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import streamlit as st
import pandas as pd
import os
from utils.Evaluation_answer_txt import Evaluation_answer_txt
from utils.upload_hub import upload_scores_to_hub, file_name_decode
import time
import json
import datasets
from huggingface_hub import HfApi
from huggingface_hub import hf_hub_download
# 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'])
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()
# replace model name column @ to /
df['model name'] = df['model name'].str.replace('@', '/')
# remove the detail result column
default_columns = [c for c in df.columns if c not in ['detail result']]
selected_columns = st.multiselect('Select columns to display', df.columns, default=default_columns)
leaderboard_df = st.dataframe(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 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")
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