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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
"""
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