app.py

__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']

import gradio as gr
import pandas as pd
import re
import os
import json
import yaml
import matplotlib.pyplot as plt
import seaborn as sns
import plotnine as p9
import sys
sys.path.append('./src')
sys.path.append('.')

from src.about import *
from src.saving_utils import *
from src.vis_utils import *
from src.bin.PROBE import run_probe

def add_new_eval(
    human_file,
    skempi_file,
    model_name_textbox: str,
    revision_name_textbox: str,
    benchmark_type,
    similarity_tasks,
    function_prediction_aspect,
    function_prediction_dataset,
    family_prediction_dataset,
):
    representation_name = model_name_textbox if revision_name_textbox == '' else revision_name_textbox
    results = run_probe(benchmark_type, representation_name, human_file, skempi_file, similarity_tasks, function_prediction_aspect, function_prediction_dataset, family_prediction_dataset)

    for benchmark_type in results:
        if benchmark_type == 'similarity':
            save_similarity_output(results['similarity'], representation_name)
        elif benchmark_type == 'function':
            save_function_output(results['function'], representation_name)
        elif benchmark_type == 'family':
            save_family_output(results['family'], representation_name)
        elif benchmark_type == "affinity":
            save_affinity_output(results['affinity', representation_name])

# Function to update leaderboard dynamically based on user selection
def update_leaderboard(selected_methods, selected_metrics):
    return get_baseline_df(selected_methods, selected_metrics)

block = gr.Blocks()

with block:
    gr.Markdown(LEADERBOARD_INTRODUCTION)
    
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        # table jmmmu bench
        with gr.TabItem("🏅 PROBE Leaderboard", elem_id="probe-benchmark-tab-table", id=1):

            method_names = pd.read_csv(CSV_RESULT_PATH)['method_name'].unique().tolist()
            metric_names = pd.read_csv(CSV_RESULT_PATH).columns.tolist()
            metrics_with_method = metric_names.copy()
            metric_names.remove('method_name')  # Remove method_name from the metric options
        
            # Leaderboard section with method and metric selectors
            with gr.Row():
                # Add method and metric selectors for leaderboard
                leaderboard_method_selector = gr.CheckboxGroup(
                    choices=method_names, label="Select method_names for Leaderboard", value=method_names, interactive=True
                )
                leaderboard_metric_selector = gr.CheckboxGroup(
                    choices=metric_names, label="Select Metrics for Leaderboard", value=metric_names, interactive=True
                )

            # Display the filtered leaderboard
            baseline_value = get_baseline_df(method_names, metric_names)
            baseline_header = ["method_name"] + metric_names
            baseline_datatype = ['markdown'] + ['number'] * len(metric_names)

            data_component = gr.components.Dataframe(
                value=baseline_value,
                headers=baseline_header,
                type="pandas",
                datatype=baseline_datatype,
                interactive=False,
                visible=True,
            )

            # Update leaderboard when method/metric selection changes
            leaderboard_method_selector.change(
                update_leaderboard, 
                inputs=[leaderboard_method_selector, leaderboard_metric_selector], 
                outputs=data_component
            )
            leaderboard_metric_selector.change(
                update_leaderboard, 
                inputs=[leaderboard_method_selector, leaderboard_metric_selector], 
                outputs=data_component
            )

            # Dynamic selectors
            x_metric_selector = gr.Dropdown(choices=[], label="Select X-axis Metric", visible=False)
            y_metric_selector = gr.Dropdown(choices=[], label="Select Y-axis Metric", visible=False)
            aspect_type_selector = gr.Dropdown(choices=[], label="Select Aspect Type", visible=False)
            dataset_type_selector = gr.Dropdown(choices=[], label="Select Dataset Type", visible=False)
            dataset_selector = gr.Dropdown(choices=[], label="Select Dataset", visible=False)
            single_metric_selector = gr.Dropdown(choices=[], label="Select Metric", visible=False)
            
            # CheckboxGroup for methods
            method_selector = gr.CheckboxGroup(choices=method_names, label="Select methods to visualize", interactive=True, value=method_names)
            
            # Button to draw the plot for the selected benchmark
            plot_button = gr.Button("Plot")
            plot_output = gr.Image(label="Plot")
            
            # Update metric selectors based on benchmark type
            def update_metric_choices(benchmark_type):
                if benchmark_type == 'similarity':
                    # Show x and y metric selectors for similarity
                    metric_names = benchmark_specific_metrics.get(benchmark_type, [])
                    return (
                        gr.update(choices=metric_names, value=metric_names[0], visible=True),
                        gr.update(choices=metric_names, value=metric_names[1], visible=True),
                        gr.update(visible=False), gr.update(visible=False), 
                        gr.update(visible=False), gr.update(visible=False)
                    )
                elif benchmark_type == 'function':
                    # Show aspect and dataset type selectors for function
                    aspect_types = benchmark_specific_metrics[benchmark_type]['aspect_types']
                    dataset_types = benchmark_specific_metrics[benchmark_type]['dataset_types']
                    return (
                        gr.update(visible=False), gr.update(visible=False),
                        gr.update(choices=aspect_types, value=aspect_types[0], visible=True),
                        gr.update(choices=dataset_types, value=dataset_types[0], visible=True),
                        gr.update(visible=False), gr.update(visible=False)
                    )
                elif benchmark_type == 'family':
                    # Show dataset and metric selectors for family
                    datasets = benchmark_specific_metrics[benchmark_type]['datasets']
                    metrics = benchmark_specific_metrics[benchmark_type]['metrics']
                    return (
                        gr.update(visible=False), gr.update(visible=False),
                        gr.update(visible=False), gr.update(visible=False),
                        gr.update(choices=datasets, value=datasets[0], visible=True),
                        gr.update(choices=metrics, value=metrics[0], visible=True)
                    )
                elif benchmark_type == 'affinity':
                    # Show single metric selector for affinity
                    metrics = benchmark_specific_metrics[benchmark_type]
                    return (
                        gr.update(visible=False), gr.update(visible=False),
                        gr.update(visible=False), gr.update(visible=False),
                        gr.update(visible=False), gr.update(choices=metrics, value=metrics[0], visible=True)
                    )
                return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
            
            # Dropdown for benchmark type
            benchmark_type_selector = gr.Dropdown(choices=list(benchmark_specific_metrics.keys()), label="Select Benchmark Type")
            
            # Update selectors when benchmark type changes
            benchmark_type_selector.change(
                update_metric_choices,
                inputs=[benchmark_type_selector],
                outputs=[x_metric_selector, y_metric_selector, aspect_type_selector, dataset_type_selector, dataset_selector, single_metric_selector]
            )
            
            plot_button.click(
                benchmark_plot,
                inputs=[benchmark_type_selector, method_selector, x_metric_selector, y_metric_selector, aspect_type_selector, dataset_type_selector, dataset_selector, single_metric_selector],
                outputs=plot_output
            )
            
        with gr.TabItem("📝 About", elem_id="probe-benchmark-tab-table", id=2):
            with gr.Row():
                gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here! ", elem_id="probe-benchmark-tab-table", id=3):
            with gr.Row():
                gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

            with gr.Row():
                gr.Markdown("# ✉️✨ Submit your model's representation files here!", elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    model_name_textbox = gr.Textbox(
                        label="Model name",
                    )
                    revision_name_textbox = gr.Textbox(
                        label="Revision Model Name",
                    )
                    
                    benchmark_type = gr.CheckboxGroup(
                        choices=TASK_INFO,
                        label="Benchmark Type",
                        interactive=True,
                    )
                    similarity_tasks = gr.CheckboxGroup(
                        choices=similarity_tasks_options,
                        label="Select Similarity Tasks",
                        interactive=True,
                    )
                
                    function_prediction_aspect = gr.Radio(
                        choices=function_prediction_aspect_options,
                        label="Select Function Prediction Aspect",
                        interactive=True,
                    )
                
                    family_prediction_dataset = gr.CheckboxGroup(
                        choices=family_prediction_dataset_options,
                        label="Select Family Prediction Dataset",
                        interactive=True,
                    )

                    function_prediction_dataset = "All_Data_Sets"
            with gr.Column():
                human_file = gr.components.File(label="Click to Upload the representation file (csv) for Human dataset", file_count="single", type='filepath')
                skempi_file = gr.components.File(label="Click to Upload the representation file (csv) for SKEMPI dataset", file_count="single", type='filepath')
    
                submit_button = gr.Button("Submit Eval")
                submission_result = gr.Markdown()
                submit_button.click(
                    add_new_eval,
                    inputs=[
                        human_file,
                        skempi_file,
                        model_name_textbox,
                        revision_name_textbox,
                        benchmark_type,
                        similarity_tasks,
                        function_prediction_aspect,
                        function_prediction_dataset,
                        family_prediction_dataset,
                    ],
                )

    def refresh_data():
        value = get_baseline_df(method_names, metric_names)
        return value

    with gr.Row():
        data_run = gr.Button("Refresh")
        data_run.click(refresh_data, outputs=[data_component])

    with gr.Accordion("Citation", open=False):
        citation_button = gr.Textbox(
            value=CITATION_BUTTON_TEXT,
            label=CITATION_BUTTON_LABEL,
            elem_id="citation-button",
            show_copy_button=True,
        )

block.launch()