Datasets:
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---
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.
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