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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 # Hide the plot if no selection | |
| # 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(data=go.Heatmap( | |
| z=similarities, | |
| x=selected_models, | |
| y=selected_models, | |
| colorscale='Viridis', | |
| zmin=0, zmax=1, | |
| text=similarities, | |
| hoverinfo="text" | |
| )) | |
| # Update layout for title, size, margins, etc. | |
| 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 axes to be categorical and explicitly set the order | |
| fig.update_xaxes( | |
| type="category", | |
| tickangle=45, | |
| categoryorder="array", | |
| categoryarray=selected_models, # Explicitly force ordering to match your list | |
| automargin=True, | |
| showgrid=True, | |
| showticklabels=True | |
| ) | |
| fig.update_yaxes( | |
| type="category", | |
| categoryorder="array", | |
| categoryarray=selected_models, | |
| automargin=True, | |
| showgrid=True, | |
| showticklabels=True | |
| ) | |
| 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!") | |
| # 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() | |