---
title: Paper to Code
emoji: 🧪
colorFrom: yellow
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: mit
---

# Paper to Code

## Question

What is the engineering step after reading a paper?

## System Boundary

This Space turns a method description into an implementation scaffold. It does not claim to reproduce a paper automatically; it decomposes the method into code boundaries and a checklist.

## Method

The app extracts method signals from the text, optionally asks a Hugging Face-hosted language model for a structured plan, and falls back to a deterministic scaffold if no token is configured.

## Technique

This is research-to-code decomposition. The method text is converted into modules, interfaces, evaluation notes, and a minimal implementation scaffold.

The key idea is to separate the scientific claim from the engineering surfaces needed to test it.

## Output

The app returns a technique summary, module plan, PyTorch or evaluation code scaffold, and reproducibility checklist.

## Why It Matters

Research engineering is translation: claims become modules, datasets, baselines, metrics, and tests. This Space makes that translation explicit.

## What To Notice

The scaffold should not be trusted as final code. It should reveal the implementation questions: what is the encoder, what is the loss, what is the metric, and what baseline is required?

## Effect In Practice

This workflow helps readers move from passive paper reading to active reproduction planning.

## Hugging Face Extension

The Space can connect to paper pages, model repositories, datasets, and reproducibility reports on the Hub.

## Limitations

The scaffold is a starting point. Reproducing a paper requires reading the full method, matching datasets, validating hyperparameters, and comparing against baselines.

## Run Locally

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