File size: 10,420 Bytes
925ac7f b7fabea 54d8c8e fbdc657 925ac7f fbdc657 68e6513 b7fabea 54d8c8e 68e6513 fbdc657 68e6513 fbdc657 68e6513 fbdc657 68e6513 fbdc657 68e6513 fbdc657 68e6513 fbdc657 68e6513 fbdc657 54d8c8e fbdc657 925ac7f fbdc657 68e6513 54d8c8e fbdc657 54d8c8e fbdc657 68e6513 fbdc657 54d8c8e 925ac7f 54d8c8e 68e6513 54d8c8e fbdc657 54d8c8e 68e6513 54d8c8e 68e6513 fbdc657 68e6513 fbdc657 68e6513 fbdc657 68e6513 fbdc657 68e6513 fbdc657 68e6513 54d8c8e bbae640 54d8c8e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 |
import pandas as pd
import gradio as gr
import os
from gradio_rangeslider import RangeSlider
import math
from utils.filter_utils import filter, filter_cols
# MAPS = filter_utils.LANG_MAPPING
# Main Leaderboard containing everything
text_leaderboard = pd.read_csv(os.path.join('src', 'main_df.csv'))
text = "## The range is: {min} to {max}"
# Short leaderboard containing fixed columns
short_leaderboard = filter_cols(text_leaderboard)
## Extract data
langs = []
licenses = []
ip_prices = []
op_prices = []
latencies = []
parameters = []
contexts = []
dates = []
for i in range(len(text_leaderboard)):
lang_splits = text_leaderboard.iloc[i]['Languages'].split(',')
lang_splits = [s.strip() for s in lang_splits]
langs += lang_splits
license_name = text_leaderboard.iloc[i]['License Name']
licenses.append(license_name)
ip_prices.append(text_leaderboard.iloc[i]['Input $/1M'])
op_prices.append(text_leaderboard.iloc[i]['Output $/1M'])
latencies.append(text_leaderboard.iloc[i]['Average Latency (s)'])
parameters.append(text_leaderboard.iloc[i]['Parameter Size (B)'])
contexts.append(text_leaderboard.iloc[i]['Context Size'])
dates.append(text_leaderboard.iloc[i]['Release Date'])
langs = list(set(langs))
langs.sort()
licenses = list(set(licenses))
licenses.sort()
max_input_price = max(ip_prices)
max_output_price = max(op_prices)
max_latency = max(latencies)
max_parameter = max(parameters)
max_parameter = math.ceil(math.log2(max_parameter))
max_context = max(contexts)/1024
max_context = math.ceil(math.log2(max_context))
min_date = min(dates)
max_date = max(dates)
llm_calc_app = gr.Blocks()
with llm_calc_app:
### Language filter
with gr.Row():
lang_dropdown = gr.Dropdown(
choices=langs,
value=[],
multiselect=True,
label="Select Languages 🕹️"
)
with gr.Row():
with gr.Column(scale=3):
parameter_slider = RangeSlider(
minimum=0,
maximum=max_parameter,
label="Select Parameters. Range -> 2^x - 2^y"
)
with gr.Column(scale=1):
range_ = gr.Markdown(value=text.format(min=0, max=math.pow(2, max_parameter)))
parameter_slider.change(lambda s: text.format(min=int(pow(2,s[0])), max=int(pow(2,s[1]))), parameter_slider, range_,
show_progress="hide", trigger_mode="always_last")
with gr.Row():
with gr.Column(scale=3):
context_slider = RangeSlider(
minimum=0,
maximum=max_context,
label="Select Context length range. Range -> 2^x k - 2^y k"
)
with gr.Column(scale=1):
context_range_ = gr.Markdown(value=text.format(min=0, max=math.pow(2, max_context)))
context_slider.change(lambda s: text.format(min=int(pow(2,s[0])), max=int(pow(2,s[1]))), context_slider, context_range_,
show_progress="hide", trigger_mode="always_last")
with gr.Row():
with gr.Column():
start_date = gr.DateTime(
value=min_date,
type="string",
label="Select start date"
)
with gr.Column():
end_date = gr.DateTime(
value=max_date,
type="string",
label="Select end date"
)
with gr.Row():
input_pricing_slider = RangeSlider(
minimum=0,
maximum=max_input_price,
value=(0, max_input_price),
label="Select Price range /1M input tokens"
)
output_pricing_slider = RangeSlider(
minimum=0,
maximum=max_output_price,
value=(0, max_output_price),
label="Select Price range /1M output tokens"
)
with gr.Row():
with gr.Column():
multimodal_checkbox = gr.CheckboxGroup(
choices=['Image', 'Multi-Image', 'Audio', 'Video'],
value=[],
label="Select additional Modalities",
)
with gr.Column():
open_weight_checkbox = gr.CheckboxGroup(
choices=['Open', 'Commercial'],
value=['Open', 'Commercial'],
label="Filter Open-weight model or commercial model",
)
with gr.Column():
license_checkbox = gr.CheckboxGroup(
choices=licenses,
value=licenses,
label="Filter based on the type of License",
)
with gr.Row():
"""
Main Leaderboard Row
"""
leaderboard_table = gr.Dataframe(
value=short_leaderboard,
elem_id="text-leaderboard-table",
interactive=False,
visible=True,
height=800,
datatype=['html', 'number', 'number', 'date', 'number', 'number', 'number', 'number', 'html']
)
dummy_leaderboard_table = gr.Dataframe(
value=text_leaderboard,
elem_id="dummy-leaderboard-table",
interactive=False,
visible=False
)
lang_dropdown.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
parameter_slider.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
input_pricing_slider.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
output_pricing_slider.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
multimodal_checkbox.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
open_weight_checkbox.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
context_slider.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
start_date.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
end_date.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
license_checkbox.change(
filter,
[dummy_leaderboard_table, lang_dropdown, parameter_slider,
input_pricing_slider, output_pricing_slider, multimodal_checkbox,
context_slider, open_weight_checkbox, start_date, end_date, license_checkbox],
[leaderboard_table],
queue=True
)
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
"""
|