# Chartsmith Devpost submission package

Status: paste-ready except for the `/feedback` Session ID and the final
account-bound submission action

## Elevator pitch

One chart image in. A stronger visual story out—autonomously redesigned,
inspected, and repaired.

## Project story

### Inspiration

I work with a very busy boss, and that made the power of visualization
impossible to ignore. A strong chart catches attention before a paragraph can.
It can compress a complex argument, direct the first read, and make the most
important point easier to remember.

I was also surprised by how capable recent AI models became at understanding
images. That led to the question behind Chartsmith: if visual design can be
reasoned about—what should be noticed first, what evidence must stay intact, and
which composition makes the message land—how far can an AI system take that
judgment from the chart image alone?

### What it does

Chartsmith accepts one chart image. It does not ask for raw data, a design brief,
or a sequence of follow-up choices. It reconstructs the visible evidence,
preserves uncertainty, proposes three materially different visual arguments,
selects one, renders it, inspects the actual pixels at audience display sizes,
repairs named defects, and releases only an accepted redesign.

The goal is not merely a prettier chart. The goal is a more powerful
visualization for presentation: faster to understand, more persuasive, and
capable of trying an authored design idea the user may not have imagined.

### How I built it

The outer workflow is deterministic and auditable. Model intelligence operates
inside bounded stages:

1. perceptual reconstruction;
2. design exploration and autonomous selection;
3. real image rendering;
4. independent rendered-image inspection;
5. targeted repair or concept pivot; and
6. final release comparison.

GPT-5.6 performs the visual judgments: reconstruction, concept choice,
rendered-image review, repair judgment, and final comparison. Hybrid concepts
can use GPT Image 2 for visual elements that conventional chart libraries make
awkward. Python owns scheduling, artifacts, budgets, and hard release gates.

The judge-facing React application turns every completed stage into a polished
result rather than exposing machine logs. Ten complete accepted journeys are
available immediately in the public showcase. Genuine new-image generation is
available in the local product and is not simulated in hosting.

### How Codex shaped the Build Week work

Chartsmith began as an earlier experimental repository. During Build Week, I
used Codex as my engineering and product collaborator across the full codebase.
Together we separated historical experiments from the promoted system, replaced
the longer redundant workflow with a compact vNext design, hardened it through
in-depth real cases, built the user-facing product, expanded the portfolio to
ten chart grammars, diagnosed workflow and visual failures, packaged a
rights-conscious public repository, deployed the showcase, and verified the
release.

Codex was not just used to generate a few files. The collaboration spanned
product doctrine, architecture, implementation, evaluation, debugging,
publication, deployment, and submission evidence.

### Challenges

The hardest problem was not generating an attractive chart. It was preventing a
good idea from being released with bad pixels.

AI-generated visuals can contain overflow, overlap, unreadable text, broken
annotations, strange margins, or semantic drift. People notice those defects
immediately. Chartsmith therefore reviews the rendered image—not only the plan—
at multiple display sizes. A named failure receives a bounded repair; an
unsalvageable concept must pivot; production and evidence integrity are hard
gates.

Image-only reconstruction creates a second challenge. Chartsmith must be bold
without pretending that approximate, image-derived values are audited source
data. It keeps uncertainty explicit and judges whether the redesign preserves
the defensible message and chart grammar.

### What I learned

The project strengthened my belief that much of visualization design can be
handled as rational judgment rather than mysterious taste. The system improves
when each stage has a clear local responsibility and when evaluation looks
deeply at a small number of real cases instead of optimizing a large abstract
batch.

I also learned why a workflow can earn its complexity. A single model call may
produce a plausible image, but it does not reliably expose its interpretation,
compare materially different arguments, inspect its own output at real display
sizes, repair a precise defect, and prove why the release is stronger. The
system matters when each stage creates a consequence the audience can see.

### Accomplishments

- one-shot, image-only autonomous input;
- a deterministic seven-stage workflow with bounded GPT-5.6 judgment;
- user-facing intermediate results rather than machine-level progress;
- ten accepted journeys across ten distinct chart grammars;
- twenty-six targeted repairs across the accepted portfolio;
- a fair direct single-call comparison;
- zero visible production blockers in accepted releases;
- a responsive public showcase and a reproducible public MIT repository.

### What's next

The next step is to make the full generation workflow practical as a hosted
experience without weakening its inspection and repair contract. I also want to
expand the design-reference catalogue, learn stronger priors from real user
reactions, and test where image-only reconstruction stops being sufficient and
optional raw data becomes worth the extra user effort.

## Built With

Recommended tags:

- OpenAI
- GPT-5.6
- GPT Image 2
- Codex
- Python
- TypeScript
- React
- Vite
- Playwright
- Pillow
- FFmpeg
- YAML
- JSON Schema
- HTML
- CSS

## Try it out

- Public showcase: <https://chartsmith-build-week.sseo.chatgpt.site>
- Public code: <https://github.com/sseo0817/chartsmith>
- Downloadable source fallback:
  <https://chartsmith-build-week.sseo.chatgpt.site/downloads/chartsmith-source-snapshot.zip>
- Video demo: <https://youtu.be/5CUBe7hqI8o>

## Project media upload order

All generated upload files are under
`output/milestone5_media/project_media/`.

1. `01_product_hero.png`
   - Caption: Chartsmith turns one chart image into a stronger visual argument.
2. `03_before_after_backlog.png`
   - Caption: The same evidence becomes a much faster, more persuasive first
     read.
3. `02_reasoning_visible.png`
   - Caption: Intermediate reconstruction, exploration, inspection, and repair
     stay visible.
4. `04_ten_accepted_journeys.jpg`
   - Caption: Ten accepted journeys across ten distinct chart grammars.
5. `05_mobile_showcase.png`
   - Caption: The public showcase is responsive and production-ready.

Thumbnail:
`docs/current/assets/chartsmith-devpost-thumbnail.png`

## YouTube upload copy

Title:

> Chartsmith — One Chart Image, One Stronger Story | OpenAI Codex Build Week

Description:

> Chartsmith is a one-shot autonomous chart-redesign system. It reconstructs a
> chart from the image alone, explores stronger visual arguments, inspects the
> rendered pixels, repairs visible defects, and releases only an accepted
> redesign.
>
> Live showcase: https://chartsmith-build-week.sseo.chatgpt.site
>
> Source code: https://github.com/sseo0817/chartsmith
>
> Built solo with Codex, GPT-5.6, GPT Image 2, Python, and React for OpenAI Codex
> Build Week.
>
> The hosted product is an evidence-rich showcase of ten complete accepted
> journeys. Genuine new-image generation runs locally and is not simulated in
> the deployment.

Upload settings:

- visibility: **Public**;
- audience: **No, it is not made for kids**;
- language: English;
- captions: upload
  `docs/current/assets/milestone5/video/chartsmith-build-week-demo.srt`;
- thumbnail: `docs/current/assets/chartsmith-devpost-thumbnail.png`.

## Prior-work disclosure

Chartsmith existed as an experimental repository before Build Week. The
pre-event baseline is preserved by the `pre-build-week-clean-20260719` tag.
Build Week work introduced the promoted compact vNext workflow, real-case
hardening, the complete judge-facing product, the ten-example accepted
portfolio, the public release package, deployment, media, and submission
evidence. The repository milestone documents distinguish prior work from Build
Week implementation.

## Known limitations

- Image-only reconstruction is approximate and is not intended for high-stakes
  numerical certification.
- Full-quality local runs can take tens of minutes.
- The hosted submission exposes complete genuine journeys but does not simulate
  live generation.
- Slight image-derived numeric error can be acceptable; visible production
  defects, invented chart structure, and broken semantic mappings are not.

## Final user-only checklist

- [ ] Review the story for personal voice.
- [ ] Upload the five project media files in the recommended order.
- [x] Upload the final MP4 to YouTube as **Public**.
- [x] Add the public YouTube URL to Devpost copy and this document.
- [ ] Run `/feedback` in the primary Codex task.
- [ ] Record the resulting Session ID: `[USER: feedback Session ID]`.
- [ ] Review Devpost rules and attestations.
- [ ] Confirm the public showcase, GitHub, and YouTube links all open.
- [ ] Click the final Devpost submission action before the deadline.
