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# Utility functions for filtering the dataframe
import pandas as pd
def filter_cols(df):
df = df[[
'Model Name',
'Clemscore',
'Input $/1M tokens',
'Output $/1M tokens',
'Latency (s)',
'Context Size (k)',
'Parameters (B)',
'Release Date',
'License'
]]
return df
def filter(df, language_list, parameters, input_price, output_price, multimodal,
context, open_weight, start, end, license ):
if not df.empty: # Check if df is non-empty
df = df[df['Languages'].apply(lambda x: all(lang in x for lang in language_list))]
if not df.empty:
# Split dataframe by Open Weight
open_weight_true = df[df['Open Weight'] == True]
open_weight_false = df[df['Open Weight'] == False]
# Get max parameter size for open weight models
max_parameter_size = open_weight_true['Parameters (B)'].max() if not open_weight_true.empty else 0
# Filter only the open weight models based on parameters
if not open_weight_true.empty:
if parameters[1] >= max_parameter_size:
filtered_open = open_weight_true[
(open_weight_true['Parameters (B)'] >= parameters[0])
]
else:
filtered_open = open_weight_true[
(open_weight_true['Parameters (B)'] >= parameters[0]) &
(open_weight_true['Parameters (B)'] <= parameters[1])
]
# Combine filtered open weight models with unfiltered commercial models
df = pd.concat([filtered_open, open_weight_false])
if not df.empty: # Check if df is non-empty
df = df[(df['Input $/1M tokens'] >= input_price[0]) & (df['Input $/1M tokens'] <= input_price[1])]
if not df.empty: # Check if df is non-empty
df = df[(df['Output $/1M tokens'] >= output_price[0]) & (df['Output $/1M tokens'] <= output_price[1])]
print("Price")
print(df)
if not df.empty: # Check if df is non-empty
if "Image" in multimodal:
df = df[df['Image'] == True]
if "Multi-Image" in multimodal:
df = df[df['Multiple Image'] == True]
if "Audio" in multimodal:
df = df[df['Audio'] == True]
if "Video" in multimodal:
df = df[df['Video'] == True]
# if not df.empty: # Check if df is non-empty
# df = df[(df['Context Size (k)'] >= (context[0])) & (df['Context Size (k)'] <= (context[1]))]
print("Modality")
print(df)
if not df.empty: # Check if df is non-empty
if "Open" in open_weight and "Commercial" not in open_weight:
df = df[df['Open Weight'] == True]
elif "Commercial" in open_weight and "Open" not in open_weight:
df = df[df['Open Weight'] == False]
elif "Open" not in open_weight and "Commercial" not in open_weight:
# Return empty DataFrame with same columns
df = pd.DataFrame(columns=df.columns)
if not df.empty: # Check if df is non-empty
df = df[df['License Name'].apply(lambda x: any(lic in x for lic in license))]
# Convert 'Release Date' to int temporarily
if not df.empty: # Check if df is non-empty
df['Temp Date'] = pd.to_datetime(df['Temp Date']).astype(int) // 10**9 # Convert to seconds since epoch
# Convert start and end to int (seconds since epoch)
start = int(pd.to_datetime(start).timestamp())
end = int(pd.to_datetime(end).timestamp())
# Filter based on the converted 'Release Date'
if not df.empty: # Check if df is non-empty
df = df[(df['Temp Date'] >= start) & (df['Temp Date'] <= end)]
df = filter_cols(df)
df = df.sort_values(by='Clemscore', ascending=False)
print(df)
return df # Return the filtered dataframe
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