ClauseClear: Gemma 3 1B flags the risky clauses in your client contract, privately

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

ClauseClear is a local contract checklist for freelancers.

You drop in a client agreement (PDF or paste). A Gemma 3 1B model running in Ollama on this laptop returns the clauses worth negotiating: payment, IP, exclusivity, termination, late fees, scope, liability. Each finding has to carry a verbatim quote. If Gemma paraphrases, ClauseClear snaps the claim back to the nearest real sentence — or marks it review and retries. Then it drafts the email you send the client.

The PDF never leaves the machine. There is no account. There is no cloud LLM.

Who would pay: a solo designer, developer, or illustrator staring at a five-page "standard" contractor agreement the night before kickoff. A lawyer skim is often more expensive than the first invoice. Cloud legal-AI wants the whole NDA uploaded. ClauseClear is the product in between: cheap enough to sell as a one-time license, private enough to use on a real client paper.

This is not legal advice. It is a flag list. A human still has to read the paper. The disclaimer is in the UI, the CLI, and the report.

No login. From a cold Ollama on CPU (no GPU, 16 GB RAM):

$ ollama ps
NAME         SIZE     PROCESSOR    CONTEXT
gemma3:1b    880 MB   100% CPU     4096

$ python -m clauseclear samples/abusive_client_agreement.txt --trace samples/trace.json

The bundled abusive agreement is the shape of paper freelancers actually get: 90-day payment, IP assigned whether or not you have been paid, 12-month worldwide exclusivity, terminate-anytime-without-paying, uncapped late fees, unlimited revisions.

First pass with Gemma 1B paraphrased every quote. The validator rejected all six (87s, 3 attempts). After grounding:

Browser UI: streamlit run app.py — paste, review on this laptop, copy the negotiation email, download markdown. Redacted span dump from the same run is in samples/trace.json (infer 30.6s + 27.6s, validate 19ms, no contract text).

There is no live inference URL on purpose. Gemma stays on the laptop. A sample-only front end lives in public/ for a later Render/.tech host; that host is not live yet, so it is not listed below.

Local contract review for freelancers. Paste a client agreement; a Gemma open-weight model running in Ollama on your laptop returns a plain-English checklist of clauses to negotiate. The PDF never leaves the machine.

Not legal advice. ClauseClear is a flag list, not a lawyer. A human still has to read the paper.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.

A 15-minute lawyer skim of a freelance agreement is often more expensive than the first invoice. Cloud legal-AI tools want the full contract uploaded. ClauseClear is the side hustle in between: cheap enough to sell, private enough to use on a real NDA.

The product is the validator, not the chatbot. Every quote the model cites has to appear in the contract. Small local models paraphrase; ClauseClear snaps those paraphrases back to the nearest real sentence, or marks…

ollama serve
ollama pull gemma3:1b
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python -m clauseclear samples/abusive_client_agreement.txt
streamlit run app.py

Swap the model with export OLLAMA_MODEL=gemma3:4b (or any OpenAI-compatible tag). The product does not care who served the weights. MIT license. Tests do not need a GPU (pytest -q, 13 passed).

ClauseClear is built around local inference of an open-weight Gemma model, not a chat wrapper around a closed API.

flowchart LR
  pdf[PDF_or_paste] --> extract[pypdf_text]
  extract --> gemma[Gemma_local_Ollama]
  gemma --> json[structured_clauses]
  json --> validate[quote_must_be_in_paper]
  validate -->|paraphrase| snap[snap_to_nearest_sentence]
  snap -->|still missing| retry[retry_with_the_miss]
  validate -->|ok| report[checklist_and_email]

Three design bets:

pypdf extracts text. Gemma only classifies. A 1B model that also has to read pixels would miss the 90-day clause.quote has to survive whitespace-folded substring matching. Small local models invent fluent nonsense; the fix is a checker, not a bigger prompt.gemma3:1b is an 806 MB pull. ollama ps showed Dead end that became the product. First run: 0 validated quotes. Gemma restated every clause in nicer English. A closed legal API would have shipped those paraphrases as citations. The snap step (token overlap + SequenceMatcher against numbered clauses) is what made a 1B model usable. Retry only fires for quotes that still cannot be bound to the paper.

The pipeline records redacted spans (model, attempt, duration). Infer dominates: ~30s then ~28s on CPU. Validate is 19ms. Contract text is never in the trace — only a source hash and character count. Sentry is wired the same way (SENTRY_DSN optional, before_send strips quotes) so I am not entering Best Use of Sentry until a real dashboard screenshot exists. I will not fake a partner category.

For this product, an open-weight model is a requirement.

The trade-off is honesty: a 1B local model paraphrases. That is why the validator exists. I would rather ship a small model that can be checked than a fluent API that cannot.

I have not taken money yet, and I will not invent a testimonial. I ran ClauseClear on a realistic abusive agreement (the bundled sample) and on a fairer one. The abusive paper is the one a friend would actually forward at 11pm. What I would ask them next: would you pay €12 once to keep this on your laptop, or do you want it to compare two versions of the same contract? That second request is the obvious v2 — a redline, not just a flag list.

If you are a freelancer and you have a contract you can share with the names stripped, I want that test more than I want another sample file.