crummyh/Barbell

AI Summary: Implement a streaming endpoint that yields bytes, similar to the provided example, for all relevant API routes. This involves modifying existing FastAPI endpoints to support streaming responses instead of returning data directly. The implementation should handle potential errors gracefully and efficiently manage resources.

AI Summary: Integrate Cloudflare Turnstile into the frcVisionDataset project to detect and mitigate bot traffic. This involves adding the Turnstile API to the existing FastAPI application, likely requiring modifications to existing endpoints and potentially adding new ones for handling Turnstile verification responses. The implementation should seamlessly integrate with the existing user experience.

AI Summary: The task involves ensuring consistent data type handling throughout the FRCVisionDataset project. This includes resolving inconsistencies between UUIDs and hashes, `IO[bytes]` and `BinaryIO`, and potentially other data type discrepancies. The goal is to improve code readability, maintainability, and prevent potential errors stemming from mixed data types.

AI Summary: Implement more robust error handling in the FRCVisionDataset application to improve user experience and provide helpful messages for client-side errors. This involves catching exceptions, logging errors effectively, and returning informative error responses to the client, potentially including suggestions for troubleshooting.

AI Summary: Develop internal API endpoints for account creation/removal, API key rotation, server status checks, and database migrations. This involves implementing secure authentication and authorization mechanisms, robust error handling, and potentially integrating with a database migration tool.

AI Summary: Create a new database table to store images before they are processed and added to the main dataset. This 'pre-image' table will allow for review and labeling before public availability. The task involves designing the table schema, implementing database interactions (CRUD operations), and integrating it with the existing application's image upload and processing workflow.

AI Summary: This task involves creating FastAPI endpoints to allow users to label images and storing this label data within the AWS S3 system. This includes designing the API endpoints, handling data transfer to S3, and ensuring data integrity and consistency with the existing database schema.

AI Summary: Create Docker containers for the FastAPI application, including necessary dependencies (PostgreSQL, AWS S3 access if applicable), and configure the Dockerfile for building and running the application. This will involve learning Docker if not already familiar and potentially addressing any issues arising from containerizing the application's dependencies.

AI Summary: The project is a pre-alpha web application for FRC teams to upload match images and download object detection datasets. The task involves completing a long TODO list encompassing various aspects, from fixing bugs and improving error handling to adding features like user accounts, image labeling, and a robust admin dashboard. The project uses FastAPI, SQLModel, and other libraries, and requires familiarity with Python, web development, and potentially cloud services (AWS).