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Update app.py
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app.py
CHANGED
@@ -38,6 +38,7 @@ def add_new_eval(
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submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
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submission_repo.git_pull()
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filename = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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now = datetime.datetime.now()
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with open(f'{SUBMISSION_NAME}/{filename}.zip','wb') as f:
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f.write(input_file)
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@@ -103,7 +104,10 @@ def add_new_eval(
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new_data.append("User Upload")
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else:
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new_data.append(team_name)
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-
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csv_data.loc[col] = new_data
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csv_data = csv_data.to_csv(CSV_DIR, index=False)
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submission_repo.push_to_hub()
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@@ -114,7 +118,7 @@ def get_normalized_df(df):
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# final_score = df.drop('name', axis=1).sum(axis=1)
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# df.insert(1, 'Overall Score', final_score)
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normalize_df = df.copy().fillna(0.0)
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for column in normalize_df.columns[1:-
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min_val = NORMALIZE_DIC[column]['Min']
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max_val = NORMALIZE_DIC[column]['Max']
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normalize_df[column] = (normalize_df[column] - min_val) / (max_val - min_val)
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@@ -166,7 +170,7 @@ 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).drop('Source', axis=1).drop('Mail', 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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@@ -246,6 +250,7 @@ def get_baseline_df():
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df = get_final_score(df, checkbox_group.value)
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df = df.sort_values(by="Selected Score", ascending=False)
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present_columns = MODEL_INFO + checkbox_group.value
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df = df[present_columns]
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df = convert_scores_to_percentage(df)
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return df
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@@ -320,7 +325,7 @@ def convert_scores_to_percentage(df):
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# 对DataFrame中的每一列(除了'name'列)进行操作
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if 'Source' in df.columns:
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skip_col =
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else:
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skip_col =1
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for column in df.columns[skip_col:]: # 假设第一列是'name'
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@@ -649,4 +654,4 @@ with block:
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data_run.click(on_filter_model_size_method_change, inputs=[checkbox_group], outputs=data_component)
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block.launch()
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submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
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submission_repo.git_pull()
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filename = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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update_time = now.strftime("%Y-%m-%d") # Capture update time
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now = datetime.datetime.now()
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with open(f'{SUBMISSION_NAME}/{filename}.zip','wb') as f:
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f.write(input_file)
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new_data.append("User Upload")
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else:
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new_data.append(team_name)
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new_data.append(contact_email) # Add contact email [private]
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new_data.append(update_time) # Add the update time
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csv_data.loc[col] = new_data
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csv_data = csv_data.to_csv(CSV_DIR, index=False)
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submission_repo.push_to_hub()
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# final_score = df.drop('name', axis=1).sum(axis=1)
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# df.insert(1, 'Overall Score', final_score)
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normalize_df = df.copy().fillna(0.0)
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for column in normalize_df.columns[1:-3]:
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min_val = NORMALIZE_DIC[column]['Min']
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max_val = NORMALIZE_DIC[column]['Max']
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normalize_df[column] = (normalize_df[column] - min_val) / (max_val - min_val)
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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).drop('Mail', axis=1).drop('Date',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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df = get_final_score(df, checkbox_group.value)
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df = df.sort_values(by="Selected Score", ascending=False)
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present_columns = MODEL_INFO + checkbox_group.value
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print(present_columns)
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df = df[present_columns]
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df = convert_scores_to_percentage(df)
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return df
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# 对DataFrame中的每一列(除了'name'列)进行操作
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if 'Source' in df.columns:
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skip_col =3
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else:
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skip_col =1
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for column in df.columns[skip_col:]: # 假设第一列是'name'
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data_run.click(on_filter_model_size_method_change, inputs=[checkbox_group], outputs=data_component)
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block.launch(server_name="0.0.0.0").queue()
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