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|
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def filter_cols(df): |
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|
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df = df[[ |
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'model_name', |
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'input_price', |
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'output_price', |
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'release_date', |
|
'context_size', |
|
'average_clemscore', |
|
'average_latency', |
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'parameter_size', |
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]] |
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|
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return df |
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|
|
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])] |
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df = df[(df['input_price'] >= input_price[0]) & (df['input_price'] <= input_price[1])] |
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df = df[(df['output_price'] >= output_price[0]) & (df['output_price'] <= output_price[1])] |
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|
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df = filter_cols(df) |
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return df |
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|
|
