import pandas as pd import gradio as gr import os from gradio_rangeslider import RangeSlider text_leaderboard = pd.read_csv(os.path.join('src', 'main_df.csv')) text = "## The range is: {min} to {max}" llm_calc_app = gr.Blocks() with llm_calc_app: with gr.Row(): """ Main Filters Row """ range_slider = RangeSlider(minimum=0, maximum=100, value=(0, 100)) range_ = gr.Markdown(value=text.format(min=0, max=100)) range_slider.change(lambda s: text.format(min=s[0], max=s[1]), range_slider, range_, show_progress="hide", trigger_mode="always_last") with gr.Row(): """ Main Leaderboard Row """ leaderboard_table = gr.Dataframe( value=text_leaderboard, elem_id="text-leaderboard-table", interactive=False, visible=True, height=800 ) llm_calc_app.load() llm_calc_app.queue() llm_calc_app.launch() """ model_name, input_price, output_price, multimodality_image,multimodality_multiple_image,multimodality_audio,multimodality_video, source,licence_name,licence_url,languages,release_date, parameters_estimated,parameters_actual, open_weight,context, additional_prices_context_caching, additional_prices_context_storage, additional_prices_image_input,additional_prices_image_output,additional_prices_video_input,additional_prices_video_output,additional_prices_audio_input,additional_prices_audio_output,clemscore_v1.6.5_multimodal,clemscore_v1.6.5_ascii,clemscore_v1.6,latency_v1.6,latency_v1.6.5_multimodal,latency_v1.6.5_ascii, average_clemscore,average_latency,parameters Final list model_name, input_price, output_price, multimodality_image,multimodality_multiple_image,multimodality_audio,multimodality_video, source,licence_name,licence_url,languages,release_date, open_weight,context, average_clemscore,average_latency,parameters Filter multimodality_image,multimodality_multiple_image,multimodality_audio,multimodality_video, licence_name+licence_url, languages, release_date, open_weight RR model_name, input_price, output_price, source, release_date """