---
title: OmniParser v2.0 API
emoji: 🖼️
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.0.0
app_file: app_launcher.py
pinned: false
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# OmniParser v2.0 API

This is a public API endpoint for Microsoft's OmniParser v2.0, which can parse UI screenshots and return structured data.

## Features

- Parses UI screenshots into structured JSON data
- Identifies interactive elements (buttons, menus, icons, etc.)
- Provides captions describing the functionality of each element
- Returns visualization of detected elements
- Accessible via a simple REST API

## Enhancement Opportunities

The current implementation provides a solid foundation, but there are several opportunities for enhancement:

### Data Fusion
- **Current**: YOLO for detection and VLM for captioning are used separately
- **Enhancement**: Implement a more integrated approach that combines YOLO, VLM, OCR, and SAM
- **Benefits**: More accurate detection, better context understanding, and more precise segmentation

### OCR Integration
- **Current**: OCR is used separately from YOLO detection
- **Enhancement**: Use OCR results to refine YOLO detections and merge overlapping text and UI elements
- **Benefits**: Better text recognition in UI elements and improved element classification

### SAM Integration
- **Current**: No segmentation model is used
- **Enhancement**: Integrate SAM (Segment Anything Model) for precise segmentation of UI elements
- **Benefits**: Better handling of complex UI layouts and irregular-shaped elements

### Confidence Scoring
- **Current**: Simple confidence scores from individual models
- **Enhancement**: Combine confidence scores from multiple models and consider element context
- **Benefits**: More reliable confidence scores and better prioritization of elements

### Predictive Monitoring
- **Current**: No verification of detected elements
- **Enhancement**: Verify that detected elements make sense in the UI context
- **Benefits**: Identify missing or incorrectly detected elements and improve detection accuracy

## API Usage

You can use this API by sending a POST request with a file upload:

```python
import requests

# Replace with your actual API URL after deployment
OMNIPARSER_API_URL = "https://your-username-omniparser-api.hf.space/api/parse"

# Upload a file
files = {'image': open('screenshot.png', 'rb')}

# Send request
response = requests.post(OMNIPARSER_API_URL, files=files)

# Get JSON result
result = response.json()

# Access parsed elements
elements = result["elements"]
for element in elements:
    print(f"Element {element['id']}: {element['text']} - {element['caption']}")
    print(f"Coordinates: {element['coordinates']}")
    print(f"Interactable: {element['is_interactable']}")
    print(f"Confidence: {element['confidence']}")
    print("---")

# Access visualization (base64 encoded image)
visualization_base64 = result["visualization"]
```

## Response Format

The API returns a JSON object with the following structure:

```json
{
  "status": "success",
  "elements": [
    {
      "id": 0,
      "text": "Button 1",
      "caption": "Click to submit form",
      "coordinates": [0.1, 0.1, 0.3, 0.2],
      "is_interactable": true,
      "confidence": 0.95
    },
    {
      "id": 1,
      "text": "Menu",
      "caption": "Navigation menu",
      "coordinates": [0.4, 0.5, 0.6, 0.6],
      "is_interactable": true,
      "confidence": 0.87
    }
  ],
  "visualization": "base64_encoded_image_string"
}
```

## Deployment

This API is deployed on Hugging Face Spaces using Gradio. The deployment is free and provides a public URL that you can use in your applications.

## Credits

This API uses Microsoft's OmniParser v2.0, which is a screen parsing tool for pure vision-based GUI agents. For more information, visit the [OmniParser GitHub repository](https://github.com/microsoft/OmniParser).

## License

Please note that the OmniParser models have specific licenses:
- icon_detect model is under AGPL license
- icon_caption is under MIT license

Please refer to the LICENSE file in the folder of each model in the original repository.