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import pandas as pd
import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
import numpy as np
type_emoji = {
"RTL-Specific": "🔴",
"General": "🟢",
"Coding": "🔵"
}
def model_hyperlink(link, model_name):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def handle_special_cases(benchmark, metric):
if metric == 'Exact Matching (EM)':
benchmark = 'RTL-Repo'
elif benchmark == 'RTL-Repo':
metric = 'Exact Matching (EM)'
return benchmark, metric
def filter_RTLRepo(subset: pd.DataFrame) -> pd.DataFrame:
details = subset[['Model', 'Model URL', 'Model Type', 'Params']].drop_duplicates('Model')
filtered_df = subset[['Model', 'Score']].rename(columns={'Score': 'Exact Matching (EM)'})
filtered_df = pd.merge(filtered_df, details, on='Model', how='left')
filtered_df['Model'] = filtered_df.apply(lambda row: model_hyperlink(row["Model URL"], row["Model"]), axis=1)
filtered_df['Type'] = filtered_df['Model Type'].map(lambda x: type_emoji.get(x, ""))
filtered_df = filtered_df[['Type', 'Model', 'Params', 'Exact Matching (EM)']]
filtered_df = filtered_df.sort_values(by='Exact Matching (EM)', ascending=False).reset_index(drop=True)
# filtered_df.insert(0, '', range(1, len(filtered_df) + 1))
return filtered_df
def filter_bench(subset: pd.DataFrame) -> pd.DataFrame:
details = subset[['Model', 'Model URL', 'Model Type', 'Params']].drop_duplicates('Model')
pivot_df = subset.pivot_table(index='Model', columns='Metric', values='Score', aggfunc='mean').reset_index()
pivot_df['Average ⬆️'] = pivot_df.mean(axis=1, numeric_only=True).round(2)
pivot_df = pd.merge(pivot_df, details, on='Model', how='left')
pivot_df['Model'] = pivot_df.apply(lambda row: model_hyperlink(row["Model URL"], row["Model"]), axis=1)
pivot_df['Type'] = pivot_df['Model Type'].map(lambda x: type_emoji.get(x, ""))
pivot_df.rename(columns={'Syntax (STX)': 'STX', 'Functionality (FNC)': 'FNC', 'Synthesis (SYN)': 'SYN', 'Performance': 'Perf'}, inplace=True)
columns_order = ['Type', 'Model', 'Params', 'Average ⬆️', 'STX', 'FNC', 'SYN', 'Power', 'Perf', 'Area']
pivot_df = pivot_df[[col for col in columns_order if col in pivot_df.columns]]
pivot_df = pivot_df.sort_values(by='Average ⬆️', ascending=False).reset_index(drop=True)
# pivot_df.insert(0, '', range(1, len(pivot_df) + 1))
return pivot_df
def filter_bench_all(subset: pd.DataFrame) -> pd.DataFrame:
details = subset[['Model', 'Model URL', 'Model Type', 'Params']].drop_duplicates('Model')
pivot_df = subset.pivot_table(index='Model', columns='Metric', values='Score', aggfunc='mean').reset_index().round(2)
pivot_df['Average ⬆️'] = pivot_df.mean(axis=1, numeric_only=True).round(2)
pivot_df = pd.merge(pivot_df, details, on='Model', how='left')
pivot_df['Model'] = pivot_df.apply(lambda row: model_hyperlink(row["Model URL"], row["Model"]), axis=1)
pivot_df['Type'] = pivot_df['Model Type'].map(lambda x: type_emoji.get(x, ""))
pivot_df.rename(columns={
'Exact Matching (EM)': 'EM',
'Syntax (STX)': 'Avg STX',
'Functionality (FNC)': 'Avg FNC',
'Synthesis (SYN)': 'Avg SYN',
'Power': 'Avg Power',
'Performance': 'Avg Perf',
'Area': 'Avg Area',
}, inplace=True)
# columns_order = ['Type', 'Model', 'Params', 'Average ⬆️', 'Avg STX', 'Avg FNC', 'Avg SYN', 'Avg Power', 'Avg Perf', 'Avg Area']
columns_order = ['Type', 'Model', 'Params', 'Average ⬆️', 'Avg STX', 'Avg FNC', 'Avg SYN', 'Avg Power', 'Avg Perf', 'Avg Area']
pivot_df = pivot_df[[col for col in columns_order if col in pivot_df.columns]]
pivot_df = pivot_df.sort_values(by='Average ⬆️', ascending=False).reset_index(drop=True)
# pivot_df.insert(0, '', range(1, len(pivot_df) + 1))
return pivot_df
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