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