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import gradio as gr
import plotly.graph_objects as go
import plotly.io as pio
import numpy as np
from src.dataloading import get_leaderboard_models_cached, get_leaderboard_datasets
pio.renderers.default = "iframe"
def create_heatmap(selected_models, selected_dataset):
# Return nothing if no inputs are provided
if not selected_models or not selected_dataset:
return None
# Generate a random symmetric similarity matrix
size = len(selected_models)
similarities = np.random.rand(size, size)
similarities = (similarities + similarities.T) / 2
similarities = np.round(similarities, 2)
# Create a heatmap trace using go.Heatmap; we set x and y to the model names.
fig = go.Figure(data=go.Heatmap(
z=similarities,
x=selected_models,
y=selected_models,
colorscale="Viridis",
zmin=0, zmax=1,
text=similarities,
hoverinfo="text"
))
# Update layout: add title, axis titles, set fixed dimensions and margins
fig.update_layout(
title=f"Similarity Matrix for {selected_dataset}",
xaxis_title="Models",
yaxis_title="Models",
width=800,
height=800,
margin=dict(l=100, r=100, t=100, b=100)
)
return fig
def validate_inputs(selected_models, selected_dataset):
if not selected_models:
raise gr.Error("Please select at least one model!")
if not selected_dataset:
raise gr.Error("Please select a dataset!")
with gr.Blocks(title="LLM Similarity Analyzer") as demo:
gr.Markdown("## Model Similarity Comparison Tool")
with gr.Row():
dataset_dropdown = gr.Dropdown(
choices=get_leaderboard_datasets(),
label="Select Dataset",
filterable=True,
interactive=True,
info="Leaderboard benchmark datasets"
)
model_dropdown = gr.Dropdown(
choices=get_leaderboard_models_cached(),
label="Select Models",
multiselect=True,
filterable=True,
allow_custom_value=False,
info="Search and select multiple models"
)
generate_btn = gr.Button("Generate Heatmap", variant="primary")
# Initialize the Plot component without a figure (it will be updated)
heatmap = gr.Plot(label="Similarity Heatmap", visible=True)
# First validate inputs, then create the heatmap; note that we use a single output.
generate_btn.click(
fn=validate_inputs,
inputs=[model_dropdown, dataset_dropdown],
queue=False
).then(
fn=create_heatmap,
inputs=[model_dropdown, dataset_dropdown],
outputs=heatmap
)
# Clear button to reset selections and clear the plot
clear_btn = gr.Button("Clear Selection")
clear_btn.click(
lambda: [None, None, None],
outputs=[model_dropdown, dataset_dropdown, heatmap]
)
if __name__ == "__main__":
demo.launch(ssr_mode=False) |