> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Gradio Agent

> Learn how to create web interfaces for your AI agents using Gradio

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    In[User Input] --> UI[Gradio UI]
    UI --> Agent[AI Agent]
    Agent --> Out[Results Display]
    
    style In fill:#8B0000,color:#fff
    style UI fill:#2E8B57,color:#fff
    style Agent fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff
```

## Quick Start

<Steps>
  <Step title="Install Dependencies">
    First, install the required packages:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    pip install praisonaiagents gradio
    ```
  </Step>

  <Step title="Create Script">
    Create a new file `app.py`:

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    import gradio as gr
    from praisonaiagents import Agent, Tools
    from praisonaiagents import duckduckgo

    def research(query):
        agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
        result = agent.start(query)
        # Format the result with enhanced markdown
        formatted_result = f"""
    {result}
    ----
    *Generated by PraisonAI Research Assistant*
        """
        return formatted_result

    # Create a simple Gradio interface
    demo = gr.Interface(
        fn=research,
        inputs=gr.Textbox(
            label="Research Query",
            placeholder="Enter your research topic...",
            lines=2
        ),
        outputs=gr.Markdown(
            show_copy_button=True,
            height=500,
            container=True
        ),
        title="AI Research Assistant",
        description="Enter your research query below to get started!",
    )

    if __name__ == "__main__":
        demo.launch()
    ```
  </Step>

  <Step title="Run Application">
    Run your Gradio app:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    python app.py
    ```
  </Step>
</Steps>

## Features

<CardGroup cols={2}>
  <Card title="Simple Interface" icon="wand-magic-sparkles">
    Create beautiful UIs with minimal code.
  </Card>

  <Card title="Markdown Support" icon="markdown">
    Rich text output with built-in markdown rendering.
  </Card>

  <Card title="Copy Button" icon="copy">
    One-click copying of results.
  </Card>

  <Card title="Responsive Design" icon="mobile">
    Mobile-friendly interface out of the box.
  </Card>
</CardGroup>

## Understanding the Code

The example demonstrates a simple research assistant with these key components:

1. **Function Definition**:
   * `research()` function that processes user input
   * Agent initialization and execution
   * Result formatting with markdown

2. **Interface Setup**:
   * Input textbox configuration
   * Markdown output with copy button
   * Title and description settings

3. **Launch Configuration**:
   * Main entry point check
   * Server launch with default settings

## Customization

You can enhance the UI with additional Gradio components:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Add multiple input types
demo = gr.Interface(
    fn=process_inputs,
    inputs=[
        gr.Textbox(label="Query"),
        gr.File(label="Upload Document"),
        gr.Dropdown(choices=["Option 1", "Option 2"])
    ],
    outputs=[
        gr.Markdown(label="Results"),
        gr.Plot(label="Visualization")
    ]
)

# Add themes and styling
demo = gr.Interface(
    ...
    theme="default",
    css=".gradio-container {background-color: #f0f0f0}"
)

# Add authentication
demo.launch(auth=("username", "password"))
```

## Next Steps

* Learn about [Prompt Chaining](/features/promptchaining) for complex UI workflows
* Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving responses
* Check out [Streamlit Integration](/ui/streamlit) for an alternative UI framework