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Running Demos (This area is still under construction..)

The AI Imaging Agent can execute tool demos directly on your uploaded images. This guide explains how demo execution works and how to use it effectively.

What Are Demos?

Demos are runnable examples of imaging tools, typically hosted as:

  • HuggingFace Spaces: Interactive Gradio or Streamlit applications
  • Jupyter Notebooks: Google Colab or similar notebook environments
  • Web Applications: Hosted web interfaces
  • GitHub Examples: Code repositories with example scripts

Demo Execution Flow

1. Agent Offers to Run Demo

After providing recommendations, the agent may offer:

Agent: Would you like me to run the demo with your image?

This appears when:

  • Tool has a compatible Gradio Space demo
  • Your image format is compatible
  • Demo's API is accessible

2. You Confirm

Respond with affirmative language:

  • "yes"
  • "sure"
  • "ok"
  • "please"
  • "go ahead"
  • "run it"

The agent detects these patterns and proceeds.

3. Execution Happens

The agent:

  1. Uploads your image to the demo space
  2. Configures any required parameters
  3. Triggers execution
  4. Monitors progress
  5. Retrieves results

4. Results Display

You receive:

  • Success message with output
  • Result images or files
  • Execution trace showing what happened

Demo Types

Gradio Space Demos

Best supported - Direct API integration:

🚀 Demo: https://huggingface.co/spaces/username/toolname

Features:

  • ✅ Automatic execution
  • ✅ Progress monitoring
  • ✅ Result retrieval
  • ✅ Error handling

Example: 

Running TotalSegmentator on your CT scan...
✓ Image uploaded
✓ Processing started
✓ Segmentation complete
✓ Results downloaded

Notebook Demos

Partially supported - Links provided for manual execution:

📓 Notebook: https://colab.research.google.com/...

Process:

  1. Click the notebook link
  2. Open in Google Colab
  3. Upload your image to the notebook
  4. Run cells sequentially
  5. Download results

Web Application Demos

Manual execution - Opens in browser:

🌐 Web Demo: https://example.com/tool

Process:

  1. Click the demo link
  2. Web app opens in new tab
  3. Upload your image via the web UI
  4. Configure settings
  5. Run and download results

GitHub Repository Examples

Code-based - Requires local setup:

💻 Repository: https://github.com/user/repo

Process:

  1. Clone the repository
  2. Install dependencies
  3. Run example scripts
  4. Adapt for your data

Execution Traces

When demos run, you see detailed traces:

<details>
<summary>🔧 Tool Execution Trace</summary>

Step 1: Uploading image to Gradio Space
  ✓ Connected to space: username/toolname
  ✓ Image uploaded: 2.3 MB

Step 2: Configuring parameters
  ✓ Set task: lung-segmentation
  ✓ Set format: DICOM

Step 3: Running inference
  ⏳ Processing... (estimated 30s)
  ✓ Completed in 28s

Step 4: Retrieving results
  ✓ Downloaded segmentation mask: 1.1 MB
  ✓ Downloaded visualization: 0.8 MB

Status: ✅ Success
</details>

Click to expand and see full details.

Supported Gradio Spaces

Auto-Detected Parameters

The agent automatically configures:

Image Input

  • Detects image input component(s)
  • Uploads your file
  • Converts format if needed

Task Selection

Common task parameters:

  • Task dropdown: Matches your query to task option
  • Model selection: Chooses appropriate model
  • Mode: Inference, predict, analyze, etc.

Format Options

  • Input format: DICOM, NIfTI, PNG, etc.
  • Output format: Segmentation mask, visualization, etc.
  • Data type: 2D, 3D, specific modality

Manual Parameters

Some demos require manual interaction:

Agent: This demo has additional parameters. Please visit the link to configure:
- Segmentation threshold: 0.5
- Post-processing: enabled

Demo Execution Best Practices

Check Compatibility First

Verify the tool supports your file format in the recommendation metadata.

Use Standard Formats

Demos work best with standard formats (PNG, JPEG for general; DICOM, NIfTI for medical).

Be Patient

Some demos take time, especially for: - Large images or volumes - Deep learning models - 3D processing

Typical times: 10 seconds to 2 minutes.

Save Results Immediately

Download result files promptly - they may not persist after closing the browser.

Rate Limits

Public Gradio Spaces may have rate limits or queue systems during high usage.

Troubleshooting Demo Execution

Demo Fails to Run

Error: Connection timeout or failed upload

Solutions:

  • Check internet connection
  • Try again (server may be busy)
  • Visit demo link manually
  • Try alternative recommendation

Wrong Results

Error: Output doesn't match expectations

Solutions:

  • Check if correct parameters were used
  • Verify image uploaded correctly
  • Try adjusting task settings manually
  • Compare with demo's example images

Incompatible Format

Error: "Format not supported"

Solutions:

  • Convert image to supported format
  • Use tool that accepts your format
  • Try alternative recommendation

Error: 404 or space not found

Solutions:

  • Space may be temporarily down
  • Check GitHub repo for alternative demo
  • Try different tool recommendation
  • Report broken link

Manual Demo Execution

If automatic execution isn't available:

For Gradio Spaces

  1. Click the demo link
  2. The space opens in your browser
  3. Upload your image via the UI
  4. Select appropriate options
  5. Click "Submit" or "Run"
  6. Download results

For Colab Notebooks

  1. Click the notebook link
  2. Open in Google Colab
  3. Run setup cells (install dependencies)
  4. Upload your image when prompted: 
    from google.colab import files
uploaded = files.upload()
  5. Run processing cells
  6. Download results: 
    files.download('result.png')

For Local Execution

  1. Clone the repository: 

    git clone https://github.com/user/repo
cd repo

  2. Install dependencies: 

    pip install -r requirements.txt

  3. Run example script: 

    python run_demo.py --input your_image.png --output result.png

  4. Check output directory for results

Understanding Results

Segmentation Results

Typically includes:

  • Segmentation mask: Binary or multi-class mask
  • Overlay visualization: Mask overlaid on original image
  • Statistics: Volume, area, counts

Detection Results

Usually provides:

  • Bounding boxes: Coordinates of detected objects
  • Annotated image: Visual with boxes/labels
  • Confidence scores: Detection confidence

Registration Results

Common outputs:

  • Transformed image: Registered/aligned image
  • Transformation matrix: Spatial transform parameters
  • Quality metrics: Similarity scores

Classification Results

Typical outputs:

  • Class labels: Predicted categories
  • Probabilities: Confidence per class
  • Visualization: Class activation maps

Demo Feedback

Help improve the agent by reporting:

Successful Demos

When demos work well, this validates:

  • Tool compatibility
  • Parameter auto-configuration
  • Format handling

Issues

Report when:

  • Demo fails unexpectedly
  • Results are incorrect
  • Parameters were misconfigured
  • Format conversion was wrong

Feedback helps refine the agent's demo execution capabilities.

Next Steps