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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, sorted):
folder = f"./results/{folder_name}/"
display_names = {
'ASR': 'Automatic Speech Recognition',
'SQA': 'Speech Question Answering',
'SI': 'Speech Instruction',
'AC': 'Audio Captioning',
'ASQA': 'Audio Scene Question Answering',
'AR': 'Accent Recognition',
'GR': 'Gender Recognition',
'ER': 'Emotion Recognition'
}
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
sort_by = dataset_name.replace('-', '_').lower()
chart_data = chart_data.sort_values(by=[sort_by], ascending=ascend)
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[category_name]}"},
"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)
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