tags: [gradio-custom-component, HTML] title: gradio_htmlplus short_description: Gradio HTML Advanced Component colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py

gradio_htmlplus

Gradio HTML Advanced Component

Installation

pip install gradio_htmlplus

Usage

import gradio as gr
import pandas as pd
import numpy as np
import uuid

# Import the custom component and the HTML generator
from gradio_htmlplus import HTMLPlus
from leaderboard import generate_leaderboard_html

# Generate Mock Data
def create_mock_leaderboard_data(num_rows=15):
    """
    Creates a pandas DataFrame with random data for the leaderboard demo.

    Args:
        num_rows (int): The number of rows to generate.

    Returns:
        pd.DataFrame: A DataFrame containing mock leaderboard data.
    """
    data = {
        'run_id': [str(uuid.uuid4()) for _ in range(num_rows)],
        'model': [f'model-v{i}-{np.random.choice(["alpha", "beta", "gamma"])}' for i in range(num_rows)],
        'agent_type': np.random.choice(['tool', 'code', 'both'], num_rows),
        'provider': np.random.choice(['litellm', 'transformers'], num_rows),
        'success_rate': np.random.uniform(40, 99.9, num_rows),
        'total_tests': np.random.randint(50, 100, num_rows),
        'avg_steps': np.random.uniform(3, 8, num_rows),
        'avg_duration_ms': np.random.uniform(1500, 5000, num_rows),
        'total_tokens': np.random.randint(10000, 50000, num_rows),
        'total_cost_usd': np.random.uniform(0.01, 0.2, num_rows),
        'co2_emissions_g': np.random.uniform(0.5, 5, num_rows),
        'gpu_utilization_avg': [np.random.uniform(60, 95) if i % 2 == 0 else None for i in range(num_rows)],
        'timestamp': pd.to_datetime(pd.Timestamp.now() - pd.to_timedelta(np.random.rand(num_rows), unit='D')),
        'submitted_by': [f'user_{np.random.randint(1, 5)}' for _ in range(num_rows)],
    }
    df = pd.DataFrame(data)
    df['successful_tests'] = (df['total_tests'] * (df['success_rate'] / 100)).astype(int)
    df['failed_tests'] = df['total_tests'] - df['successful_tests']
    return df


with gr.Blocks(css=".gradio-container { max-width: 95% !important; }") as demo:
    gr.Markdown("# 🏆 Interactive Leaderboard with Action Buttons")
    gr.Markdown("Click on any row in the table to view its complete data, or click a button for a specific action.")

    # Create and display the initial table
    leaderboard_df = create_mock_leaderboard_data(15)
    leaderboard_html = generate_leaderboard_html(leaderboard_df)

    html_table = HTMLPlus(
        value=leaderboard_html,
        # Define both the action button and the table row as selectable elements.
        # The more specific selector should come first to ensure it's matched first.
        selectable_elements=[".tm-action-button", "tr"]
    )

    clicked_data_output = gr.JSON(label="Selected Row Data")
    action_log_output = gr.Textbox(label="Action Log", interactive=False)
    
    def on_element_selected(evt: gr.SelectData):
        """
        Handles select events from the HTMLPlus component. It differentiates actions
        based on which CSS selector was matched (evt.index).

        Args:
            evt (gr.SelectData): The event data object, containing the matched
                                 selector (`.index`) and the element's data
                                 attributes (`.value`).

        Returns:
            tuple: A tuple of values to update the output components.
                   Uses gr.skip() to avoid updating a component.
        """
        if evt.index == ".tm-action-button":
            # This block handles clicks on the 'Delete' button.
            action = evt.value.get('action')
            run_id = evt.value.get('run-id') or "Unknown" 
            
            log_message = f"ACTION: Button '{action}' clicked for Run ID: {run_id[:8]}..."
            
            # Update the log, but skip updating the JSON output.
            return gr.skip(), log_message

        elif evt.index == "tr":
            # This block handles clicks on the table row itself.
            data = evt.value
            run_id = data.get('run-id') or "Unknown"
            
            log_message = f"INFO: Row selected for Run ID: {run_id[:8]}..."
            
            # The frontend sends all data attributes as strings.
            # Convert numeric strings back to numbers for cleaner display.
            numeric_keys = [
                'success-rate', 'total-tests', 'avg-steps', 'avg-duration-ms', 
                'total-tokens', 'total-cost-usd', 'co2-emissions-g', 
                'gpu-utilization-avg', 'successful-tests', 'failed-tests'
            ]
            
            for key in numeric_keys:
                if key in data and data[key] not in ['None', None, '']:
                    try:
                        num_val = float(data[key])
                        if num_val.is_integer():
                            data[key] = int(num_val)
                        else:
                            data[key] = round(num_val, 4)
                    except (ValueError, TypeError):
                        pass  # Leave as a string if conversion fails

            # Update both the JSON output and the log.
            return data, log_message
        
        # A fallback for any unexpected event.
        return gr.skip(), "Unknown action occurred."

    # Connect the 'select' event to the callback function, mapping its
    # return values to the two output components.
    html_table.select(
        fn=on_element_selected,
        inputs=None,
        outputs=[clicked_data_output, action_log_output]
    )

if __name__ == "__main__":
    demo.launch()

HTMLPlus

Initialization

str | Callable | None

str | I18nData | None

Timer | float | None

Component | Sequence[Component] | set[Component] | None

bool

bool | Literal["hidden"]

str | None

list[str] | str | None

bool

int | str | tuple[int | str, ...] | None

list[str] | str | None

int | None

int | None

bool

bool

bool

List[str] | None

Events

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

• As output: Is passed, (Rarely used) passes the HTMLPlus as a str.
• As input: Should return, expects a str consisting of valid HTMLPlus.

def predict(
    value: str | None
) -> str | None:
    return value