Metadata-Version: 2.4
Name: atlas-eval
Version: 0.4.0
Summary: Evaluate any AI model for India-readiness (capability, safety, sovereignty) on the Bharat AI Index.
Author: Atlas AI Labs
License: MIT
Project-URL: Homepage, https://github.com/info-gallary/BharatAIndex
Project-URL: Repository, https://github.com/info-gallary/BharatAIndex
Project-URL: Documentation, https://github.com/info-gallary/BharatAIndex/tree/main/sdk
Project-URL: Bug Tracker, https://github.com/info-gallary/BharatAIndex/issues
Keywords: llm,slm,evaluation,eval,benchmark,india,indic,sovereign-ai,india-readiness
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Testing
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# atlas-eval (Python)

Is your model ready for India? Evaluate **any** model on the Bharat AI Index.

> Requires a running Atlas instance (default `http://localhost:3000`). Start one with
> `npm run dev` in the repo, or `docker run -p 3000:3000 ... bharat-ai-index`.

## Install

```bash
pip install atlas-eval           # live on PyPI
```

## Use

```python
from atlas_eval import evaluate_model

# point at ANY model — OpenAI-compatible endpoint or local Ollama:
report = evaluate_model("gpt-4o-mini", api_key="sk-...")
# report = evaluate_model("my-finetune", base_url="http://localhost:8000/v1")
# report = evaluate_model("gemma2:2b", kind="ollama")

r = report["readiness"]
print(r["verdict"], "| grade", r["sovereigntyGrade"])
print("Indic", r["indicScore"], "vs Global", r["globalScore"], "| gap", r["indicGap"])
print("weakest axes:", r["weakest"])   # what to fix before deploying for India
```

## CLI

```bash
atlas-eval --version
atlas-eval check --base-url http://localhost:3000   # verify a reachable Atlas instance
```

## What you get back

`evaluate_model` returns the full report:

- `run` — `overall ± stderr`, per-axis `categories`, and the Indic-vs-Global `locales` split.
- `sovereignty` — overall score, AAA→C `grade`, and the four sovereignty `dimensions`.
- `readiness` — a plain-English `verdict`, `indicScore` / `globalScore` / `indicGap`,
  and the `weakest` / `strongest` axes.
- `certificateId` — a deterministic, recomputable id for the rating.

Zero runtime dependencies (Python stdlib only). Ships type hints (PEP 561 `py.typed`).
Docs: <https://github.com/info-gallary/BharatAIndex/tree/main/sdk>

MIT © Atlas AI Labs
