]/page.tsx from 1 to 3, enabling more parallel page generation tasks in the frontend.
• Dependency Updates: Added aiofiles and annotated-doc to the project dependencies and updated several existing packages including boto3, botocore, fastapi, google-api-core, and openai.

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[gemini-code-assist]

Code Review

This pull request aims to improve API performance by refactoring synchronous file operations to an asynchronous implementation using aiofiles. The changes are well-executed for the most part, correctly replacing os functions with their aioos counterparts for file existence checks, reading, writing, and removal. Increasing the number of uvicorn workers and client-side concurrent requests is also a sensible step towards better performance.

However, I've identified a critical issue where a synchronous os.walk() call remains within an async function, which will block the event loop and counteract the intended performance gains. Additionally, there's a potential startup crash issue related to how the number of workers is determined. My review includes detailed feedback and suggestions to address these points.