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"""UI components for the Safe Scan competition dashboard."""
import streamlit as st
import pandas as pd
from typing import Dict, Any
def render_competition_header():
"""Render the main header of the competition dashboard."""
st.markdown("<h2 style='text-align: center; font-size: 28px;'>Competitions</h2>", unsafe_allow_html=True)
def render_competition_list(competitions: list, leader_info: Dict[str, Any]):
"""Render the list of competitions with their details.
Args:
competitions: List of competition names
leader_info: Dictionary containing leader information for each competition
"""
cols = st.columns([1, 3, 2, 2, 3])
headers = ["Index", "Competition Name", "Date", "Winning hotkey"]
for col, header in zip(cols, headers):
col.write(header)
for index, competition in enumerate(competitions, start=1):
leader_info_comp = leader_info.get(competition, {})
cols = st.columns([1, 3, 2, 2, 3])
cols[0].write(index)
if cols[1].button(competition):
st.session_state.selected_competition = competition
cols[2].write(leader_info_comp.get("Date", "N/A"))
cols[3].write(leader_info_comp.get("Miner hotkey", "N/A"))
def render_competition_details(
competition_name: str,
competition_summary_df: pd.DataFrame,
models_evaluation_df: pd.DataFrame,
highlight_score_column
):
"""Render detailed view for a selected competition.
Args:
competition_name: Name of the selected competition
competition_summary_df: DataFrame containing competition summary
models_evaluation_df: DataFrame containing model evaluations
highlight_score_column: Function to highlight score column
"""
st.subheader(f"Competition: {competition_name}")
miner_hotkey_search = st.text_input("Search for Miner Hotkey", "")
st.subheader("Competition Summary")
if not competition_summary_df.empty:
filtered_summary = competition_summary_df
if miner_hotkey_search:
filtered_summary = competition_summary_df[
competition_summary_df["Winning Hotkey"].str.contains(miner_hotkey_search, na=False, case=False)
]
st.dataframe(filtered_summary, height=500, hide_index=True)
else:
st.warning("No competition summary data available.")
st.subheader("Models Evaluation")
if not models_evaluation_df.empty:
filtered_models = models_evaluation_df
if miner_hotkey_search:
filtered_models = models_evaluation_df[
models_evaluation_df["Miner hotkey"].str.contains(miner_hotkey_search, na=False, case=False)
]
st.dataframe(filtered_models.style.apply(highlight_score_column, axis=0), height=500, hide_index=True)
else:
st.warning("No models evaluation data available.")
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