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

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

global data_component, filter_component

def get_method_color(method):
    return color_dict.get(method, 'black')  # If method is not in color_dict, use black

def draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title):
    df = pd.read_csv(CSV_RESULT_PATH)
    # Filter the dataframe based on selected methods
    filtered_df = df[df['method_name'].isin(methods_selected)]

    def get_method_color(method):
        return color_dict.get(method.upper(), 'black')

    # Add a new column to the dataframe for the color
    filtered_df['color'] = filtered_df['method_name'].apply(get_method_color)

    adjust_text_dict = {
        'expand_text': (1.15, 1.4), 'expand_points': (1.15, 1.25), 'expand_objects': (1.05, 1.5),
        'expand_align': (1.05, 1.2), 'autoalign': 'xy', 'va': 'center', 'ha': 'center',
        'force_text': (.0, 1.), 'force_objects': (.0, 1.),
        'lim': 500000, 'precision': 1., 'avoid_points': True, 'avoid_text': True
    }

    # Create the scatter plot using plotnine (ggplot)
    g = (p9.ggplot(data=filtered_df,
                   mapping=p9.aes(x=x_metric,  # Use the selected x_metric
                                  y=y_metric,  # Use the selected y_metric
                                  color='color',  # Use the dynamically generated color
                                  label='method_names'))  # Label each point by the method name
         + p9.geom_point(size=3)  # Add points with no jitter, set point size
         + p9.geom_text(nudge_y=0.02, size=8)  # Add method names as labels, nudge slightly above the points
         + p9.labs(title=title, x=f"{x_metric}", y=f"{y_metric}")  # Dynamic labels for X and Y axes
         + p9.scale_color_identity()  # Use colors directly from the dataframe
         + p9.theme(legend_position='none', 
                    figure_size=(8, 8),  # Set figure size
                    axis_text=p9.element_text(size=10),   
                    axis_title_x=p9.element_text(size=12),
                    axis_title_y=p9.element_text(size=12))
    )

    # Save the plot as an image
    save_path = "./plot_images"  # Ensure this folder exists or adjust the path
    os.makedirs(save_path, exist_ok=True)  # Create directory if it doesn't exist
    filename = os.path.join(save_path, title.replace(" ", "_") + "_Similarity_Scatter.png")
    
    g.save(filename=filename, dpi=400)
    
    return filename

def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric):
    if benchmark_type == 'flexible':
        # Use general visualizer logic
        return general_visualizer_plot(methods_selected, x_metric=x_metric, y_metric=y_metric)
    elif benchmark_type == 'similarity':
        title = f"{x_metric} vs {y_metric}"
        return draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title)
    elif benchmark_type == 'Benchmark 3':
        return benchmark_3_plot(x_metric, y_metric)
    elif benchmark_type == 'Benchmark 4':
        return benchmark_4_plot(x_metric, y_metric)
    else:
        return "Invalid benchmark type selected."


def get_baseline_df(selected_methods, selected_metrics):
    df = pd.read_csv(CSV_RESULT_PATH)
    present_columns = ["method_name"] + selected_metrics
    df = df[df['method_name'].isin(selected_methods)][present_columns]
    return df

def general_visualizer(methods_selected, x_metric, y_metric):
    df = pd.read_csv(CSV_RESULT_PATH)
    filtered_df = df[df['method_name'].isin(methods_selected)]

    # Create a Seaborn lineplot with method as hue
    plt.figure(figsize=(10, 8))  # Increase figure size
    sns.lineplot(
        data=filtered_df, 
        x=x_metric, 
        y=y_metric, 
        hue="method_name",  # Different colors for different methods
        marker="o",  # Add markers to the line plot
    )
    
    # Add labels and title
    plt.xlabel(x_metric)
    plt.ylabel(y_metric)
    plt.title(f'{y_metric} vs {x_metric} for selected methods')
    plt.grid(True)
    
    # Save the plot to display it in Gradio
    plot_path = "plot.png"
    plt.savefig(plot_path)
    plt.close()
    
    return plot_path

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)
    return None

# 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
            )
            
            # Dropdown for benchmark type
            benchmark_types = TASK_INFO + ['flexible']
            benchmark_type_selector = gr.Dropdown(choices=benchmark_types, label="Select Benchmark Type for Visualization", value="flexible")
            
            # Dynamic metric selectors (will be updated based on benchmark type)
            x_metric_selector = gr.Dropdown(choices=[], label="Select X-axis Metric")
            y_metric_selector = gr.Dropdown(choices=[], label="Select Y-axis Metric")
            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 Visualization")
            plot_output = gr.Image(label="Plot")

            # Update metric selectors when benchmark type is chosen
            def update_metric_choices(benchmark_type):
                if benchmark_type == 'flexible':
                    # Show all metrics for the flexible visualizer
                    metric_names = df.columns.tolist()
                    return gr.update(choices=metric_names, value=metric_names[0]), gr.update(choices=metric_names, value=metric_names[1])
                elif benchmark_type in benchmark_specific_metrics:
                    metrics = benchmark_specific_metrics[benchmark_type]
                    return gr.update(choices=metrics, value=metrics[0]), gr.update(choices=metrics)
                return gr.update(choices=[]), gr.update(choices=[])

            benchmark_type_selector.change(
                update_metric_choices, 
                inputs=[benchmark_type_selector], 
                outputs=[x_metric_selector, y_metric_selector]
            )

            # Generate the plot based on user input
            plot_button.click(
                benchmark_plot, 
                inputs=[benchmark_type_selector, method_selector, x_metric_selector, y_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,
                    )
                
                    function_prediction_dataset = gr.Radio(
                        choices=function_prediction_dataset_options,
                        label="Select Function Prediction Dataset",
                        interactive=True,
                    )
                
                    family_prediction_dataset = gr.CheckboxGroup(
                        choices=family_prediction_dataset_options,
                        label="Select Family Prediction Dataset",
                        interactive=True,
                    )

            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()