app.py

import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from io import BytesIO
from PIL import Image
from src.dataloading import get_leaderboard_models_cached, get_leaderboard_datasets

# Set matplotlib backend for non-GUI environments
plt.switch_backend('Agg')

def create_heatmap(selected_models, selected_dataset):
    if not selected_models or not selected_dataset:
        return None
    
    size = len(selected_models)
    similarities = np.random.rand(size, size)
    similarities = (similarities + similarities.T) / 2
    similarities = np.round(similarities, 2)

    # Create figure and heatmap using seaborn
    plt.figure(figsize=(10, 8))
    ax = sns.heatmap(
        similarities,
        annot=True,
        fmt=".2f",
        cmap="viridis",
        vmin=0,
        vmax=1,
        xticklabels=selected_models,
        yticklabels=selected_models
    )
    
    # Customize plot
    plt.title(f"Similarity Matrix for {selected_dataset}", fontsize=14)
    plt.xlabel("Models")
    plt.ylabel("Models")
    plt.xticks(rotation=45, ha='right')
    plt.yticks(rotation=0)
    plt.tight_layout()

    # Save to buffer
    buf = BytesIO()
    plt.savefig(buf, format="png", dpi=100, bbox_inches="tight")
    plt.close()
    
    # Convert to PIL Image
    buf.seek(0)
    img = Image.open(buf).convert("RGB")
    return img

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")
    heatmap = gr.Image(label="Similarity Heatmap", visible=True)
    
    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_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)