Streamlit web app over the pipeline (issue #5) - #4
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Wraps the notebook pipeline in a form-driven web UI, per issue #5. - pipeline.py: the pipeline extracted into a provider-agnostic, Colab-free module — LLMClient (Gemini/Anthropic/DeepSeek/Groq), scraping, markdown-aware chunking, threshold retrieval, generation, coverage eval, two-layer injection screen, and fabrication check. Importable anywhere; embeddings stay local. - app.py: the Streamlit UI — provider/key sidebar (secrets → env → session field), JD paste/URL, résumé upload (.md/.txt) or paste, GitHub username, real step-by-step progress (st.status, not a bare spinner), an up-front injection gate, and results in tabs: tailored résumé with source-tag badges + markdown download, what-changed, coverage detail, fabrication check, diff, run log. Empty state teaches the flow. - Design follows the impeccable "product register": one sans, hairline borders, flat surfaces, restrained gold accent (primary action only), patina = improved/covered, vermilion = gaps/warnings. Theme in .streamlit/config.toml. - requirements.txt, README (run + deploy), .gitignore (incl. secrets.toml). resolve_resume() and the app.py/pipeline.py split match issue camunity#8's expected shape, so PDF upload is a clean one-branch follow-up. Verified: both modules compile, pure-logic unit checks pass (chunking 1->12 on a one-line résumé, injection screen, coverage eval, diff, output split), and the app boots headless (HTTP 200, no errors) rendering the full form + empty state. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Implements camunity issue #5 — moves the tool from a linear Colab notebook to a real web app UI. Also unblocks camunity#8 (PDF):
resolve_resume()+ theapp.py/pipeline.pysplit match the shape camunity#8 expects.Stacked PR: base is
fix/markdown-chunking(top of the fork stack), so this diff is just the new app. Full stack:main← #1 multi-provider ← #2 injection ← #3 chunking ← this.What's new
pipeline.py— the notebook pipeline extracted into a provider-agnostic, Colab-free module:LLMClient(Gemini/Anthropic/DeepSeek/Groq), JD scraping, markdown-aware chunking, threshold retrieval, generation, coverage eval, two-layer injection screen, fabrication check, diff. Importable anywhere; embeddings stay local.app.py— Streamlit UI over that pipeline:.md/.txt) or paste, optional GitHub username.st.statusstep-by-step (the guidance's rule: show real progress for processing, not a bare spinner).[SOURCE]badges + Markdown download · what-changed · coverage detail · fabrication check · diff · run log.requirements.txt,README.md(run + deploy to Streamlit Cloud / HF Spaces),.gitignore(ignores.streamlit/secrets.toml).Design
Follows the attached impeccable "product register" guidance: one well-tuned sans, hairline borders + flat surfaces (no shadow/glass), restrained accent — gold for the primary action only, patina for improved/covered, vermilion for gaps/warnings. Avoids the AI-slop tells (
side-tab,icon-tile-stack,cream-palette,ai-color-palette,nested-cards). Palette is the Neo-Kinpaku system with OKLCH tokens converted to hex; theme in.streamlit/config.toml.Verification
🤖 Generated with Claude Code