app_simple.py

import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
from io import BytesIO
from PIL import Image

from src.dataloading import get_leaderboard_models_cached, get_leaderboard_datasets
from src.similarity import compute_similarity

# Set the backend to 'Agg' for non-GUI environments (optional)
import matplotlib
matplotlib.use('Agg')


def generate_plot():
    # Generate data
    x = np.linspace(0, 10, 100)
    y = np.sin(x)
    
    # Create figure
    fig, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_title("Sine Wave")
    
    # Save figure to a BytesIO buffer
    buf = BytesIO()
    fig.savefig(buf, format="png", bbox_inches="tight", facecolor="white", dpi=100)
    plt.close(fig)  # Close the figure to free memory
    
    # Convert buffer to PIL Image
    buf.seek(0)
    img = Image.open(buf).convert("RGB")
    return img


def validate_inputs(selected_model_a, selected_model_b, selected_dataset):
    if not selected_model_a:
        raise gr.Error("Please select Model A!")
    if not selected_model_b:
        raise gr.Error("Please select Model B!")
    if not selected_dataset:
        raise gr.Error("Please select a dataset!")

def display_similarity(model_a, model_b, dataset):
    # Assuming compute_similarity returns a float or a string
    similarity_score = compute_similarity(model_a, model_b, dataset)
    return f"The similarity between {model_a} and {model_b} on {dataset} is: {similarity_score}"

with gr.Blocks(title="LLM Similarity Analyzer") as demo:
    gr.Markdown("## Model Similarity Comparison Tool")
    
    dataset_dropdown = gr.Dropdown(
        choices=get_leaderboard_datasets(),
        label="Select Dataset",
        filterable=True,
        interactive=True,
        info="Leaderboard benchmark datasets"
    )
    
    model_a_dropdown = gr.Dropdown(
        choices=get_leaderboard_models_cached(),
        label="Select Model A",
        filterable=True,
        allow_custom_value=False,
        info="Search and select models"
    )

    model_b_dropdown = gr.Dropdown(
        choices=get_leaderboard_models_cached(),
        label="Select Model B",
        filterable=True,
        allow_custom_value=False,
        info="Search and select models"
    )

    generate_btn = gr.Button("Compute Similarity", variant="primary")
    
    # Textbox to display the similarity result
    similarity_output = gr.Textbox(
        label="Similarity Result",
        interactive=False
    )
    
    generate_btn.click(
        fn=validate_inputs,
        inputs=[model_a_dropdown, model_b_dropdown, dataset_dropdown],
        queue=False
    ).then(
        fn=display_similarity,
        inputs=[model_a_dropdown, model_b_dropdown, dataset_dropdown],
        outputs=similarity_output
    )
    
    clear_btn = gr.Button("Clear Selection")
    clear_btn.click(
        lambda: [None, None, None, ""],
        outputs=[model_a_dropdown, model_b_dropdown, dataset_dropdown, similarity_output]
    )

    gr.Markdown("## Matplotlib Plot in Gradio")
    plot_button = gr.Button("Generate Plot")
    plot_output = gr.Image(label="Sine Wave Plot")
    plot_button.click(fn=generate_plot, outputs=plot_output)

if __name__ == "__main__":
    demo.launch()