Readme.md

metadata

title: Chandra OCR 2
emoji: 📄
colorFrom: indigo
colorTo: purple
sdk: gradio
app_file: app.py
pinned: false
license: apache-2.0
short_description: Layout-aware document OCR to markdown + extracted figures
models:
  - datalab-to/chandra-ocr-2
tags:
  - ocr
  - document-ai
  - vision-language
suggested_hardware: zero-a10g

Chandra OCR 2 — Space demo

Gradio demo for datalab-to/chandra-ocr-2, Datalab's layout-aware document OCR model. Upload PDFs or images and get back markdown with tables, math, forms, and reading order preserved — plus a separate figure-extraction stage that crops charts, diagrams, photos and stamps at full resolution and reports the model's caption and any structured data it read from them.

Hardware

This will not run on the free CPU tier. The model is ~10B parameters, roughly 20 GB in bf16.

Hardware	Works?
CPU basic (free)	No — OOM at load
ZeroGPU (H200 slice)	Yes — recommended
RTX PRO 6000 (Blackwell, sm_120)	Yes
L40S / A100	Yes
T4 (16 GB)	No, unless you add 4-bit quantisation

Set this under Settings → Hardware after creating the Space. suggested_hardware in the frontmatter is only a hint to visitors; it does not provision anything.

How it works

• PDFs are rasterised page by page with PyMuPDF (no poppler/apt needed).
• Each page is capped on its long edge before inference — visual token count scales with area, so this is the main lever on latency and memory.
• Pages are batched PAGES_PER_GPU_CALL at a time so each ZeroGPU allocation finishes inside its duration budget.
• Stage 1 (text): raw layout output → markdown via parse_markdown.
• Stage 2 (figures): parse_chunks locates figure-like blocks, which are cropped from the full-resolution source page (the bbox is per-axis normalised, so it maps cleanly onto any resolution with the same aspect ratio). Captions and structured data come from the model's layout output and are labelled degraded when the fallback path was used.

Output is offered as rendered markdown, markdown source, the raw model string, a figure gallery + metadata table, a combined .md, and a .zip containing per-page markdown, per-page raw output, extracted figure images, and a manifest.json.

The app prefers the official chandra package (generate_hf + BatchInputItem

• parse_markdown + parse_chunks). If that import fails, it falls back to driving the chat template through plain transformers so the Space still boots — and reports the degradation in the status panel and diagnostics.

Configuration

Edit the constants at the top of app.py:

Constant	Default	Purpose
PAGES_PER_GPU_CALL	3	Pages per ZeroGPU allocation
GPU_DURATION	180	Seconds requested per allocation
MAX_PAGES	20	Per-run page cap

Prompt types are enumerated from the package (PROMPT_MAPPING), not hardcoded. Only ocr_layout and ocr are currently exposed; the dropdown is editable so you can try undocumented values.

Troubleshooting

Symptom	Cause / fix
Space won't start, opaque "upload failed"	Model load failed at import — the diagnostics panel auto-opens with the exact error.
CUDA out of memory per page	Lower Max image side; it's the biggest VRAM lever.
No figures in the gallery	The model returned no figure bboxes for this prompt type, or the chandra package is unavailable (outputs marked degraded).
flash-attn build error	Do not add flash-attn — Blackwell (sm_120) has no prebuilt wheels. SDPA is used.
torch version mismatch	Do not pin torch — the Space image ships a build matched to the driver/CUDA.

Licence — read before making this public

The Space code here is Apache-2.0. The model weights are not:

Code is Apache 2.0. Model weights are under a modified OpenRAIL-M license. Free for research, personal use, and startups under $2M funding/revenue. Cannot be used competitively with our API.

A public, free, hosted OCR endpoint is plausibly "competitive with our API." If you are past the revenue threshold, or intend this as a product rather than a demo, check with Datalab first. Setting the Space to private avoids the question entirely.