README.md

metadata

title: Scholar Lens
emoji: 🔬
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.0.0
app_file: app.py
pinned: false
license: mit
tags:
  - build-small-hackathon
  - backyard-ai
  - nvidia-nemotron
  - openai-codex
  - modal
  - gradio
  - literature-review
  - semantic-scholar

Scholar Lens

Scholar Lens is a small-model literature review assistant for atmospheric science. It searches scholarly sources, de-duplicates papers, maps paper relationships, and uses NVIDIA Llama-Nemotron-Nano 8B on Modal to write grounded, cited answers from retrieved abstracts.

Submission Links

• Live Space: https://huggingface.co/spaces/build-small-hackathon/Scholar-Lens
• GitHub: https://github.com/Mr-Gondal/scholar-lens
• Social post: You can see the LIVE DEMO AT https://youtu.be/IvhU4a2kTnI?si=gM5FjZWltF2sKTV8

Team

• @kinggondal731 - builder / app owner

What It Does

• Ask: enter a research question and get a cited synthesis grounded only in retrieved abstracts.
• Search: search Semantic Scholar, OpenAlex, Crossref, arXiv, and PubMed from one query.
• Constellation: build a connected literature map that shows papers, clusters, and relationships.
• Compare: choose two papers and ask Nemotron to compare methods, claims, limitations, and best use cases.
• Summarize: summarize a selected paper or pasted abstract/results section and export Markdown.

Why This Matters

The app is built for a real atmospheric-science literature review workflow: papers are scattered across databases, abstracts need to be compared quickly, and researchers need evidence-grounded synthesis rather than another list of links.

Scholar Lens keeps the model task focused: it retrieves paper metadata and abstracts first, then asks a small model to reason only over that context. If evidence is missing, the app is designed to say so instead of inventing sources.

Model And Infrastructure

• Model: nvidia/Llama-3.1-Nemotron-Nano-8B-v1
• Inference: Modal-hosted FastAPI endpoints
• App: Gradio on Hugging Face Spaces
• Paper APIs: Semantic Scholar, OpenAlex, Crossref, arXiv, PubMed

Required Hugging Face Secrets:

MODAL_SUMMARIZE_URL
MODAL_SYNTHESIZE_URL
SCHOLAR_LENS_MODAL_TOKEN
SEMANTIC_SCHOLAR_API_KEY
SCHOLAR_LENS_CONTACT_EMAIL

Optional:

OPENALEX_API_KEY

Judging Fit

Criterion	Scholar Lens proof
Specific real problem	Built around atmospheric-science literature review: search, triage, compare, synthesize.
Small-model fit	Nemotron handles bounded grounded synthesis over retrieved abstracts instead of open-ended web guessing.
NVIDIA fit	The main AI workflow is powered by NVIDIA Llama-Nemotron-Nano 8B on Modal.
Codex fit	OpenAI Codex was used to build, debug, polish, and prepare the app.
App polish	Dark UI, source badges, search pagination, paper comparison, constellation map, exports, and friendly empty states.

Run Locally

pip install -r requirements.txt
python app.py

The app starts a local Gradio server on port 7860.

Deploy Modal Inference

modal deploy modal_inference.py

After deployment, copy the Modal endpoint URLs into the Hugging Face Space secrets:

MODAL_SUMMARIZE_URL=https://your-summarize-endpoint.modal.run
MODAL_SYNTHESIZE_URL=https://your-synthesize-endpoint.modal.run
SCHOLAR_LENS_MODAL_TOKEN=your-shared-secret-token

Tests

python -m pytest tests/test_app_core.py

Final README Checklist

• Tags are in the YAML block at the top.
• Demo video link is listed in Submission Links.
• Social post link is listed in Submission Links.
• Team Hugging Face usernames are listed in Team.