EvalArena

Sleeping

wip

6b070cd over 1 year ago

19.9 kB

import glob
import os
from typing import Callable

import gradio as gr
import pandas as pd
from loguru import logger

from src.config import TEST_TYPES


class UI:
    """Handles the Gradio UI components and interface"""

    def __init__(
        self,
        refresh_fn: Callable,
        submit_fn: Callable,
        evaluate1_fn: Callable,
        evaluate2_fn: Callable,
        winner1_fn: Callable,
        winner2_fn: Callable,
        both_correct_fn: Callable,
        both_incorrect_fn: Callable,
        refresh_leaderboard_fn: Callable,
        leaderboard_df: pd.DataFrame,
        load_benchmark_fn: Callable = None,
    ):
        self.refresh_fn = refresh_fn
        self.submit_fn = submit_fn
        self.evaluate1_fn = evaluate1_fn
        self.evaluate2_fn = evaluate2_fn
        self.winner1_fn = winner1_fn
        self.winner2_fn = winner2_fn
        self.both_correct_fn = both_correct_fn
        self.both_incorrect_fn = both_incorrect_fn
        self.refresh_leaderboard_fn = refresh_leaderboard_fn
        self.leaderboard_df = leaderboard_df
        self.load_benchmark_fn = load_benchmark_fn

    def refresh_benchmark_types(
        self,
    ):
        try:
            new_benchmark_types = [d for d in os.listdir("benchmarks") if os.path.isdir(os.path.join("benchmarks", d))]

            logger.info(f"Refreshed benchmark types: {new_benchmark_types}")

            # Update the benchmark type dropdown
            if new_benchmark_types:
                # Return the updated dropdown and trigger dataset reload
                return gr.update(choices=new_benchmark_types, value=new_benchmark_types[0])
            else:
                return gr.update(choices=[], value=None)
        except (FileNotFoundError, PermissionError) as e:
            logger.error(f"Error refreshing benchmark types: {e}")
            return gr.update(choices=[], value=None)

    # Benchmark tab event handlers
    def get_benchmark_datasets(self, benchmark_type):
        if not benchmark_type:
            return gr.update(choices=[], value=None)

        try:
            # Find all CSV files that match the pattern <dataset>-judges-metrics.csv
            pattern = os.path.join("benchmarks", benchmark_type, "*-judges-metrics.csv")
            files = glob.glob(pattern)

            # Extract dataset names from file paths
            datasets = []
            for file in files:
                basename = os.path.basename(file)
                dataset_name = basename.replace("-judges-metrics.csv", "")
                datasets.append(dataset_name)

            logger.info(f"Found datasets for {benchmark_type}: {datasets}")

            if datasets:
                return gr.update(choices=datasets, value=datasets[0])
            else:
                return gr.update(choices=[], value=None)
        except Exception as e:
            logger.error(f"Error getting benchmark datasets: {e}")
            return gr.update(choices=[], value=None)

    def create_interface(self) -> gr.Blocks:
        """Create the Gradio interface"""
        with gr.Blocks(
            title="AI Evaluators Arena",
            theme=gr.themes.Soft(
                primary_hue=gr.themes.Color(
                    c50="#ECE9FB",
                    c100="#ECE9FB",
                    c200="#ECE9FB",
                    c300="#6B63BF",
                    c400="#494199",
                    c500="#A5183A",
                    c600="#332E68",
                    c700="#272350",
                    c800="#201E44",
                    c900="#1C1A3D",
                    c950="#100F24",
                ),
                secondary_hue=gr.themes.Color(
                    c50="#ECE9FB",
                    c100="#ECE9FB",
                    c200="#ECE9FB",
                    c300="#6B63BF",
                    c400="#494199",
                    c500="#A5183A",
                    c600="#A5183A",
                    c700="#272350",
                    c800="#201E44",
                    c900="#1C1A3D",
                    c950="#100F24",
                ),
                neutral_hue=gr.themes.Color(
                    c50="#ECE9FB",
                    c100="#ECE9FB",
                    c200="#ECE9FB",
                    c300="#6B63BF",
                    c400="#494199",
                    c500="#A5183A",
                    c600="#332E68",
                    c700="#272350",
                    c800="#201E44",
                    c900="#1C1A3D",
                    c950="#100F24",
                ),
                font=[
                    gr.themes.GoogleFont("Mulish"),
                    "Arial",
                    "sans-serif",
                ],
            ),
        ) as demo:
            gr.Markdown("# AI Evaluators Arena")
            gr.Markdown(
                "Choose which AI judge provides better evaluation of the output. "
                "This is a blind evaluation - judges' identities are hidden until after you make your selection."
            )

            with gr.Tab("🧑‍⚖️ Evaluators Arena"):
                with gr.Row():
                    with gr.Column(scale=1):
                        test_type_dropdown = gr.Dropdown(
                            choices=list(TEST_TYPES.keys()),
                            value="grounding",
                            label="Test Type",
                            info="Select the type of test to evaluate",
                        )
                        test_type_description = gr.Markdown(TEST_TYPES["grounding"])
                        refresh_button = gr.Button("Load from a dataset")

                # Create different input layouts based on test type
                with gr.Row():
                    with gr.Column(scale=2):
                        # Default grounding inputs
                        text_input = gr.Textbox(label="Text", lines=4, visible=True)
                        claim_input = gr.Textbox(label="Claim", lines=2, visible=True)

                        # Policy inputs
                        policy_input = gr.Textbox(label="Input", lines=3, visible=False)
                        policy_output = gr.Textbox(label="Output", lines=4, visible=False)
                        policy_assertion = gr.Textbox(label="Assertion", lines=2, visible=False)

                        # Prompt injection and safety input
                        single_text_input = gr.Textbox(label="Text", lines=6, visible=False)

                        # Legacy inputs (keeping for compatibility)
                        input_text = gr.Textbox(label="Input", lines=4, visible=False)
                        output_text = gr.Textbox(label="Output", lines=6, visible=False)

                        submit_button = gr.Button("Evaluate")
                        status_message = gr.Markdown(visible=False)

                with gr.Row():
                    with gr.Column():
                        evaluation1 = gr.Textbox(label="Anonymous Evaluation 1", lines=10)
                        select_eval1 = gr.Button("Select Evaluation 1", visible=False)

                    with gr.Column():
                        evaluation2 = gr.Textbox(label="Anonymous Evaluation 2", lines=10)
                        select_eval2 = gr.Button("Select Evaluation 2", visible=False)

                with gr.Row(visible=False) as additional_buttons_row:
                    with gr.Column():
                        both_correct_btn = gr.Button("Both Correct", variant="secondary")
                    with gr.Column():
                        both_incorrect_btn = gr.Button("Both Incorrect", variant="secondary")

                result_text = gr.Textbox(label="Result", lines=6)

            with gr.Tab("🏆 Leaderboard"):
                leaderboard_dataframe = gr.DataFrame(
                    value=self.leaderboard_df,
                    headers=["Judge Name", "ELO Score", "Wins", "Losses", "Total Evaluations"],
                    datatype=["str", "number", "number", "number", "number"],
                    col_count=(5, "fixed"),
                    interactive=False,
                )
                refresh_leaderboard = gr.Button("Refresh Leaderboard")

            # New Benchmarks Tab
            with gr.Tab("📊 Benchmarks"):
                types = self.refresh_benchmark_types()
                for t in types:
                    self.get_benchmark_datasets(t)

                with gr.Row():
                    with gr.Column(scale=1):
                        # Get available test types from the benchmarks directory
                        try:
                            benchmark_types = [
                                d for d in os.listdir("benchmarks") if os.path.isdir(os.path.join("benchmarks", d))
                            ]
                        except (FileNotFoundError, PermissionError):
                            # Fallback if directory can't be read
                            benchmark_types = []
                            logger.error("Failed to read benchmarks directory")

                        benchmark_type_dropdown = gr.Dropdown(
                            choices=benchmark_types,
                            label="Benchmark Type",
                            info="Select the type of benchmark to view",
                            value=benchmark_types[0] if benchmark_types else None,
                        )

                with gr.Row():
                    with gr.Column():
                        # Get available benchmark datasets for the selected type
                        benchmark_dataset_dropdown = gr.Dropdown(
                            label="Benchmark Dataset",
                            info="Select the benchmark dataset to view",
                        )

                with gr.Row():
                    with gr.Column():
                        benchmark_dataframe = gr.DataFrame(
                            headers=[
                                "Judge Name",
                                "F1 Score",
                                "Balanced Accuracy",
                                "Avg Latency (s)",
                                "Correct",
                                "Total",
                            ],
                            label="Benchmark Results",
                            interactive=False,
                        )

                        benchmark_info = gr.Markdown("Select a benchmark dataset to view results")

                        # Add a refresh button
                        refresh_benchmarks_btn = gr.Button("Refresh Benchmark List")

            with gr.Tab("About"):
                self._create_about_tab()

            # Set up event handlers
            refresh_button.click(
                self.refresh_fn,
                [test_type_dropdown],
                [
                    input_text,
                    output_text,
                    text_input,
                    claim_input,
                    single_text_input,
                    policy_input,
                    policy_output,
                    policy_assertion,
                ],
            )

            # Update UI based on test type selection
            test_type_dropdown.change(
                self._update_input_visibility,
                [test_type_dropdown],
                [
                    text_input,
                    claim_input,
                    single_text_input,
                    policy_input,
                    policy_output,
                    policy_assertion,
                    input_text,
                    output_text,
                ],
            )

            # Add handler to update the test type description
            test_type_dropdown.change(
                lambda test_type: TEST_TYPES[test_type],
                [test_type_dropdown],
                [test_type_description],
            )

            # Modified submit to prepare for evaluation and trigger both evaluations in parallel
            submit_event = submit_button.click(
                self.submit_fn,
                [
                    text_input,
                    claim_input,
                    single_text_input,
                    policy_input,
                    policy_output,
                    policy_assertion,
                    test_type_dropdown,
                ],
                [
                    evaluation1,
                    evaluation2,
                    text_input,
                    claim_input,
                    single_text_input,
                    policy_input,
                    policy_output,
                    policy_assertion,
                    test_type_dropdown,
                    status_message,
                ],
            )

            # Start both evaluations simultaneously (in parallel) after submit completes
            submit_event.then(
                self.evaluate1_fn,
                [
                    text_input,
                    claim_input,
                    single_text_input,
                    policy_input,
                    policy_output,
                    policy_assertion,
                    test_type_dropdown,
                ],
                [evaluation1, select_eval1],
                queue=False,  # Run immediately without waiting in queue
            )

            submit_event.then(
                self.evaluate2_fn,
                [
                    text_input,
                    claim_input,
                    single_text_input,
                    policy_input,
                    policy_output,
                    policy_assertion,
                    test_type_dropdown,
                ],
                [evaluation2, select_eval2, additional_buttons_row],
                queue=False,  # Run immediately without waiting in queue
            )

            # Show result buttons after both evaluations are done
            select_eval1.click(
                self.winner1_fn,
                [],
                [result_text],
            )

            select_eval2.click(
                self.winner2_fn,
                [],
                [result_text],
            )

            both_correct_btn.click(
                self.both_correct_fn,
                [],
                [result_text],
            )

            both_incorrect_btn.click(
                self.both_incorrect_fn,
                [],
                [result_text],
            )

            refresh_leaderboard.click(
                self.refresh_leaderboard_fn,
                [],
                [leaderboard_dataframe],
            )

            # Set up event handlers for the benchmark tab
            benchmark_type_dropdown.change(
                self.get_benchmark_datasets,
                [benchmark_type_dropdown],
                [benchmark_dataset_dropdown],
            )

            # Add refresh button handler
            refresh_benchmarks_btn.click(
                self.refresh_benchmark_types,
                [],
                [benchmark_type_dropdown],
            ).then(  # Chain the dataset dropdown update after the type is refreshed
                self.get_benchmark_datasets,
                [benchmark_type_dropdown],
                [benchmark_dataset_dropdown],
            )

            # Add handler to load benchmark data when dataset is selected
            if self.load_benchmark_fn:
                benchmark_dataset_dropdown.change(
                    self.load_benchmark_fn,
                    [benchmark_type_dropdown, benchmark_dataset_dropdown],
                    [benchmark_dataframe, benchmark_info],
                )

            # Load initial datasets for the default benchmark type if it exists
            if benchmark_types:
                initial_benchmark_type = benchmark_types[0]
                logger.info(f"Loading initial datasets for benchmark type: {initial_benchmark_type}")
                benchmark_type_dropdown.value = initial_benchmark_type

            # Add footer
            with gr.Row():
                gr.HTML(
                    """
                    <div style="text-align:center; margin-top:20px; padding:10px;">
                        made with ❤️ by <a href="https://qualifire.ai" target="_blank">Qualifire</a>
                    </div>
                    """
                )

            return demo

    def _create_about_tab(self) -> None:
        """Create the About tab content"""
        gr.Markdown(
            """
            # About AI Evaluators Arena
            This platform allows you to evaluate and compare different AI judges in their ability to assess various types of content.
            ## How it works
            1. Choose a test type from the dropdown
            2. Fill in the input fields or load a random example from our dataset
            3. Click "Evaluate" to get assessments from two randomly selected judges
            4. Choose which evaluation you think is better
            5. See which judge provided each evaluation
            ## Test Types
            - **Grounding**: Evaluate if a claim is grounded in a given text
            - **Prompt Injections**: Detect attempts to manipulate or jailbreak the model
            - **Safety**: Identify harmful, offensive, or dangerous content
            - **Policy**: Determine if output complies with a given policy
            ## Leaderboard
            The leaderboard tracks judge performance using an ELO rating system, with scores adjusted based on human preferences.
            """
        )

    def _update_input_visibility(self, test_type):
        """Update which input fields are visible based on the selected test type"""
        if test_type == "grounding":
            return [
                gr.update(visible=True),  # text_input
                gr.update(visible=True),  # claim_input
                gr.update(visible=False),  # single_text_input
                gr.update(visible=False),  # policy_input
                gr.update(visible=False),  # policy_output
                gr.update(visible=False),  # policy_assertion
                gr.update(visible=False),  # input_text
                gr.update(visible=False),  # output_text
            ]
        elif test_type in ["prompt_injections", "safety"]:
            return [
                gr.update(visible=False),  # text_input
                gr.update(visible=False),  # claim_input
                gr.update(visible=True),  # single_text_input
                gr.update(visible=False),  # policy_input
                gr.update(visible=False),  # policy_output
                gr.update(visible=False),  # policy_assertion
                gr.update(visible=False),  # input_text
                gr.update(visible=False),  # output_text
            ]
        elif test_type == "policy":
            return [
                gr.update(visible=False),  # text_input
                gr.update(visible=False),  # claim_input
                gr.update(visible=False),  # single_text_input
                gr.update(visible=True),  # policy_input
                gr.update(visible=True),  # policy_output
                gr.update(visible=True),  # policy_assertion
                gr.update(visible=False),  # input_text
                gr.update(visible=False),  # output_text
            ]
        else:
            # Legacy fallback
            return [
                gr.update(visible=False),  # text_input
                gr.update(visible=False),  # claim_input
                gr.update(visible=False),  # single_text_input
                gr.update(visible=False),  # policy_input
                gr.update(visible=False),  # policy_output
                gr.update(visible=False),  # policy_assertion
                gr.update(visible=True),  # input_text
                gr.update(visible=True),  # output_text
            ]

	import glob
	import os
	from typing import Callable

	import gradio as gr
	import pandas as pd
	from loguru import logger

	from src.config import TEST_TYPES


	class UI:
	"""Handles the Gradio UI components and interface"""

	def __init__(
	self,
	refresh_fn: Callable,
	submit_fn: Callable,
	evaluate1_fn: Callable,
	evaluate2_fn: Callable,
	winner1_fn: Callable,
	winner2_fn: Callable,
	both_correct_fn: Callable,
	both_incorrect_fn: Callable,
	refresh_leaderboard_fn: Callable,
	leaderboard_df: pd.DataFrame,
	load_benchmark_fn: Callable = None,
	):
	self.refresh_fn = refresh_fn
	self.submit_fn = submit_fn
	self.evaluate1_fn = evaluate1_fn
	self.evaluate2_fn = evaluate2_fn
	self.winner1_fn = winner1_fn
	self.winner2_fn = winner2_fn
	self.both_correct_fn = both_correct_fn
	self.both_incorrect_fn = both_incorrect_fn
	self.refresh_leaderboard_fn = refresh_leaderboard_fn
	self.leaderboard_df = leaderboard_df
	self.load_benchmark_fn = load_benchmark_fn

	def refresh_benchmark_types(
	self,
	):
	try:
	new_benchmark_types = [d for d in os.listdir("benchmarks") if os.path.isdir(os.path.join("benchmarks", d))]

	logger.info(f"Refreshed benchmark types: {new_benchmark_types}")

	# Update the benchmark type dropdown
	if new_benchmark_types:
	# Return the updated dropdown and trigger dataset reload
	return gr.update(choices=new_benchmark_types, value=new_benchmark_types[0])
	else:
	return gr.update(choices=[], value=None)
	except (FileNotFoundError, PermissionError) as e:
	logger.error(f"Error refreshing benchmark types: {e}")
	return gr.update(choices=[], value=None)

	# Benchmark tab event handlers
	def get_benchmark_datasets(self, benchmark_type):
	if not benchmark_type:
	return gr.update(choices=[], value=None)

	try:
	# Find all CSV files that match the pattern <dataset>-judges-metrics.csv
	pattern = os.path.join("benchmarks", benchmark_type, "*-judges-metrics.csv")
	files = glob.glob(pattern)

	# Extract dataset names from file paths
	datasets = []
	for file in files:
	basename = os.path.basename(file)
	dataset_name = basename.replace("-judges-metrics.csv", "")
	datasets.append(dataset_name)

	logger.info(f"Found datasets for {benchmark_type}: {datasets}")

	if datasets:
	return gr.update(choices=datasets, value=datasets[0])
	else:
	return gr.update(choices=[], value=None)
	except Exception as e:
	logger.error(f"Error getting benchmark datasets: {e}")
	return gr.update(choices=[], value=None)

	def create_interface(self) -> gr.Blocks:
	"""Create the Gradio interface"""
	with gr.Blocks(
	title="AI Evaluators Arena",
	theme=gr.themes.Soft(
	primary_hue=gr.themes.Color(
	c50="#ECE9FB",
	c100="#ECE9FB",
	c200="#ECE9FB",
	c300="#6B63BF",
	c400="#494199",
	c500="#A5183A",
	c600="#332E68",
	c700="#272350",
	c800="#201E44",
	c900="#1C1A3D",
	c950="#100F24",
	),
	secondary_hue=gr.themes.Color(
	c50="#ECE9FB",
	c100="#ECE9FB",
	c200="#ECE9FB",
	c300="#6B63BF",
	c400="#494199",
	c500="#A5183A",
	c600="#A5183A",
	c700="#272350",
	c800="#201E44",
	c900="#1C1A3D",
	c950="#100F24",
	),
	neutral_hue=gr.themes.Color(
	c50="#ECE9FB",
	c100="#ECE9FB",
	c200="#ECE9FB",
	c300="#6B63BF",
	c400="#494199",
	c500="#A5183A",
	c600="#332E68",
	c700="#272350",
	c800="#201E44",
	c900="#1C1A3D",
	c950="#100F24",
	),
	font=[
	gr.themes.GoogleFont("Mulish"),
	"Arial",
	"sans-serif",
	],
	),
	) as demo:
	gr.Markdown("# AI Evaluators Arena")
	gr.Markdown(
	"Choose which AI judge provides better evaluation of the output. "
	"This is a blind evaluation - judges' identities are hidden until after you make your selection."
	)

	with gr.Tab("🧑‍⚖️ Evaluators Arena"):
	with gr.Row():
	with gr.Column(scale=1):
	test_type_dropdown = gr.Dropdown(
	choices=list(TEST_TYPES.keys()),
	value="grounding",
	label="Test Type",
	info="Select the type of test to evaluate",
	)
	test_type_description = gr.Markdown(TEST_TYPES["grounding"])
	refresh_button = gr.Button("Load from a dataset")

	# Create different input layouts based on test type
	with gr.Row():
	with gr.Column(scale=2):
	# Default grounding inputs
	text_input = gr.Textbox(label="Text", lines=4, visible=True)
	claim_input = gr.Textbox(label="Claim", lines=2, visible=True)

	# Policy inputs
	policy_input = gr.Textbox(label="Input", lines=3, visible=False)
	policy_output = gr.Textbox(label="Output", lines=4, visible=False)
	policy_assertion = gr.Textbox(label="Assertion", lines=2, visible=False)

	# Prompt injection and safety input
	single_text_input = gr.Textbox(label="Text", lines=6, visible=False)

	# Legacy inputs (keeping for compatibility)
	input_text = gr.Textbox(label="Input", lines=4, visible=False)
	output_text = gr.Textbox(label="Output", lines=6, visible=False)

	submit_button = gr.Button("Evaluate")
	status_message = gr.Markdown(visible=False)

	with gr.Row():
	with gr.Column():
	evaluation1 = gr.Textbox(label="Anonymous Evaluation 1", lines=10)
	select_eval1 = gr.Button("Select Evaluation 1", visible=False)

	with gr.Column():
	evaluation2 = gr.Textbox(label="Anonymous Evaluation 2", lines=10)
	select_eval2 = gr.Button("Select Evaluation 2", visible=False)

	with gr.Row(visible=False) as additional_buttons_row:
	with gr.Column():
	both_correct_btn = gr.Button("Both Correct", variant="secondary")
	with gr.Column():
	both_incorrect_btn = gr.Button("Both Incorrect", variant="secondary")

	result_text = gr.Textbox(label="Result", lines=6)

	with gr.Tab("🏆 Leaderboard"):
	leaderboard_dataframe = gr.DataFrame(
	value=self.leaderboard_df,
	headers=["Judge Name", "ELO Score", "Wins", "Losses", "Total Evaluations"],
	datatype=["str", "number", "number", "number", "number"],
	col_count=(5, "fixed"),
	interactive=False,
	)
	refresh_leaderboard = gr.Button("Refresh Leaderboard")

	# New Benchmarks Tab
	with gr.Tab("📊 Benchmarks"):
	types = self.refresh_benchmark_types()
	for t in types:
	self.get_benchmark_datasets(t)

	with gr.Row():
	with gr.Column(scale=1):
	# Get available test types from the benchmarks directory
	try:
	benchmark_types = [
	d for d in os.listdir("benchmarks") if os.path.isdir(os.path.join("benchmarks", d))
	]
	except (FileNotFoundError, PermissionError):
	# Fallback if directory can't be read
	benchmark_types = []
	logger.error("Failed to read benchmarks directory")

	benchmark_type_dropdown = gr.Dropdown(
	choices=benchmark_types,
	label="Benchmark Type",
	info="Select the type of benchmark to view",
	value=benchmark_types[0] if benchmark_types else None,
	)

	with gr.Row():
	with gr.Column():
	# Get available benchmark datasets for the selected type
	benchmark_dataset_dropdown = gr.Dropdown(
	label="Benchmark Dataset",
	info="Select the benchmark dataset to view",
	)

	with gr.Row():
	with gr.Column():
	benchmark_dataframe = gr.DataFrame(
	headers=[
	"Judge Name",
	"F1 Score",
	"Balanced Accuracy",
	"Avg Latency (s)",
	"Correct",
	"Total",
	],
	label="Benchmark Results",
	interactive=False,
	)

	benchmark_info = gr.Markdown("Select a benchmark dataset to view results")

	# Add a refresh button
	refresh_benchmarks_btn = gr.Button("Refresh Benchmark List")

	with gr.Tab("About"):
	self._create_about_tab()

	# Set up event handlers
	refresh_button.click(
	self.refresh_fn,
	[test_type_dropdown],
	[
	input_text,
	output_text,
	text_input,
	claim_input,
	single_text_input,
	policy_input,
	policy_output,
	policy_assertion,
	],
	)

	# Update UI based on test type selection
	test_type_dropdown.change(
	self._update_input_visibility,
	[test_type_dropdown],
	[
	text_input,
	claim_input,
	single_text_input,
	policy_input,
	policy_output,
	policy_assertion,
	input_text,
	output_text,
	],
	)

	# Add handler to update the test type description
	test_type_dropdown.change(
	lambda test_type: TEST_TYPES[test_type],
	[test_type_dropdown],
	[test_type_description],
	)

	# Modified submit to prepare for evaluation and trigger both evaluations in parallel
	submit_event = submit_button.click(
	self.submit_fn,
	[
	text_input,
	claim_input,
	single_text_input,
	policy_input,
	policy_output,
	policy_assertion,
	test_type_dropdown,
	],
	[
	evaluation1,
	evaluation2,
	text_input,
	claim_input,
	single_text_input,
	policy_input,
	policy_output,
	policy_assertion,
	test_type_dropdown,
	status_message,
	],
	)

	# Start both evaluations simultaneously (in parallel) after submit completes
	submit_event.then(
	self.evaluate1_fn,
	[
	text_input,
	claim_input,
	single_text_input,
	policy_input,
	policy_output,
	policy_assertion,
	test_type_dropdown,
	],
	[evaluation1, select_eval1],
	queue=False, # Run immediately without waiting in queue
	)

	submit_event.then(
	self.evaluate2_fn,
	[
	text_input,
	claim_input,
	single_text_input,
	policy_input,
	policy_output,
	policy_assertion,
	test_type_dropdown,
	],
	[evaluation2, select_eval2, additional_buttons_row],
	queue=False, # Run immediately without waiting in queue
	)

	# Show result buttons after both evaluations are done
	select_eval1.click(
	self.winner1_fn,
	[],
	[result_text],
	)

	select_eval2.click(
	self.winner2_fn,
	[],
	[result_text],
	)

	both_correct_btn.click(
	self.both_correct_fn,
	[],
	[result_text],
	)

	both_incorrect_btn.click(
	self.both_incorrect_fn,
	[],
	[result_text],
	)

	refresh_leaderboard.click(
	self.refresh_leaderboard_fn,
	[],
	[leaderboard_dataframe],
	)

	# Set up event handlers for the benchmark tab
	benchmark_type_dropdown.change(
	self.get_benchmark_datasets,
	[benchmark_type_dropdown],
	[benchmark_dataset_dropdown],
	)

	# Add refresh button handler
	refresh_benchmarks_btn.click(
	self.refresh_benchmark_types,
	[],
	[benchmark_type_dropdown],
	).then( # Chain the dataset dropdown update after the type is refreshed
	self.get_benchmark_datasets,
	[benchmark_type_dropdown],
	[benchmark_dataset_dropdown],
	)

	# Add handler to load benchmark data when dataset is selected
	if self.load_benchmark_fn:
	benchmark_dataset_dropdown.change(
	self.load_benchmark_fn,
	[benchmark_type_dropdown, benchmark_dataset_dropdown],
	[benchmark_dataframe, benchmark_info],
	)

	# Load initial datasets for the default benchmark type if it exists
	if benchmark_types:
	initial_benchmark_type = benchmark_types[0]
	logger.info(f"Loading initial datasets for benchmark type: {initial_benchmark_type}")
	benchmark_type_dropdown.value = initial_benchmark_type

	# Add footer
	with gr.Row():
	gr.HTML(
	"""
	<div style="text-align:center; margin-top:20px; padding:10px;">
	made with ❤️ by <a href="https://qualifire.ai" target="_blank">Qualifire</a>
	</div>
	"""
	)

	return demo

	def _create_about_tab(self) -> None:
	"""Create the About tab content"""
	gr.Markdown(
	"""
	# About AI Evaluators Arena

	This platform allows you to evaluate and compare different AI judges in their ability to assess various types of content.

	## How it works
	1. Choose a test type from the dropdown
	2. Fill in the input fields or load a random example from our dataset
	3. Click "Evaluate" to get assessments from two randomly selected judges
	4. Choose which evaluation you think is better
	5. See which judge provided each evaluation

	## Test Types
	- Grounding: Evaluate if a claim is grounded in a given text
	- Prompt Injections: Detect attempts to manipulate or jailbreak the model
	- Safety: Identify harmful, offensive, or dangerous content
	- Policy: Determine if output complies with a given policy

	## Leaderboard
	The leaderboard tracks judge performance using an ELO rating system, with scores adjusted based on human preferences.
	"""
	)

	def _update_input_visibility(self, test_type):
	"""Update which input fields are visible based on the selected test type"""
	if test_type == "grounding":
	return [
	gr.update(visible=True), # text_input
	gr.update(visible=True), # claim_input
	gr.update(visible=False), # single_text_input
	gr.update(visible=False), # policy_input
	gr.update(visible=False), # policy_output
	gr.update(visible=False), # policy_assertion
	gr.update(visible=False), # input_text
	gr.update(visible=False), # output_text
	]
	elif test_type in ["prompt_injections", "safety"]:
	return [
	gr.update(visible=False), # text_input
	gr.update(visible=False), # claim_input
	gr.update(visible=True), # single_text_input
	gr.update(visible=False), # policy_input
	gr.update(visible=False), # policy_output
	gr.update(visible=False), # policy_assertion
	gr.update(visible=False), # input_text
	gr.update(visible=False), # output_text
	]
	elif test_type == "policy":
	return [
	gr.update(visible=False), # text_input
	gr.update(visible=False), # claim_input
	gr.update(visible=False), # single_text_input
	gr.update(visible=True), # policy_input
	gr.update(visible=True), # policy_output
	gr.update(visible=True), # policy_assertion
	gr.update(visible=False), # input_text
	gr.update(visible=False), # output_text
	]
	else:
	# Legacy fallback
	return [
	gr.update(visible=False), # text_input
	gr.update(visible=False), # claim_input
	gr.update(visible=False), # single_text_input
	gr.update(visible=False), # policy_input
	gr.update(visible=False), # policy_output
	gr.update(visible=False), # policy_assertion
	gr.update(visible=True), # input_text
	gr.update(visible=True), # output_text
	]