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| from data.model_handler import ModelHandler | |
| import pandas as pd | |
| def make_clickable_model(model_name:str, link=None): | |
| if link is None: | |
| link = "https://huggingface.co/" + model_name | |
| return f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name.replace("TitanCAProject/", "")}</a>' | |
| def add_rank_and_format(df:pd.DataFrame): | |
| df = df.reset_index() | |
| df = df.rename(columns={"index": "Model"}) | |
| df = ModelHandler.add_rank(df) | |
| df["Model"] = df["Model"].apply(make_clickable_model) | |
| return df | |
| def remove_duplicates(df:pd.DataFrame): | |
| """Remove duplicate models based on their name (after the last '/' if present).""" | |
| df["model_name"] = df["Model"].str.replace("_", "/") | |
| df = df.sort_values("Rank").drop_duplicates(subset=["model_name"], keep="first") | |
| df = df.drop("model_name", axis=1) | |
| return df | |
| def get_refresh_function(model_handler:ModelHandler): | |
| def _refresh(metric): | |
| model_handler.get_titan_data() | |
| data_task_category = model_handler.compute_averages(metric) | |
| df = add_rank_and_format(data_task_category) | |
| return df | |
| return _refresh | |
| def filter_models(data, search_term): | |
| if search_term: | |
| data = data[data["Model"].str.contains(search_term, case=False, na=False)] | |
| return data | |