---
title: GitHub Issue Triager
emoji: 🎫
colorFrom: yellow
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: mit
---

# GitHub Issue Triager

## Question

Can an LLM turn a messy issue report into useful project-management structure?

## System Boundary

This Space is a triage assistant. It does not make final product decisions; it proposes labels, priority, severity, assignment hints, and next steps for human review.

## Method

The issue text is passed to an instruction model with a structured JSON schema. The app parses the response and presents both the classification and the reasoning.

## Technique

This is structured generation. Instead of asking the model for a paragraph, the app asks for a bounded object with fields that can be validated and used by another system.

The schema is the interface between language understanding and workflow automation.

## Output

The app returns a title, labels, priority, severity, category, assignee suggestion, estimated effort, and rationale.

## Why It Matters

LLMs become more useful in operations when their output is constrained into a schema. This demo shows how unstructured text can become a reviewable workflow artifact.

## What To Notice

The rationale is important. A label without reasoning is hard to review; a label with reasoning can be corrected and used to improve future prompts or datasets.

## Effect In Practice

Issue triage can reduce repetitive coordination work and make human review more consistent, especially in repositories with many incoming reports.

## Hugging Face Extension

This can grow into a labeled issue-triage dataset and a model-comparison Space for classification accuracy, calibration, and rationale quality.

## Limitations

The model does not know repository history unless the text includes it. Production triage should integrate repository metadata, issue history, code ownership, and feedback loops.

## Run Locally

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