---
title: Trajectory Reviewer
emoji: 🎯
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.1.0
app_file: app.py
pinned: false
short_description: Review and filter training data trajectories
---

# 🎯 Trajectory Reviewer

Quick dataset curation tool for assessing trajectory quality. Designed for rapid keep/remove decisions on training data.

## Features

- **Quick Decisions**: One-click Keep/Remove/Review buttons
- **Issue Tracking**: Flag task mismatches, already completed tasks, and custom issues
- **Smart Ledger**: CSV-based tracking with live updates
- **Shared Dataset**: Collect evaluations from all users into centralized HF Dataset
- **HF Space Sync**: Auto-commits evaluations to Space repository
- **Playback Speed**: Adjustable 1x/2x/4x speed for efficient review
- **Auto-advance**: Automatically loads next trajectory after save

## Usage

1. **Load Dataset**: Enter HuggingFace dataset repo and number of samples
2. **Review**: Watch video (use 2-3x playback speed for efficiency)
3. **Decide**: Click Keep/Remove/Review button
4. **Add Details** (optional): Flag issues and add notes for downstream analysis
5. **Continue**: App auto-advances to next trajectory

## Workflow

```
Load 20 samples → Review video → Quick decision → Auto-advance
```

**Speed**: ~20-30 seconds per trajectory

## Output

All evaluations saved to `evaluations.csv`:

```csv
dataset_repo,config_name,trajectory_id,task,decision,issue_type,notes,timestamp
```

Use the **Download** button in the table to export your evaluations.

## Tips

- Use quick buttons (Keep/Remove/Review) for most trajectories
- Add detailed notes only when needed for issue aggregation
- Use 2x/4x playback speed buttons for faster review

## Data Persistence

### Shared HF Dataset

**Collect all users' evaluations into one centralized dataset**:

1. Create dataset: `huggingface-cli repo create traj-evaluations --type dataset`
2. Add to Space secrets:
   - `HF_TOKEN`: Your HF write token
   - `EVAL_DATASET_REPO`: `username/traj-evaluations`
3. Deploy Space

All evaluations automatically append to the shared dataset.

### CSV Auto-Commit

**On HF Space**: Evaluations CSV automatically commits to the Space's git repository (zero config!)

Check the Files tab in your Space to download accumulated evaluations.

## Related Tools

- **Trajectory Endpoint Labeler**: For precise frame-level endpoint labeling
- Use this reviewer first for quality filtering

---

Built with Gradio, OpenCV, and HuggingFace Datasets