---
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:

```text
MODAL_SUMMARIZE_URL
MODAL_SYNTHESIZE_URL
SCHOLAR_LENS_MODAL_TOKEN
SEMANTIC_SCHOLAR_API_KEY
SCHOLAR_LENS_CONTACT_EMAIL
```

Optional:

```text
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

```bash
pip install -r requirements.txt
python app.py
```

The app starts a local Gradio server on port `7860`.

## Deploy Modal Inference

```bash
modal deploy modal_inference.py
```

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

```text
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

```bash
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**.