Pathya (പഥ്യം)
India's pharmacogenomic results are stuck in PDFs and WhatsApp photos. Pathya turns them into verified, de-identified research data.
Watch the 3-minute demo · Live: https://pathya.anukritiai.com — no sign-up, no code. Read a WhatsApp photo, dictate results, browse the demo cohort, the simulated-population cohort or a sample record. Live runs carry a small per-visitor hourly budget because each one spends model credits.
Research tool, not medical advice. Pathya reads and standardises pharmacogenomic results and aggregates them into cohort statistics. It makes no dose, drug or test recommendation and has no patient-facing output. All data in this repository is synthetic.
Pathya is the Ayurvedic word for the regimen that suits a particular person. Built by Anukriti AI, Kochi, for the Meta Global AI Developer Hackathon 2026 (Startup track, "Build for Your World"). The repository was created during the event; nothing pre-existing is included.
The problem
India's evidence on drug-relevant gene variants is thin, and the variants that matter here differ from Europe's:
- The most common Indian decreased-function DPYD variant is HapB3, not *2A (PMID 40810747).
- DPYD c.85T>C (*9A) is carried by ~25% of Indians. CPIC calls it normal function; South Indian prospective cohorts report a toxicity association (PMC11300675). It is an open research question.
- NUDT15 risk alleles (~7–11%) are more common in Indians than TPMT's (~3–4%) (PMID 29470173).
- India's DPYD frequency data rests on two studies of about 2,000 and 1,612 people (PMID 33105068, PMID 40810747). There is no ICMR guideline on DPYD testing.
Labs already run these tests. The results travel to hospitals as PDFs and WhatsApp photos, in many layouts, and stay there. None reach a dataset. Evidence brief with sources: docs/EVIDENCE.md.
What it does
- Ingest a lab report (PDF or phone photo), results read aloud, or a GRCh38 VCF.
- Read with Muse Spark under a strict JSON schema: every gene call with the line it came from, plus the variants the report says it tested. Dictation goes through Muse Voice Transcribe (English and Malayalam) and Muse Spark reads the transcript into the same schema.
- Standardise with PharmCAT to PharmVar allele names and CPIC phenotype terms. Guideline and drug text is discarded, never stored.
- Check quality with deterministic rules (
src/lib/quality.ts): which India-relevant variants the panel did not name, unreadable calls, and removal of any patient or lab reference.
- Record: de-identified, exportable as JSON, PharmCAT outside-call TSV or CSV, with the source document beside the quotes. A curator verifies each call; every step goes to an append-only audit log.
- Cohort (
/cohort, src/lib/cohort.ts): carrier frequency per variant with Wilson 95% intervals. Panels that never named a variant are left out of its denominator, not counted as non-carriers.
- Literature: Muse Spark web search for published Indian frequency data per variant, with linked sources.
Meta models used
| Model | Where |
|---|
muse-spark-1.3 | Report reading (PDF and image input, strict JSON schema), dictated-transcript reading, web search for published frequencies |
muse-voice-transcribe-1.0 | Results read aloud: languageBias English + Malayalam, keywords biased to gene and variant names |
meta-models/muse-spark-open-1026 (Muse Spark Open, open weights) | The same reader, on a second inference path. Served through Baseten; runs equally on your own GPU, which is the point. 177/177 on the held-out benchmark |
Not used on purpose: Muse Image and SAM 3.1 (no image to generate or segment).
Where the open-weights model runs. meta-models/muse-spark-open-1026 is served through Baseten's inference API (BASETEN_BASE_URL), called with the same OpenAI wire format as the hosted path, from the same container on AWS Mumbai. Nothing about the call is Baseten-specific: point BASETEN_BASE_URL at a vLLM server on your own hardware and the pipeline is unchanged. Three ways it does real work, not demo work:
- On demand. "Read the same photo on open weights" on the home page, or
provider=baseten on POST /api/analyze. The record then says read by meta-models/muse-spark-open-1026, and the calls section is badged Muse Spark Open.
- As the failover for every ingest path. If api.meta.ai refuses or times out, the report is re-read on open weights. A PDF is rendered to a page image first (
pdftoppm), because the open path has no responses API and cannot take a PDF natively.
- As the whole pipeline.
MUSE_PROVIDER=baseten makes it primary; the end-to-end eval passes with no hosted call at all.
Why it matters here: Meta's credit terms allow API content to be used to improve Meta models. A biobank holding real genomic data cannot accept that, so the self-hostable path is the only version of Pathya that could ever touch a real patient's report.
What we built during the event
The repository was created on 26 October 2026 and every commit is from the event window. Pathya is not a wrapper around one model call; what follows is ours, written here:
This is the beginning of the README. Read the rest at the original. Read the full README ↗
