---
license: mit
task_categories:
- image-to-text
tags:
- ui-automation
- gui-agent
- multi-video
language:
- en
size_categories:
- n<1K
dataset_info:
  features:
  - name: video_id
    dtype: string
  - name: step
    dtype: int32
  - name: system
    dtype: string
  - name: user
    dtype: string
  - name: assistant
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_examples: 2
---

# UI Automation Dataset (Multi-Video)

**2 examples** from **1 videos** - UI automation tasks from screen recordings.

## Dataset Structure

Each entry contains:
- **video_id**: Sequential ID for each video (video_001, video_002, etc.)
- **step**: Step number within that video (0, 1, 2, ...)
- **system**: System prompt for the GUI agent
- **user**: Task instruction + previous actions
- **assistant**: Model's reasoning and action
- **image**: Screenshot of the UI state

## Usage

```python
from datasets import load_dataset

ds = load_dataset("KMH158-QLU/recruiter_perfect2")

# Access by video
for video_id in set(ds['train']['video_id']):
    video_data = ds['train'].filter(lambda x: x['video_id'] == video_id)
    print(f"Video {video_id}: {len(video_data)} steps")

# Or iterate all examples
for item in ds['train']:
    print(f"{item['video_id']} - Step {item['step']}: {item['assistant'][:50]}...")
```

## Growing Dataset

This dataset supports multiple videos. Each video gets a unique ID (video_001, video_002, etc.).
New videos are automatically appended with the next available ID.