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from data.model_handler import ModelHandler
def make_clickable_model(model_name, link=None):
if link is None:
desanitized_model_name = model_name.replace("_", "/")
desanitized_model_name = desanitized_model_name.replace("-thisisapoint-", ".")
if "/captioning" in desanitized_model_name:
desanitized_model_name = desanitized_model_name.replace("/captioning", "")
if "/ocr" in desanitized_model_name:
desanitized_model_name = desanitized_model_name.replace("/ocr", "")
link = "https://huggingface.co/" + desanitized_model_name
return f'<a target="_blank" style="text-decoration: underline" href="{link}">{desanitized_model_name}</a>'
def add_rank_and_format(df, benchmark_version=1):
df = df.reset_index()
df = df.rename(columns={"index": "Model"})
df = ModelHandler.add_rank(df, benchmark_version)
df["Model"] = df["Model"].apply(make_clickable_model)
# df = remove_duplicates(df)
return df
def remove_duplicates(df):
"""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, benchmark_version):
def _refresh(metric):
model_handler.get_vidore_data(metric)
data_task_category = model_handler.compute_averages(metric, benchmark_version)
df = add_rank_and_format(data_task_category, benchmark_version)
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
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