File size: 6,476 Bytes
565f995 abc1e1a 565f995 abc1e1a 565f995 abc1e1a 565f995 abc1e1a 565f995 abc1e1a 565f995 abc1e1a 565f995 abc1e1a 565f995 |
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 |
import streamlit as st
import pandas as pd
import numpy as np
from streamlit_echarts import st_echarts
# from streamlit_echarts import JsCode
from streamlit_javascript import st_javascript
# from PIL import Image
links_dic = {"random": "https://seaeval.github.io/",
"meta_llama_3_8b": "https://huggingface.co/meta-llama/Meta-Llama-3-8B",
"mistral_7b_instruct_v0_2": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2",
"sailor_0_5b": "https://huggingface.co/sail/Sailor-0.5B",
"sailor_1_8b": "https://huggingface.co/sail/Sailor-1.8B",
"sailor_4b": "https://huggingface.co/sail/Sailor-4B",
"sailor_7b": "https://huggingface.co/sail/Sailor-7B",
"sailor_0_5b_chat": "https://huggingface.co/sail/Sailor-0.5B-Chat",
"sailor_1_8b_chat": "https://huggingface.co/sail/Sailor-1.8B-Chat",
"sailor_4b_chat": "https://huggingface.co/sail/Sailor-4B-Chat",
"sailor_7b_chat": "https://huggingface.co/sail/Sailor-7B-Chat",
"sea_mistral_highest_acc_inst_7b": "https://seaeval.github.io/",
"meta_llama_3_8b_instruct": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct",
"flan_t5_base": "https://huggingface.co/google/flan-t5-base",
"flan_t5_large": "https://huggingface.co/google/flan-t5-large",
"flan_t5_xl": "https://huggingface.co/google/flan-t5-xl",
"flan_t5_xxl": "https://huggingface.co/google/flan-t5-xxl",
"flan_ul2": "https://huggingface.co/google/flan-t5-ul2",
"flan_t5_small": "https://huggingface.co/google/flan-t5-small",
"mt0_xxl": "https://huggingface.co/bigscience/mt0-xxl",
"seallm_7b_v2": "https://huggingface.co/SeaLLMs/SeaLLM-7B-v2",
"gpt_35_turbo_1106": "https://openai.com/blog/chatgpt",
"meta_llama_3_70b": "https://huggingface.co/meta-llama/Meta-Llama-3-70B",
"meta_llama_3_70b_instruct": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct",
"sea_lion_3b": "https://huggingface.co/aisingapore/sea-lion-3b",
"sea_lion_7b": "https://huggingface.co/aisingapore/sea-lion-7b",
"qwen1_5_110b": "https://huggingface.co/Qwen/Qwen1.5-110B",
"qwen1_5_110b_chat": "https://huggingface.co/Qwen/Qwen1.5-110B-Chat",
"llama_2_7b_chat": "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf",
"gpt4_1106_preview": "https://openai.com/blog/chatgpt",
"gemma_2b": "https://huggingface.co/google/gemma-2b",
"gemma_7b": "https://huggingface.co/google/gemma-7b",
"gemma_2b_it": "https://huggingface.co/google/gemma-2b-it",
"gemma_7b_it": "https://huggingface.co/google/gemma-7b-it",
"qwen_1_5_7b": "https://huggingface.co/Qwen/Qwen1.5-7B",
"qwen_1_5_7b_chat": "https://huggingface.co/Qwen/Qwen1.5-7B-Chat",
"sea_lion_7b_instruct": "https://huggingface.co/aisingapore/sea-lion-7b-instruct",
"sea_lion_7b_instruct_research": "https://huggingface.co/aisingapore/sea-lion-7b-instruct-research",
"LLaMA_3_Merlion_8B": "https://seaeval.github.io/",
"LLaMA_3_Merlion_8B_v1_1": "https://seaeval.github.io/"}
links_dic = {k.lower().replace('_', '-') : v for k, v in links_dic.items()}
# huggingface_image = Image.open('style/huggingface.jpg')
def nav_to(value):
try:
url = links_dic[str(value).lower()]
js = f'window.open("{url}", "_blank").then(r => window.parent.location.href);'
st_javascript(js)
except:
pass
def draw(folder_name, category_name, dataset_name, metrics):
folder = f"./results/{metrics}/"
display_names = {
'SU': 'Speech Understanding',
'ASU': 'Audio Scene Understanding',
'VU': 'Voice Understanding'
}
data_path = f'{folder}/{category_name.lower()}.csv'
chart_data = pd.read_csv(data_path).round(2).dropna(axis=0)
if len(chart_data) == 0:
return
# if sorted == 'Ascending':
# ascend = True
# else:
# ascend = False
dataset_name = dataset_name.replace('-', '_').lower()
chart_data = chart_data[['Model', dataset_name]]
chart_data = chart_data.sort_values(by=[dataset_name], ascending=False)
min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1)
max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1)
columns = list(chart_data.columns)[1:]
series = []
for col in columns:
series.append(
{
"name": f"{col.replace('_', '-')}",
"type": "line",
"data": chart_data[f'{col}'].tolist(),
}
)
options = {
"title": {"text": f"{display_names[folder_name.upper()]}"},
"tooltip": {
"trigger": "axis",
"axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
"triggerOn": 'mousemove',
},
"legend": {"data": ['Overall Accuracy']},
"toolbox": {"feature": {"saveAsImage": {}}},
"grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
"xAxis": [
{
"type": "category",
"boundaryGap": False,
"triggerEvent": True,
"data": chart_data['Model'].tolist(),
}
],
"yAxis": [{"type": "value",
"min": min_value,
"max": max_value,
# "splitNumber": 10
}],
"series": series,
}
events = {
"click": "function(params) { return params.value }"
}
value = st_echarts(options=options, events=events, height="500px")
if value != None:
# print(value)
nav_to(value)
# if value != None:
# highlight_table_line(value)
### create table
st.divider()
# chart_data['Link'] = chart_data['Model'].map(links_dic)
st.dataframe(chart_data,
# column_config = {
# "Link": st.column_config.LinkColumn(
# display_text= st.image(huggingface_image)
# ),
# },
hide_index = True,
use_container_width=True)
|