import streamlit as st
import pandas as pd
import datetime
from huggingface_hub import HfApi
import json

# 定义一些全局变量或参数
MAX_SUBMISSIONS_PER_DAY = 3  # 每天的最大提交次数
submissions_log = {}  # 用于记录用户提交的次数

# 获取用户的唯一ID（使用Hugging Face的OAuth2身份验证）
def get_user_id():
    api = HfApi()
    user_info = api.whoami(token=st.secrets["hf_api_token"])
    return user_info["name"]

# 检查用户当天的提交次数
def check_submission_limit(user_id):
    today = datetime.date.today()
    if user_id in submissions_log:
        if submissions_log[user_id]["date"] == today:
            return submissions_log[user_id]["count"] < MAX_SUBMISSIONS_PER_DAY
        else:
            submissions_log[user_id] = {"date": today, "count": 0}
            return True
    else:
        submissions_log[user_id] = {"date": today, "count": 0}
        return True

# 更新用户的提交记录
def update_submission_count(user_id):
    submissions_log[user_id]["count"] += 1

# CSS样式
st.markdown("""
<style>
h1 {
    font-size: 2.5em;  /* 标题字体大小 */
}
.stDataFrame {
    font-family: Helvetica;
}
.dataframe th, .dataframe td {
    width: auto;
    min-width: 500px; 
}
</style>
""", unsafe_allow_html=True)

# 标题
st.title('🏆AEOLLM Leaderboard')

# 描述
st.markdown("""
This leaderboard is used to show the performance of the **automatic evaluation methods of LLMs** submitted by the **AEOLLM team** on four tasks:
- Summary Generation (SG)
- Non-Factoid QA (NFQA)
- Dialogue Generation (DG)
- Text Expansion (TE).
            
Details of AEOLLLM can be found at the link: [https://cjj826.github.io/AEOLLM/](https://cjj826.github.io/AEOLLM/)
            
Submit your result here (.json): 
""", unsafe_allow_html=True)

# user_id = get_user_id()
# st.write(f"欢迎, {user_id}!")

# # 检查用户的提交限制
# if check_submission_limit(user_id):
#     st.write(f"您今天还可以提交 {MAX_SUBMISSIONS_PER_DAY - submissions_log[user_id]['count']} 次。")
    
#     # 创建文件上传组件
#     uploaded_file = st.file_uploader("选择一个文件", type=["json"])
    
#     # 创建一个按钮，用户点击后提交文件
#     if st.button("提交文件"):
#         if uploaded_file is not None:
#             # 读取文件内容
#             file_content = uploaded_file.read().decode("utf-8")
            
#             # 如果是JSON文件，解析内容
#             try:
#                 json_data = json.loads(file_content)
#                 st.success("文件已成功提交！")
#                 st.json(json_data)  # 显示上传的JSON数据
#             except json.JSONDecodeError:
#                 st.error("无法解析JSON文件，请确保文件格式正确。")
#         else:
#             st.warning("请先上传一个文件！")
            
# else:
#     st.error("您今天的提交次数已达上限。请明天再试。")

# 创建示例数据
SG = {
    "methods": ["Model A", "Model B", "Model C"],
    "team": ["U1", "U2", "U3"],
    "acc": [0.75, 0.64, 0.83],
    "tau": [0.05, 0.28, 0.16],
    "s": [0.12, 0.27, 0.18],
}
df1 = pd.DataFrame(SG)

NFQA = {
    "methods": ["Model A", "Model B", "Model C"],
    "team": ["U1", "U2", "U3"],
    "acc": [0.75, 0.64, 0.83],
    "tau": [0.05, 0.28, 0.16],
    "s": [0.12, 0.27, 0.18]
}
df2 = pd.DataFrame(NFQA)

DG = {
    "methods": ["Model A", "Model B", "Model C"],
    "team": ["U1", "U2", "U3"],
    "acc": [0.75, 0.64, 0.83],
    "tau": [0.05, 0.28, 0.16],
    "s": [0.12, 0.27, 0.18]
}
df3 = pd.DataFrame(DG)

TE = {
    "methods": ["Model A", "Model B", "Model C"],
    "team": ["U1", "U2", "U3"],
    "acc": [0.75, 0.64, 0.83],
    "tau": [0.05, 0.28, 0.16],
    "s": [0.12, 0.27, 0.18]
}
df4 = pd.DataFrame(TE)

# 创建标签页
tab1, tab2, tab3, tab4 = st.tabs(["SG", "NFQA", "DG", "TE"])

# 在标签页 1 中添加内容
with tab1:
    st.header("Summary Generation")
    st.dataframe(df1, use_container_width=True)

# 在标签页 2 中添加内容
with tab2:
    st.header("Non-Factoid QA")
    st.dataframe(df2, use_container_width=True)

# 在标签页 3 中添加内容
with tab3:
    st.header("Dialogue Generation")
    st.dataframe(df3, use_container_width=True)

# 在标签页 4 中添加内容
with tab4:
    st.header("Text Expansion")
    st.dataframe(df4, use_container_width=True, )