CharaForge T2I Lab — Text-to-Image, ControlNet, and LoRA Training Platform

The project is now packaged as a demonstrable portfolio piece. The GitHub Pages demo can be opened directly and includes interactive scenarios, screenshots, recorded demo, architecture explanation, and runbook. The README documents setup, tests, deployment, and architecture. CI/Pages deployment works from `main`. The codebase passes ruff, pytest, React lint/test/build, and npm audit checks; backend tests cover health, model scanning, datasets, auth/security, ownership, WebSocket tickets, and observability behavior.

• GPU-free GitHub Pages demo with interactive scenarios, screenshot gallery, MP4 walkthrough, architecture notes, and reviewer runbook
• FastAPI `/api/v1` API covering t2i, controlnet, batch, finetune, datasets, models, auth, and WebSocket flows
• Celery + Redis async jobs with submit/status/cancel modeling for generation, model scanning, and LoRA training
• AI pipeline integration with PyTorch, Diffusers, Transformers, Accelerate, PEFT, and safetensors
• Security and governance: API keys, JWT refresh cookies, CSRF, short-lived WebSocket tickets, and rate-limit buckets
• Observability and operations: Prometheus metrics, JSON request logs, X-Request-ID, and consistent error responses
• Portfolio packaging: README, docs, screenshots, recording, demo script, and deployment workflow

• Turning heavy GPU workloads into governed async API flows while keeping local model/dataset paths flexible
• Designing a GitHub Pages showcase that communicates the project clearly despite not being able to run backend/GPU work
• Converting an internal AI tooling project into a portfolio artifact with screenshots, recording, flow, and runbook

• Portfolio reviewers and interviewers
• Technical readers who need a quick view of purpose, stack, and maturity

• Detected technical signals: FastAPI, Python, PyTorch, Diffusers, PEFT (LoRA), Accelerate, Celery, Redis, React 19, Vite, Zustand, Docker, GitHub Pages
• README evidence exists and can support a fuller reviewed case study
• A public GitHub repository is linked for source traceability

This case study is generated from the portfolio catalog pipeline using README, Git metadata, package/build configuration, and media signals. The final architecture narrative still needs source-level review. Current detected technology signals include: FastAPI, Python, PyTorch, Diffusers, PEFT (LoRA), Accelerate, Celery, Redis, React 19, Vite, Zustand, Docker, GitHub Pages.

A public data-flow narrative is not fully reviewed yet. If the project includes data processing, AI pipelines, or backend APIs, the next pass should document input, processing, storage, and UI/output flow end to end.

charaforge-T2I-Lab/
  README.md              # project documentation, when available
  source files           # implementation reviewed by local audit
  package/build config   # detected capability signals

This project does not expose a verified runnable web command yet. Review the README/source tree and add exact install, run, test, and build commands before interview use.
No verified build command was detected. Treat the current portfolio page as a case-study placeholder until build steps are reviewed.

• Complete the production-quality README, screenshots, and demo recording
• Add architecture diagrams, data-flow notes, and key technical decisions
• Verify build/test status and update the portfolio release report

• State the current maturity and demonstrable scope first
• Focus on verified stack, source structure, and completed behavior
• Do not claim unverified deployment, video, or test coverage as finished

• Add more recordings and metrics from real GPU inference/training sessions
• Automate screenshots and demo recording through Playwright so showcase assets are reproducible
• Expand E2E and GPU smoke tests so the local full inference path is easier to validate