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Update app.py
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app.py
CHANGED
@@ -144,7 +144,7 @@ def calculate_selected_score_i2v(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('Model Name (clickable)', 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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@@ -199,7 +199,7 @@ def get_final_score_i2v(df, selected_columns):
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def get_final_score_quality(df, selected_columns):
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normalize_df = get_normalized_df(df)
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for name in normalize_df.drop('Model Name (clickable)', axis=1):
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normalize_df[name] = normalize_df[name]*DIM_WEIGHT[name]
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quality_score = normalize_df[QUALITY_TAB].sum(axis=1) / sum([DIM_WEIGHT[i] for i in QUALITY_TAB])
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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 (clickable)', axis=1).drop('Source', 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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def get_final_score_quality(df, selected_columns):
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normalize_df = get_normalized_df(df)
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for name in normalize_df.drop('Model Name (clickable)', axis=1).drop('Video-Text Camera Motion', axis=1):
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normalize_df[name] = normalize_df[name]*DIM_WEIGHT[name]
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quality_score = normalize_df[QUALITY_TAB].sum(axis=1) / sum([DIM_WEIGHT[i] for i in QUALITY_TAB])
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