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import pandas as pd
import streamlit as st
# Define color maps for both light and dark modes
COLOR_MAP = {
"light": {
"yellow": "background-color: rgba(255, 255, 204, 0.5)", # Reasoning models
"green": "background-color: rgba(227, 251, 233, 0.5)", # Linear attention hybrid
"blue": "background-color: rgba(230, 244, 255, 0.5)" # SSM hybrid models
},
}
def get_color_map():
"""Returns the appropriate color map based on the current theme."""
return COLOR_MAP["light"]
def style_zero_context(df):
"""
Similar approach to style_long_context:
1) color rows based on model name
2) numeric formatting
"""
import pandas as pd
# Example color dict, tweak as needed:
color_mapping = {
"minimax-text-01": get_color_map()["green"],
"jamba-1.5-large": get_color_map()["blue"],
"deepseek-r1": get_color_map()["yellow"],
"o1-mini": get_color_map()["yellow"],
"qwq-32b-preview": get_color_map()["yellow"],
# Add any other special-cased models here
# "o1-mini": COLOR_MAP["yellow"], etc.
}
styler = df.style.apply(
lambda row: [color_mapping.get(row["Model"], "")]*len(row),
axis=1
)
# # Attach custom tooltips (optional)
# tooltips = pd.DataFrame("", index=df.index, columns=df.columns)
# if "1st<50% op" in df.columns:
# tooltips["1st<50% op"] = "First operation number with accuracy <50%"
# if "1st<10% op" in df.columns:
# tooltips["1st<10% op"] = "First operation number with accuracy <10%"
# if "Avg. Acc op≤30" in df.columns:
# tooltips["Avg. Acc op≤30"] = "Average accuracy of first 30 operations"
# styler = styler.set_tooltips(tooltips)
# Apply numeric formatting
styler = styler.format({
"Symbolic": "{:,.2f}", # Format as number with thousands separator and 1 decimal place
"Medium": "{:,.2f}", # Format as number with thousands separator and 2 decimal places
"Hard": "{:,.2f}", # Format as number with thousands separator and 2 decimal places
"1st<50% op": "{:,.0f}", # Format as plain integer (no decimal places)
"1st<10% op": "{:,.0f}", # Format as plain integer (no decimal places)
"Avg. Acc op≤30": "{:.4f}", # Format with 4 decimal places
"Average↑": "{:,.2f}" # Format as number with thousands separator and 2 decimal places
})
return styler
# Add styling for model types
def style_long_context(df):
color_mapping = {
"minimax-text-01": get_color_map()["green"],
"jamba-1.5-large": get_color_map()["blue"]
}
return df.style.apply(
lambda row: [color_mapping.get(row["Model"], "")]*len(row),
axis=1
).format({
"8K": "{:,.2f}",
"16K": "{:,.2f}",
"32K": "{:,.2f}",
"Average↑": "{:,.2f}"
}) |