# Utility functions for filtering the dataframe

def filter_cols(df):

    df = df[[
    'model_name', 
    'input_price', 
    'output_price',
    'release_date', 
    'context_size', 
    'average_clemscore',
    'average_latency',
    'parameter_size',
    ]]

    return df


def filter(df, language_list, clemscore, input_price, output_price):
    df = df[df['languages'].apply(lambda x: all(lang in x for lang in language_list))]
    df = df[(df['average_clemscore'] >= clemscore[0]) & (df['average_clemscore'] <= clemscore[1])]
    df = df[(df['input_price'] >= input_price[0]) & (df['input_price'] <= input_price[1])]
    df = df[(df['output_price'] >= output_price[0]) & (df['output_price'] <= output_price[1])]

    df = filter_cols(df)
    return df  # Return the filtered dataframe