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Update app.py
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app.py
CHANGED
@@ -38,7 +38,7 @@ def add_new_eval(
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model_name = model_name_textbox
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else:
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model_name = revision_name_textbox
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-
model_name_list = csv_data['
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name_list = [name.split(']')[0][1:] for name in model_name_list]
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if revision_name_textbox not in name_list:
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col = csv_data.shape[0]
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@@ -81,7 +81,7 @@ def calculate_selected_score(df, selected_columns):
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def get_final_score(df, selected_columns):
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normalize_df = get_normalized_df(df)
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#final_score = normalize_df.drop('name', axis=1).sum(axis=1)
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for name in normalize_df.drop('
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normalize_df[name] = normalize_df[name]*DIM_WEIGHT[name]
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quality_score = normalize_df[QUALITY_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in QUALITY_LIST])
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semantic_score = normalize_df[SEMANTIC_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in SEMANTIC_LIST ])
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model_name = model_name_textbox
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else:
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model_name = revision_name_textbox
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model_name_list = csv_data['Model Name']
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name_list = [name.split(']')[0][1:] for name in model_name_list]
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if revision_name_textbox not in name_list:
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col = csv_data.shape[0]
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def get_final_score(df, selected_columns):
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normalize_df = get_normalized_df(df)
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#final_score = normalize_df.drop('name', axis=1).sum(axis=1)
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for name in normalize_df.drop('Model Name', axis=1):
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normalize_df[name] = normalize_df[name]*DIM_WEIGHT[name]
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quality_score = normalize_df[QUALITY_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in QUALITY_LIST])
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semantic_score = normalize_df[SEMANTIC_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in SEMANTIC_LIST ])
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