Running against the localhost API. No WebGPU required. CV text is sent only to 127.0.0.1 on this machine.
Draft thematic CV sections on your computer
Turn a conventional CV into evidence for a Canadian Tri-Agency narrative CV.
This desktop build keeps extract, verify, ranking, and draft in the browser UI while
a Python process on localhost runs the language model (Ollama or llama-cpp-python).
Why desktop + local API?
No WebGPU requirement, easier model management, and stronger offline control. CV text is sent only to 127.0.0.1—never to a cloud model API. However, all data stays on your local machine and is not sent to the cloud.
What stays in the browser
CV parsing, evidence cards, gap audit, discipline ranking UI, and draft editing remain the same constrained workflow as the web prototype.
What Python owns
Model load, chat completions, and streaming drafts via Ollama (preferred) or GGUF files through llama-cpp-python.
How would you like to begin?
All three routes lead to the same evidence review. Choose the source that gives you the best factual starting point.
Requires the desktop app (or python -m localtcv_desktop) listening on localhost. WebGPU is not required.
Load a local model
Choose an Ollama tag or a GGUF file managed by the desktop API.
First Ollama use may pull weights; later runs reuse the local copy.
Prefer Ollama for easiest installs. GGUF files belong in the desktop models/ folder.
Quantized local models trade some writing quality for privacy and offline control. Human verification remains mandatory.
Waiting…
Estimating time…
Select your CV
Choose a text-based PDF, Word (.docx), or plain-text file.
Scanned image PDFs are not supported in this version.
Saved document text stays in this browser only.
Preview extracted textQuestions and suggested wording for application context
What is the funding opportunity and which evaluation criteria matter for your role?
What problem, knowledge gap, or community need will the proposed work address?
Who might use or benefit from the work, and through which planned activities?
What will you personally lead, contribute, or be accountable for?
Try: “For the [opportunity], the project will address [problem] with/for [partners or users]. In my role as [role], I will be responsible for [specific responsibilities]. My prior [expertise or experience] is especially relevant to [criterion or project need].”
Verify extracted evidence
Review what the model found, then complete the evidence pathway from your
contribution to its use and outcomes. Do not leave invented facts in place.
Position your TCV
Answer a few focused questions before ranking contributions. Your answers help choose the strongest disciplinary framing.
Clarifying questions
These questions respond to gaps in the material you provided. Leave anything irrelevant blank.
Generate a thematic section
Choose your discipline so importance ranking and drafting use the right
evidence signals. Confirm the top elements, then generate one section.
Most important elements
Ranked by discipline signals, then refined by the local model.