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import streamlit as st
import asyncio
from src.core.styles import CUSTOM_CSS
from src.components.header import render_header
from src.components.filters import render_table_filters, render_plot_filters
from src.components.visualizations import (
render_performance_plots,
render_leaderboard_table,
)
from src.services.firebase import fetch_leaderboard_data
# Configure the page
st.set_page_config(
page_title="AI-Phone Leaderboard",
page_icon="src/static/images/favicon.png",
layout="wide",
initial_sidebar_state="expanded",
)
# Apply custom CSS
st.markdown(CUSTOM_CSS, unsafe_allow_html=True)
async def main():
# Render header
render_header()
# Fetch initial data
full_df = await fetch_leaderboard_data()
if full_df.empty:
st.info("No benchmark data available yet!")
return
# Get unique values for filters
models = sorted(full_df["Model"].unique())
benchmarks = sorted(full_df["Benchmark"].unique())
platforms = sorted(full_df["Platform"].unique())
devices = sorted(full_df["Normalized Device ID"].unique())
# Render table filters and get selections
(
selected_model_table,
selected_benchmark_table,
selected_platform_table,
selected_device_table,
) = render_table_filters(models, benchmarks, platforms, devices)
# Filter data for table
table_df = full_df.copy()
if selected_model_table != "All":
table_df = table_df[table_df["Model"] == selected_model_table]
if selected_benchmark_table != "All":
table_df = table_df[table_df["Benchmark"] == selected_benchmark_table]
if selected_platform_table != "All":
table_df = table_df[table_df["Platform"] == selected_platform_table]
if selected_device_table != "All":
table_df = table_df[table_df["Normalized Device ID"] == selected_device_table]
# Render leaderboard table
render_leaderboard_table(table_df)
# Performance plots section
st.subheader("Performance Comparison")
# Render plot filters and get selections
selected_model_plot, selected_benchmark_plot = render_plot_filters(
models, benchmarks
)
# Filter data for plots
plot_df = full_df[
(full_df["Model"] == selected_model_plot)
& (full_df["Benchmark"] == selected_benchmark_plot)
]
# Render performance plots
render_performance_plots(plot_df, selected_model_plot)
if __name__ == "__main__":
asyncio.run(main()) |