Advanced

Gradio Best Practices

Production-ready tips for performance, error handling, security, UI/UX design, and testing your Gradio applications.

Performance Optimization

Python
# 1. Load models at startup, not per request
model = load_model()  # Global scope

def predict(text):
    return model.predict(text)  # Reuse loaded model

# 2. Use queue for concurrent users
demo.queue(max_size=20)  # Limits concurrent requests
demo.launch()

# 3. Enable streaming for long responses
def stream_response(text):
    for token in generate_tokens(text):
        yield token

demo = gr.Interface(fn=stream_response, inputs="text", outputs="text")

Error Handling

Python
def safe_predict(text):
    if not text or not text.strip():
        raise gr.Error("Please enter some text.")

    if len(text) > 5000:
        raise gr.Error("Input too long. Max 5000 characters.")

    try:
        result = model.predict(text)
        return result
    except Exception as e:
        raise gr.Error(f"Prediction failed: {str(e)}")

UI/UX Guidelines

💬

Clear Labels

Always add descriptive labels and placeholders to components. Users should understand what to input without instructions.

📝

Provide Examples

Include 3-5 pre-filled examples so users can try the app immediately without thinking about input.

⚠

Show Progress

Use gr.Progress() for long operations. Stream outputs so users see partial results immediately.

🔒

Input Validation

Validate inputs early with gr.Error(). Show clear, actionable error messages.

Security Checklist

  • Never hardcode secrets: Use environment variables for API keys, passwords, and tokens.
  • Validate file uploads: Check file types and sizes before processing.
  • Sanitize inputs: Prevent code injection in text inputs that feed into eval() or subprocess.
  • Rate limiting: Use demo.queue(max_size=N) to prevent abuse.
  • Authentication: Add auth for apps that access sensitive data or expensive APIs.
  • CORS: Configure allowed_paths to restrict file access.

Testing

Python
import pytest
from gradio_client import Client

def test_prediction():
    # Test the function directly
    result = predict("Hello world")
    assert isinstance(result, str)
    assert len(result) > 0

def test_api_endpoint():
    # Test via the API
    client = Client("http://localhost:7860")
    result = client.predict("Test input", api_name="/predict")
    assert result is not None

Common Mistakes to Avoid

⚠
  • Loading models inside the function: This reloads the model on every request. Load models globally.
  • Not using queue(): Without queue, concurrent users will get errors. Always enable for production.
  • Ignoring file cleanup: Large file uploads can fill disk. Clean up temp files after processing.
  • Missing error handling: Unhandled exceptions show raw tracebacks to users. Use gr.Error() for friendly messages.
  • No examples: Users don't know how to use your app without examples.

Course Complete!

Congratulations! You have completed the Gradio course. You now know how to build, customize, and deploy interactive ML demos with Gradio.

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