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import streamlit as st
import pandas as pd
from pathlib import Path
DATA_DIR = Path("mlip_arena/tasks/diatomics")
methods = ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF"]
dfs = [pd.read_json(DATA_DIR / method.lower() / "homonuclear-diatomics.json") for method in methods]
df = pd.concat(dfs, ignore_index=True)
table = pd.DataFrame(columns=["Model", "No. of supported elements", "No. of reversed forces", "Energy-consistent forces"])
for method in df["method"].unique():
rows = df[df["method"] == method]
new_row = {
"Model": method,
"No. of supported elements": len(rows["name"].unique()),
"No. of reversed forces": None, # Replace with actual logic if available
"Energy-consistent forces": None # Replace with actual logic if available
}
table = pd.concat([table, pd.DataFrame([new_row])], ignore_index=True)
# Define the data
# data = {
# "Metrics": [
# "No. of supported elements",
# "No. of reversed forces",
# "Energy-consistent forces",
# ],
# "MACE-MP(M)": ["10", "5", "Yes"],
# "CHGNet": ["20", "3", "No"],
# "Equiformer": ["15", "7", "Yes"]
# }
# # Convert the data to a DataFrame
# df = pd.DataFrame(data)
# # Set the 'Metrics' column as the index
# df.set_index("Metrics", inplace=True)
# # Transpose the DataFrame
# df = df.T
# Apply custom CSS to center the table
# Create the Streamlit table
table.set_index("Model", inplace=True)
s = table.style.background_gradient(
cmap="Spectral",
subset=["No. of supported elements"],
vmin=0, vmax=120
)
st.markdown("# Leaderboard")
st.dataframe(s, use_container_width=True)
# Define custom CSS for table
# custom_css = """
# <style>
# table {
# width: 100%;
# border-collapse: collapse;
# }
# th, td {
# border: 1px solid #ddd;
# padding: 8px;
# }
# th {
# background-color: #4CAF50;
# color: white;
# text-align: left;
# }
# tr:nth-child(even) {
# background-color: #f2f2f2;
# }
# tr:hover {
# background-color: #ddd;
# }
# </style>
# """
# # Display the table with custom CSS
# st.markdown(custom_css, unsafe_allow_html=True)
# st.markdown(table.to_html(index=False), unsafe_allow_html=True)
# import numpy as np
# import plotly.figure_factory as ff
# import streamlit as st
# st.markdown("# Dashboard")
# # Add histogram data
# x1 = np.random.randn(200) - 2
# x2 = np.random.randn(200)
# x3 = np.random.randn(200) + 2
# # Group data together
# hist_data = [x1, x2, x3]
# group_labels = ["Group 1", "Group 2", "Group 3"]
# # Create distplot with custom bin_size
# fig = ff.create_distplot(
# hist_data, group_labels, bin_size=[.1, .25, .5]
# )
# # Plot!
# st.plotly_chart(fig, use_container_width=True)
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