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| import pandas as pd | |
| from pathlib import Path | |
| from ..styles import highlight_color | |
| abs_path = Path(__file__).parent.parent.parent | |
| def replace_models_names(model_name): | |
| if "gpt" in model_name: | |
| return model_name | |
| replaces = {'meta-llama': 'meta_llama', | |
| 'epfl-llm':'epfl_llm', | |
| '01-ai':'01_ai'} | |
| new_name = model_name.replace('model-', '') | |
| for k, v in replaces.items(): | |
| if new_name.startswith(k): | |
| new_name = new_name.replace(k, v) | |
| new_name = new_name.replace('-','/',1) | |
| new_name = new_name.replace('_','-',1) | |
| new_name = f"[{new_name}](https://huggingface.co/{new_name})" | |
| return new_name | |
| def generate_order_list_and_data_types(json_data): | |
| order_list = ["model_name", "overall_accuracy"] | |
| data_types = ["markdown", "number"] | |
| for key in json_data.keys(): | |
| if key not in ["model_name", "overall_accuracy"]: | |
| order_list.append(key) | |
| data_types.append("number") | |
| order_list[2:] = sorted(order_list[2:]) | |
| return order_list, data_types | |
| def filter_data(selected_columns, search_query): | |
| df = PES_ACCS[selected_columns] | |
| if search_query: | |
| df = df[df['model_name'].str.contains(search_query, case=False, na=False)] | |
| return df | |
| def filter_columns(column_choices): | |
| selected_columns = [col for col in ORDER_LIST if col in column_choices] | |
| filtered_df = PES_ACCS[selected_columns] | |
| return filtered_df.style.highlight_max(color=highlight_color, subset=filtered_df.columns[1:]).format(precision=2) | |
| def load_json_data(file_path, order_list): | |
| PES_ACCS = pd.read_json(file_path) | |
| for column in PES_ACCS.columns: | |
| if PES_ACCS[column].apply(type).eq(dict).any(): | |
| PES_ACCS[column] = PES_ACCS[column].apply(str) | |
| PES_ACCS["model_name"] = PES_ACCS["model_name"].apply( | |
| lambda name: replace_models_names(name) | |
| ) | |
| for column in PES_ACCS.select_dtypes(include='number').columns: | |
| PES_ACCS[column] = PES_ACCS[column].round(2) | |
| ordered_columns = [col for col in order_list if col in PES_ACCS.columns] | |
| PES_ACCS = PES_ACCS[ordered_columns] | |
| if "overall_accuracy" in PES_ACCS.columns: | |
| PES_ACCS = PES_ACCS.sort_values(by="overall_accuracy", ascending=False) | |
| return PES_ACCS | |
| # file_path = str(abs_path / "leaderboards/pes_accuracy.json") | |
| file_path = str(abs_path / "leaderboards/pes_accs.json") | |
| with open(file_path, 'r', encoding='utf-8') as file: | |
| sample_data = pd.read_json(file_path).iloc[0].to_dict() # Load the first row as a dict | |
| ORDER_LIST, DATA_TYPES = generate_order_list_and_data_types(sample_data) | |
| PES_ACCS = load_json_data(file_path, ORDER_LIST) | |
| STYLED_PES_ACCS = PES_ACCS.style.highlight_max( | |
| color = highlight_color, | |
| subset=PES_ACCS.columns[1:]).format(precision=2) | |
| COLUMN_HEADERS = ORDER_LIST | |
| print('test') | |