File size: 9,677 Bytes
2f22782 197fb1d 2f22782 197fb1d 2f22782 197fb1d f9d3528 197fb1d 2f22782 197fb1d f3ddcb4 197fb1d 2f22782 74625f5 2f22782 74625f5 b8a7dc4 74625f5 2f22782 74625f5 f9d3528 74625f5 2f22782 74625f5 2f22782 |
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 |
import streamlit as st
import pandas as pd
import os
import time
import json
import tqdm
import datasets
from huggingface_hub import HfApi
from huggingface_hub import hf_hub_download
from utils.upload_hub import upload_scores_to_hub, file_name_decode
from utils.Evaluation_answer_txt import Evaluation_answer_txt
from utils.hub_info import get_model_size
from utils.filterable_dataframe import filterable_dataframe
# st.set_page_config(layout="wide")
st.set_page_config(layout="centered")
st.markdown(
f"""
<style>
.appview-container .main .block-container{{
max-width: 80%;
padding: 50px;
}}
</style>
""",
unsafe_allow_html=True
)
@st.cache_data
def download_gold_answer(repo, filename, token, force_download=False):
ret = hf_hub_download(repo_id=repo, repo_type='dataset', filename=filename, token=token, force_download=force_download)
return ret
HUB_TOKEN = st.secrets['hf']
HUB_API = HfApi(token=HUB_TOKEN)
LEADERBOARD_DATASET_REPO = 'zhaorui-nb/leaderboard-score'
# Setting1 Setting2 Setting3
ANSWER_REPO = 'zhaorui-nb/leaderboard-answer'
GET_GOLD_ANSWER_PATH = {
'Setting1': download_gold_answer(ANSWER_REPO, 'dataset/Setting1_test_answer.txt', HUB_TOKEN),
'Setting2': download_gold_answer(ANSWER_REPO, 'dataset/Setting2_test_answer.txt', HUB_TOKEN),
'Setting3': download_gold_answer(ANSWER_REPO, 'dataset/Setting3_test_answer.txt', HUB_TOKEN)
}
# cache the dataset in the session state
def get_leaderboard_df():
with st.spinner('Loading leaderboard data...'):
if st.session_state.get('leaderboard_df') is None:
dataset = datasets.load_dataset(LEADERBOARD_DATASET_REPO)
df = pd.DataFrame(dataset['train'])
# replace model name column @ to /
df['model name'] = df['model name'].str.replace('@', '/')
#### add model size
df['model size'] = df['model name'].apply(lambda x: get_model_size(x, token=HUB_TOKEN))
st.session_state['leaderboard_df'] = df
return df
else:
return st.session_state['leaderboard_df']
st.title('De-identification Model Leaderboard')
try:
with st.container():
# columns ['model name', 'dataset', 'method', 'file name', 'submitter', 'MICRO precision', 'MICRO recall', 'MICRO f1', 'MACRO precision', 'MACRO recall', 'MACRO f1', 'detail result']
df = get_leaderboard_df()
with st.sidebar: # st.expander("Leaderboard", expanded=True):
st.header("Leaderboard filter control panel")
default_columns = [c for c in df.columns if c not in ['file name', 'submitter', 'MICRO precision', 'MICRO recall', 'MACRO precision', 'MACRO recall', 'detail result']]
selected_columns = st.multiselect('Select columns to display', df.columns, default=default_columns)
# add filterable dataframe
filtered_df = filterable_dataframe(df)
# hit the user can filter the leaderboard at the sidebar
st.write("setting the filter at the sidebar")
leaderboard_df = st.dataframe(filtered_df[selected_columns], selection_mode='multi-row', on_select='rerun', key='leaderboard')
st.subheader("Detail Result")
det_ind = st.session_state.leaderboard['selection']['rows']
if len(det_ind) == 0:
st.write(f'Please check the boxes in the Model Leaderboard to view the detailed results.')
else:
col_detial = st.columns(len(det_ind))
for i, dind in enumerate(det_ind):
with col_detial[i]:
dis = f"{df.iloc[dind]['model name']}___{df.iloc[dind]['dataset']}___{df.iloc[dind]['method']}"
color = [st.success, st.info, st.warning, st.error]
color[i % 4](dis)
dic = json.loads(df.iloc[dind]['detail result'])
dt_df = pd.DataFrame(dic).T
st.dataframe(dt_df)
except Exception as e:
st.error(f"Error: {e}")
st.markdown("---")
# ############################################################################################################
# ############################################### Evaluation_answer_txt
# ############################################################################################################
model_name_input = ''
dataset_input = ''
method_input = ''
file_name = ''
submitter_input = ''
if 'score_json' not in st.session_state:
st.session_state['score_json'] = None
@st.cache_data()
def get_file_info(uploaded_file):
filename_info = file_name_decode(uploaded_file.name)
return filename_info
@st.cache_data()
def eval_answer_txt(set_name, uploaded_file):
print(f"eval_answer_txt: {time.time()}" , set_name)
if set_name not in GET_GOLD_ANSWER_PATH:
return None
gold_answer_txt = GET_GOLD_ANSWER_PATH[set_name]
eval = Evaluation_answer_txt(gold_answer_txt, uploaded_file)
score_json = eval.eval()
return score_json
def clear_score_json():
st.session_state['score_json'] = None
st.title("Model Evaluation")
st.write("Support file naming: [{Organization@Model}][{Dataaset}][{Method}]{Filename}.txt")
col_upload = st.columns([3,1])
with col_upload[0]:
uploaded_file = st.file_uploader("Please upload the answer.txt file", type=["txt"], key="uploaded_file", on_change=clear_score_json)
with col_upload[1]:
if not uploaded_file:
st.warning("please upload file")
st.session_state['score_json'] = None
else:
st.success("file uploaded successfully")
filename_info = get_file_info(uploaded_file)
if filename_info:
model_name_input = filename_info['model_name']
dataset_input = filename_info['dataset']
method_input = filename_info['method']
file_name = filename_info['file_name']
col_score = st.columns([7,5])
if uploaded_file:
with col_score[1], st.container(border=True):
model_name_input = st.text_input("model name", model_name_input)
dataset_input = st.text_input("dataset", dataset_input)
method_input = st.text_input("method", method_input)
file_name = st.text_input("file name", file_name)
submitter_input = st.text_input("submitter", submitter_input)
check_all_fill_in = model_name_input and dataset_input and method_input and file_name and submitter_input
col_sumit_and_recalculate = st.columns(2)
with col_sumit_and_recalculate[0]:
calculate_btn = st.button("calculate", type='secondary', use_container_width=True)
with col_sumit_and_recalculate[1]:
submit_btn = st.button("SUBMIT", type='primary', use_container_width=True , disabled=not check_all_fill_in)
if calculate_btn or st.session_state['score_json'] is None:
set_name = dataset_input
st.session_state['score_json'] = eval_answer_txt(set_name, uploaded_file)
if st.session_state['score_json']:
st.success("evaluation success")
else:
st.error("evaluation failed, please check the file content or set the correct dataset name.")
if st.session_state['score_json']:
with col_score[0], st.container(border=True):
df = pd.DataFrame(st.session_state['score_json']).T
# split the column MICRO_AVERAGE and MACRO_AVERAGE into another dataframe
tag_df = df.drop(["MICRO_AVERAGE", "MACRO_AVERAGE"], axis=0)
avg_df = df.loc[["MICRO_AVERAGE", "MACRO_AVERAGE"]]
col_sort_func = st.columns(2)
with col_sort_func[0]:
sorted_column = st.selectbox("选择排序列", df.columns)
with col_sort_func[1]:
ascending = st.radio("Sort Order", ["Ascending", "Descending"])
tag_df = tag_df.sort_values(by=sorted_column, ascending=ascending=="Ascending")
st.dataframe(pd.concat([tag_df, avg_df]), use_container_width=True)
if not check_all_fill_in:
st.warning("Please fill in the complete information.")
if submit_btn:
if st.session_state['score_json']:
score_json = st.session_state['score_json']
leaderboard_dict = {
"model name": model_name_input,
"dataset": dataset_input,
"method": method_input,
"file name": file_name,
"submitter": submitter_input,
"MICRO precision": score_json["MICRO_AVERAGE"]["precision"],
"MICRO recall": score_json["MICRO_AVERAGE"]["recall"],
"MICRO f1": score_json["MICRO_AVERAGE"]["f1"],
"MACRO precision": score_json["MACRO_AVERAGE"]["precision"],
"MACRO recall": score_json["MACRO_AVERAGE"]["recall"],
"MACRO f1": score_json["MACRO_AVERAGE"]["f1"],
"detail result": json.dumps(score_json,indent=4) #score_json
}
repo_file_path = f'data/train-[{model_name_input}][{dataset_input}][{method_input}][{file_name}].json'
upload_res = upload_scores_to_hub(HUB_API, leaderboard_dict, repo_file_path, hub_repo=LEADERBOARD_DATASET_REPO)
if upload_res:
st.success(f"submit success")
st.success(f"your score at here: {upload_res}")
else:
st.error("submit failed")
|