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# Utility functions for filtering the dataframe
import pandas as pd
def filter_cols(df):
df = df[[
'Model Name',
'Input $/1M',
'Output $/1M',
'Average Clemscore',
'Context Size (k)',
'Average Latency (s)',
'Parameter Size (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: # Check if df is non-empty
open_weight_df = df[df['Open Weight'] == True]
if not open_weight_df.empty: # Check if filtered df is non-empty
max_parameter_size = open_weight_df['Parameter Size (B)'].max()
print(f"MMMMMMMMMMMMMMMMMMMMMMm: {max_parameter_size}")
if parameters[1] >= max_parameter_size:
df = df[(df['Parameter Size (B)'] >= parameters[0])]
elif parameters[1] < max_parameter_size:
df = df[(df['Parameter Size (B)'] >= parameters[0]) & (df['Parameter Size (B)'] <= parameters[1])]
if not df.empty: # Check if df is non-empty
df = df[(df['Input $/1M'] >= input_price[0]) & (df['Input $/1M'] <= input_price[1])]
if not df.empty: # Check if df is non-empty
df = df[(df['Output $/1M'] >= output_price[0]) & (df['Output $/1M'] <= output_price[1])]
if not df.empty: # Check if df is non-empty
if "Image" in multimodal:
df = df[df['Multimodality Image'] == True]
if "Multi-Image" in multimodal:
df = df[df['Multimodality Multiple Image'] == True]
if "Audio" in multimodal:
df = df[df['Multimodality Audio'] == True]
if "Video" in multimodal:
df = df[df['Multimodality 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]))]
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]
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)
return df # Return the filtered dataframe
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