PROBE / app.py
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__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()