The app has grown to many screens with almost no automated coverage. Your job is a fast, deterministic test suite that runs entirely against the in-memory repository and mock data — no backend, no network — so it's reliable in CI and quick to run locally.

Widget + golden tests for the six core screens, plus unit tests for the pure domain logic. Each screen test drives the UI through its real states and asserts what the user sees.

• Set up the test harness: ProviderScope overrides + shared mock studios.
• Widget tests for Home, Dashboard, Quiz Me, Flashcards, Teach Me, Mastery Report.
• For each screen, assert all four states — loading, empty, error, data.
• Golden (snapshot) tests for at least 3 visually rich screens.
• Unit tests for mastery calculation, weak-topic selection, and the DTO mappers.
• A README covering how to run the suite and add new tests.

Real users upload PDFs, DOCX and PPTX, but the ingestion pipeline currently only accepts .txt / .md. You'll build the missing extractor as a clean, isolated module the lead can drop straight in.

A standalone Python module (your own folder) that takes file bytes + filename + mime and returns clean, chunking-ready text. Wrap it with a thin FastAPI demo endpoint so it can be tested on its own.

• PDF via pypdf · DOCX via python-docx · PPTX via python-pptx.
• Detect format from mime/extension and dispatch to the right extractor.
• Clean output: collapse whitespace, drop repeated headers/footers/page numbers, keep paragraph breaks.
• Fail gracefully on unsupported / corrupt files (clear error, no crash).
• Match the contract exactly: extract_text(filename, mime, data) → str.
• Unit tests with small sample files + a POST /extract demo endpoint.

Short-answer and fill-in-the-blank questions are graded today by exact string match, so correct-but-worded-differently answers are marked wrong. You'll replace that with semantic scoring.

A standalone FastAPI microservice that scores a student's free-text answer against the correct answer using local embedding similarity — no external API key required.

• Endpoint: POST /grade { question, studentAnswer, correctAnswer } → { correct, score 0–1, feedback }.
• Use sentence-transformers embeddings + cosine similarity.
• Choose and justify a correctness threshold; handle empty / near-empty answers.
• Return short human feedback (e.g. "Close — you missed the mechanism").
• Tests over a labeled set — correct / partial / wrong — asserting expected verdicts.
• Stretch: optional LLM-judge fallback behind a flag.

After each deploy we need a quick, automated confidence check across the API surface. You'll build the smoke test that gates it — green means safe, red means stop.

A Python + httpx script that hits a set of endpoints, validates their responses, prints a clean pass/fail report, and exits non-zero on any failure so it can run in CI.

• Check public endpoints first (health / readiness), then a few authenticated ones.
• Assert status codes and the presence/shape of key response fields.
• Handle the streaming (SSE) response format where it applies.
• Clear console report with a summary line — X passed / Y failed.
• Read base URL + token from env vars (provided privately — never hard-coded).

To test uploads and content generation properly, the team needs believable study material across subjects and lengths. You'll build a generator that produces it on demand — the fixtures everyone else tests against.

A Python CLI script that outputs varied sample study documents in .txt and .md, structured like real notes — titles, sections, key terms and examples.

• Multiple subjects — Biology, History, Networking, Economics… (a template set each).
• Multiple sizes — from a short one-page note to a long multi-section chapter.
• Realistic structure: headings, lists, definitions, worked examples.
• CLI flags: --count, --subject, --size, --out.
• Short README with example commands.