add equiformer, escn; add leaderboard

serve/models/leaderboard.py

@@ -0,0 +1,116 @@
+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)