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import streamlit as st
import wandb
import pandas as pd
import os
import time
import datetime
from typing import Dict, Any
from utils import fetch_competition_summary, fetch_models_evaluation, highlight_score_column, get_competitions, get_latest_evaluation_time
# from dotenv import load_dotenv
from cfg import *
# load_dotenv()
st.set_page_config(layout="wide")
table_font_size = 16
st.markdown(TABLE_STYLE, unsafe_allow_html=True)
### WANDB
# Access the API key from the environment variable
wandb_api_key = os.getenv('WANDB_API_KEY')
# Log in to wandb using the API key
if wandb_api_key:
wandb.login(key=wandb_api_key)
# wandb.login()
else:
st.error("WANDB_API_KEY not found in environment variables.")
wandb_api = wandb.Api()
@st.cache_data
def update_leader_info(
leader_info: Dict[str, Dict[str, Any]],
competition: str,
best_model: Dict[str, Any]
) -> Dict[str, Any]:
if leader_info.get(competition) is None:
leader_info[competition] = {
"Miner hotkey": best_model["Miner hotkey"],
"Date": time.strftime("%Y-%m-%d"),
# "Days on Top": 1
}
else:
if leader_info[competition]["Miner hotkey"] == best_model["Miner hotkey"]:
# count the number of days on top
start_date = datetime.datetime.strptime(leader_info[competition]["Date"], "%Y-%m-%d")
current_date = datetime.datetime.now()
days_on_top = (current_date - start_date).days
# leader_info[competition]["Days on Top"] = days_on_top + 1
else:
leader_info[competition]["Miner hotkey"] = best_model["Miner hotkey"]
leader_info[competition]["Date"] = time.strftime("%Y-%m-%d")
# leader_info[competition]["Days on Top"] = 1
return leader_info[competition]
@st.cache_data()
def load_competition_data(last_update_time=None):
competition_summaries = {}
model_evaluations = {}
competitions = get_competitions(CONFIG_URL)
for competition_info in competitions:
competition = competition_info[0]
competition_summaries[competition] = fetch_competition_summary(wandb_api, ENTITY, competition)
model_evaluations[competition] = fetch_models_evaluation(wandb_api, ENTITY, competition)
last_update_time = time.time()
return competition_summaries, model_evaluations, last_update_time
# Streamlit app main function
def main():
st.markdown(HEADER, unsafe_allow_html=True)
competitions = get_competitions(CONFIG_URL)
if 'last_update_time' not in st.session_state:
st.session_state.last_update_time = None
if "leader_info" not in st.session_state:
st.session_state.leader_info = {}
if 'selected_competition' not in st.session_state:
st.session_state.selected_competition = None
if st.session_state.last_update_time is None or (time.time() - st.session_state.last_update_time > UPDATE_INTERVAL):
competition_summaries, model_evaluations, st.session_state.last_update_time = load_competition_data(st.session_state.last_update_time)
for competition_info in competitions:
competition = competition_info[0]
if not competition_summaries[competition].empty:
# get only competition sumaries that heppen after latest evaluation time
evaluation_timexd = get_latest_evaluation_time(competition_info[1])
print(type(evaluation_timexd))
evaluation_time = pd.Timestamp(evaluation_timexd)
filtered_competition_summaries = competition_summaries[competition][competition_summaries[competition]["Created At"] > evaluation_time]
# get all winning hotkeys and number of wins
winning_hotkeys = filtered_competition_summaries["Winning Hotkey"].value_counts()
# if not empty, get the best hotkey
if not winning_hotkeys.empty:
best_hotkey = winning_hotkeys.idxmax()
# Filter models for the best hotkey
best_model_filtered = model_evaluations[competition][model_evaluations[competition]["Miner hotkey"] == best_hotkey]
# Check if the filtered DataFrame is not empty
if not best_model_filtered.empty:
best_model = best_model_filtered.iloc[0]
st.session_state.leader_info[competition] = update_leader_info(st.session_state.leader_info, competition, best_model)
else:
st.warning(f"No model found for the best hotkey: {best_hotkey} in competition {competition}.")
else:
st.session_state.leader_info[competition] = {
"Miner hotkey": "N/A",
"Date": "N/A",
# "Days on Top": "N/A"
}
else:
competition_summaries, model_evaluations, _ = load_competition_data(st.session_state.last_update_time)
st.subheader("###")
st.markdown("<h2 style='text-align: center; font-size: 28px;'>Competitions</h2>", unsafe_allow_html=True)
st.write("#### Select a competition to view more details and rankings.")
# Create a header for the table
cols = st.columns([1, 3, 2, 2, 3])
headers = ["Index", "Competition Name", "Date", "Miner hotkey"]
for col, header in zip(cols, headers):
col.write(header)
for index, competition_info in enumerate(competitions, start=1):
competition = competition_info[0]
leader_info = st.session_state.get("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.get("Date", "N/A"))
cols[3].write(leader_info.get("Miner hotkey", "N/A"))
# cols[4].write(leader_info.get("Days on Top", "N/A"))
if st.session_state.selected_competition:
competition_name = st.session_state.selected_competition
st.subheader(f"Competition: {competition_name}")
# Add search bar for miner hotkey
miner_hotkey_search = st.text_input("Search for Miner Hotkey", "")
st.subheader("Competition Summary")
competition_summary_df = competition_summaries.get(competition_name, pd.DataFrame())
# Filter the competition summary dataframe by miner hotkey if a search term is entered
if miner_hotkey_search:
competition_summary_df = competition_summary_df[
competition_summary_df["Winning Hotkey"].str.contains(miner_hotkey_search, na=False, case=False)
]
if not competition_summary_df.empty:
st.dataframe(competition_summary_df, height=500, hide_index=True)
else:
st.warning("No competition summary data available.")
st.subheader("Models Evaluation")
models_evaluation_df = model_evaluations.get(competition_name, pd.DataFrame())
# Filter the models evaluation dataframe by miner hotkey if a search term is entered
if miner_hotkey_search:
models_evaluation_df = models_evaluation_df[
models_evaluation_df["Miner hotkey"].str.contains(miner_hotkey_search, na=False, case=False)
]
if not models_evaluation_df.empty:
st.dataframe(models_evaluation_df.style.apply(highlight_score_column, axis=0), height=500, hide_index=True)
else:
st.warning("No models evaluation data available.")
else:
st.write("Please select a competition to view details.")
# Run the app
if __name__ == "__main__":
main() |