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import gradio as gr
import plotly.graph_objects as go
import numpy as np
from src.dataloading import get_leaderboard_models_cached, get_leaderboard_datasets
def create_heatmap(selected_models, selected_dataset):
if not selected_models or not selected_dataset:
return None # Return None to hide the plot if inputs are missing
# Generate random similarity matrix
size = len(selected_models)
similarities = np.random.rand(size, size)
similarities = (similarities + similarities.T) / 2 # Make symmetric
similarities = np.round(similarities, 2) # Round for clarity
# Create the heatmap figure
fig = go.Figure()
fig.add_trace(go.Heatmap(
z=similarities,
x=selected_models,
y=selected_models,
colorscale='Viridis',
zmin=0, zmax=1,
text=similarities, # Values to show on hover
hoverinfo="text"
))
# Update layout for overall figure settings
fig.update_layout(
title=f"Similarity Matrix for {selected_dataset}",
xaxis_title="Models",
yaxis_title="Models",
width=800 + 20 * len(selected_models),
height=800 + 20 * len(selected_models),
margin=dict(b=100, l=100)
)
# Force both axes to be categorical by explicitly specifying tick values and text
fig.update_xaxes(
type="category",
tickmode="array",
tickvals=selected_models,
ticktext=selected_models,
tickangle=45,
automargin=True,
showgrid=True,
showticklabels=True
)
fig.update_yaxes(
type="category",
tickmode="array",
tickvals=selected_models,
ticktext=selected_models,
automargin=True,
showgrid=True,
showticklabels=True
)
return fig # Return only the figure
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!")
# Gradio interface setup
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")
heatmap = gr.Plot(label="Similarity Heatmap", visible=True)
# Use a single output (the figure)
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: clear selections and 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()
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