Fandom / MediaWiki Scraper Platform

The project is now demoable as a complete Fandom / MediaWiki Wiki Data Scraper & Analysis Platform with a FastAPI backend, PyQt6 desktop viewer, SQLite wiki.db outputs, CSV/JSON/Parquet export, HTML infobox fallback, analysis visualization, bilingual Web UI, a guided user assistant, and a deployed live demo.

• Built an API-first crawler flow that prefers the MediaWiki Action API, using HTML parsing only as an infobox-like data fallback.
• Stored pages, categories, links, templates, images, revisions metadata, infobox fields, and page text in SQLite for Web and Qt browsing.
• Completed live data UX for Campaigns, Browse, and Analysis, including dataset counts, search, pagination, error drill-down, network graph, and quality summaries.
• Added a guided user assistant, bilingual UI, indigo/light visual theme, Playwright section screenshots, and demo recordings.

• Keeping the scraper compliant required clear boundaries: no CAPTCHA bypass, Cloudflare bypass, login wall handling, proxy pools, or fingerprint spoofing.
• The demo needed both offline sample data and compliant live crawl presets so it would not depend on external network state while still proving the pipeline works.
• The Qt GUI, FastAPI backend, static Web UI, SQLite schema, and portfolio page all had to reflect the same data workflow.

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

• Detected technical signals: Python, FastAPI, PyQt6, SQLite, MediaWiki API, JavaScript, HTML, CSS, Playwright, pytest
• 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: Python, FastAPI, PyQt6, SQLite, MediaWiki API, JavaScript, HTML, CSS, Playwright, pytest.

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.

fandom-gui-scraper/
  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 integration tests for more MediaWiki edge cases such as continuation, redirects, template-heavy pages, and image metadata.
• Expand the analysis layer with centrality metrics, category clustering, and text quality scoring.
• Package the live demo deployment into a more reliable container release flow to reduce manual remote deployment work.