hamzafarooq FDE Live Translate — Product Evaluation
Your job: produce PRODUCT_EVAL.md at the assignment root — the document the
student submits alongside their video demo. It combines (1) the automated rubric
- benchmark numbers and (2) a live-website test on a real site the student
does not control (default:
https://www.homedepot.com).
Work through the steps in order. Do not fabricate results — every number and sample comes from an actual run. If a step cannot be completed, say so in the report rather than inventing data.
Step 0 — Preconditions
- Confirm both services are up:
- AI service:
curl -sf localhost:8000/health - Gateway:
curl -sf localhost:8787/healthIf either is down, stop and tell the student to start them (seeREADME.md), then resume.
- AI service:
Step 1 — Automated rubric + benchmark
Run these and capture the output:
python benchmark/bench.py --json benchmark/_bench.json # SLA gate + latency + cost
python eval/eval.py --student "<name>" --video "<url>" # rubric scorecard -> eval/REPORT.md
Read eval/report.json and benchmark/_bench.json for the numbers. If the
student hasn't given a name/video yet, ask once for both.
Step 2 — Live-website test (the real FDE check)
Pick a real, content-rich site the student did NOT build. Default https://www.homedepot.com;
a product page works best. Load the widget there via the Chrome extension (extension/,
Load unpacked) — the extension injects the widget and proxies through its background worker,
so it works on strict-CSP sites. Console injection is blocked by CSP on sites like
homedepot.com, so don't rely on it for this test. Then click Translate page.
Capture, honestly:
- Did it translate? Whole page flipped to Mexican Spanish, layout intact?
- Quality: sample 6–8 original→translated string pairs. Judge es-MX register (not generic/Castilian), fluency, and that numbers, prices ($), model/SKU codes are preserved.
- Coverage: anything left in English (nav, dynamically-loaded content, image text)?
- Cache: click Restore, then Translate again — note the cache-hit badge and the latency drop.
- Resilience: did site CSP block injection? Any console errors? Did the page break?
- Screenshot: if browser tooling is available, capture before/after and reference the files. Otherwise ask the student to attach screenshots.
Note in the report if a strict-CSP site blocked the widget — that is a real finding, not a failure of the student's backend. If so, also test a permissive site and report both.
Step 3 — Assess the product
Score each dimension Pass / Partial / Fail with one line of evidence: translation accuracy · es-MX register · numbers/prices preserved · page coverage · cache effectiveness · latency vs SLA · error handling (no silent English) · resilience on a real site · UX polish.
Step 4 — Write PRODUCT_EVAL.md
Fill references/product-eval-template.md with real data and write it to the
assignment root as PRODUCT_EVAL.md. Keep it tight and evidence-first; embed the
rubric score from eval/report.json and the benchmark/cost numbers from
benchmark/_bench.json.
Step 5 — Optional PDF
If the student wants a PDF, convert it:
- Prefer the
md-to-pdfskill if available, or pandoc PRODUCT_EVAL.md -o PRODUCT_EVAL.pdfif pandoc is installed. Report which was used; if neither is available, leave the.mdand say so.
Done
Tell the student exactly what to submit: PRODUCT_EVAL.md (or the PDF) + their video demo, and surface any Fail/Partial dimensions they should fix first.