---
title: DiffSense
emoji: 🔎
colorFrom: gray
colorTo: yellow
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
  - inference-api
license: mit
short_description: Private PR review for local AI teams.
tags:
  - build-small
  - gradio
  - code-review
  - local-ai
  - backyard-ai
  - best-use-of-codex
  - best-agent
  - off-brand
  - best-demo
  - best-minicpm-build
  - nemotron-hardware-prize
  - best-use-of-modal
  - tiny-titan
models:
  - JetBrains/Mellum2-12B-A2.5B-Instruct
  - nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
  - nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
  - openbmb/MiniCPM-V-4.6
---

# DiffSense

Private, offline-first pull request review for teams that cannot send proprietary code to cloud review bots.

Paste a unified diff or a public GitHub PR URL and DiffSense returns severity-tagged findings, inline comments, and structured JSON that can be copied into a PR review. The prototype works without a GPU by using deterministic review rules, then optionally adds Mellum, Nemotron, MiniCPM-V, and Modal provider passes when credentials or endpoints are available.

## Why We Built It

Code review is one of the highest-leverage daily engineering workflows, but most AI reviewers require sending private code to a hosted SaaS. That is a deal-breaker for teams working with customer data, internal APIs, security-sensitive systems, or unreleased products.

DiffSense is the small-model version of that workflow: useful immediately, inspectable, and designed so the core review loop can run locally.

## What Works Now

- Unified diff parser with file and hunk awareness.
- Inline custom diff viewer built in Gradio.
- Deterministic review findings for security, logic, maintainability, and test risks.
- Public GitHub PR URL fetching through the PR `.diff` endpoint.
- Optional Nemotron 3 Nano routing/triage pass.
- Optional Tiny Titan 4B checker pass.
- Optional MiniCPM-V 4.6 vision pass for PR screenshots, architecture diagrams, and UI diffs.
- Optional Modal bridge through `DIFFSENSE_MODAL_ENDPOINT`.
- Structured JSON output with file, hunk, line, severity, category, comment, and suggestion.
- Optional model-assisted summary using `JetBrains/Mellum2-12B-A2.5B-Instruct` through the Hugging Face Inference API when OAuth is available, or a local checkpoint when mounted under `/data`.
- ZeroGPU/bucket-aware model runtime status for local checkpoints mounted from the `build-small-hackathon/DiffSense` bucket.

## Hackathon Track

DiffSense is entered in the Backyard AI track: a practical tool for developers that solves a real daily problem.

Prize/badge targets:

- Best Use of Codex: Codex is being used as an active build partner and will be credited in commits.
- Best Agent: the product is structured as a review pipeline: parse, classify, review, summarize, render.
- Off Brand: the app uses a custom Gradio interface instead of the default chat UI.
- Best Demo: the workflow is easy to show in under two minutes with a real risky diff.
- Best MiniCPM Build: MiniCPM-V 4.6 is integrated for optional image/diagram context.
- Nemotron Hardware Prize: Nemotron 3 Nano is integrated for optional agentic routing.
- Best Use of Modal: the app includes a provider bridge for a Modal-hosted review endpoint via `DIFFSENSE_MODAL_ENDPOINT`.
- Tiny Titan: a <=4B Nemotron 3 Nano checker is integrated as a separate optional pass.

## Planned Model Stack

All planned models are under the Build Small 32B parameter cap.

| Role | Model | Status |
| --- | --- | --- |
| Code review summary | JetBrains Mellum 2 12B Instruct | Optional HF inference hook + `/data` local checkpoint path implemented |
| Provider | Hugging Face Inference API | Optional OAuth-backed summary provider |
| Agentic routing | NVIDIA Nemotron 3 Nano | Optional HF inference hook + `/data` local checkpoint path implemented |
| Tiny checker | NVIDIA Nemotron 3 Nano 4B | Optional HF inference hook + `/data` local checkpoint path implemented |
| Visual PR context | OpenBMB MiniCPM-V 4.6 | Optional image upload + provider/local checkpoint readiness implemented |
| Runtime | Modal | Optional provider bridge via `DIFFSENSE_MODAL_ENDPOINT` implemented |

The current app intentionally keeps a deterministic fallback so the demo remains reliable even if a hosted model endpoint is cold, rate-limited, or unavailable.

## Local Checkpoint Layout

The Space is configured with a read/write bucket mounted at `/data`, so model files can be staged without committing checkpoints to the app repo. DiffSense checks these paths at runtime:

```text
/data/models/mellum2-instruct
/data/models/nemotron-3-nano-30b-a3b
/data/models/nemotron-3-nano-4b
/data/models/minicpm-v-4.6
```

Each directory is considered ready when it contains a `config.json`. If a Hugging Face provider does not serve a sponsor model, the app reports the provider limitation cleanly and keeps the deterministic review running.

## Usage

1. Open the Space.
2. Paste a unified diff, paste a public GitHub PR URL, or click **Load sample diff**.
3. Click **Review diff**.
4. Read the inline comments and copy the structured JSON into your PR workflow.

For public GitHub PRs, paste the PR URL directly. DiffSense fetches the `.diff` version with a short timeout.

## Output Shape

```json
{
  "file": "src/auth.py",
  "hunk": "@@ -1,9 +1,13 @@",
  "line": 11,
  "severity": "critical",
  "category": "security",
  "comment": "The change disables a verification check, which can turn a trusted boundary into a bypass.",
  "suggestion": "Keep verification enabled and add a narrowly scoped test fixture for local development.",
  "source": "deterministic"
}
```

## Privacy

The deterministic review path runs inside the app process and does not send the pasted diff to any external model. If a public PR URL is pasted, the app fetches its public `.diff` over the network. If an optional hosted model pass is enabled, the diff excerpt and deterministic findings are sent to the selected Hugging Face Inference model using the signed-in user's OAuth token. If a local checkpoint is mounted under `/data/models`, that local path is preferred for text-model passes.

## Local Run

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

Then open `http://localhost:7860`.

## Demo Script

1. Start with the privacy pain: cloud review bots are useful, but private code cannot always leave the machine.
2. Load the sample diff.
3. Show critical findings: hardcoded secret, disabled JWT verification, insecure pickle load, disabled TLS verification.
4. Show the JSON output as a practical artifact for PR automation.
5. Toggle the optional model summary to show the small-model enhancement path.

## Submission Artifacts

- [Demo video](https://drive.google.com/file/d/1PBLGO10Wg94jX4OmYVDh63fxFcK6j_kp/view?usp=sharing)
- [HF technical paper](HF_TECH_PAPER.md)
- [LinkedIn post draft](LINKEDIN_POST.md)
- [Demo video pitch](DEMO_VIDEO_PITCH.md)

## Social Post Draft

DiffSense is our Build Small hackathon project: a private PR reviewer for teams that cannot send proprietary code to cloud bots.

Paste a diff or public PR URL, get inline severity-tagged review comments and structured JSON. The app works offline first for pasted diffs, with optional small-model summarization through Mellum 2.

Built with Gradio, Codex, and open-weight model targets under 32B.

#BuildSmall #HuggingFace #Gradio #LocalAI #CodeReview