app.py

import pandas as pd
import streamlit as st
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.repocard import metadata_load
from utils import ascending_metrics, metric_ranges
import numpy as np
from st_aggrid import AgGrid, GridOptionsBuilder, JsCode
from os.path import exists
import threading

st.set_page_config(layout="wide")


def get_model_infos():
    api = HfApi()
    model_infos = api.list_models(filter="model-index", cardData=True)
    return model_infos


def parse_metric_value(value):
    if isinstance(value, str):
        "".join(value.split("%"))
        try:
            value = float(value)
        except:  # noqa: E722
            value = None
    elif isinstance(value, list):
        if len(value) > 0:
            value = value[0]
        else:
            value = None
    value = round(value, 4) if isinstance(value, float) else None
    return value


def parse_metrics_rows(meta, only_verified=False):
    if not isinstance(meta["model-index"], list) or len(meta["model-index"]) == 0 or "results" not in meta["model-index"][0]:
        return None
    for result in meta["model-index"][0]["results"]:
        if not isinstance(result, dict) or "dataset" not in result or "metrics" not in result or "type" not in result["dataset"]:
            continue
        dataset = result["dataset"]["type"]
        if dataset == "":
            continue
        row = {"dataset": dataset, "split": "-unspecified-", "config": "-unspecified-"}
        if "split" in result["dataset"]:
            row["split"] = result["dataset"]["split"]
        if "config" in result["dataset"]:
            row["config"] = result["dataset"]["config"]
        no_results = True
        incorrect_results = False
        for metric in result["metrics"]:
            name = metric["type"].lower().strip()

            if name in ("model_id", "dataset", "split", "config", "pipeline_tag", "only_verified"):
                # Metrics are not allowed to be named "dataset", "split", "config", "pipeline_tag"
                continue
            value = parse_metric_value(metric.get("value", None))
            if value is None:
                continue
            if name in row:
                new_metric_better = value < row[name] if name in ascending_metrics else value > row[name]
            if name not in row or new_metric_better:
                # overwrite the metric if the new value is better.

                if only_verified:
                    if "verified" in metric and metric["verified"]:
                        no_results = False
                        row[name] = value
                        if name in metric_ranges:
                            if value < metric_ranges[name][0] or value > metric_ranges[name][1]:
                                incorrect_results = True
                else:
                    no_results = False
                    row[name] = value
                    if name in metric_ranges:
                        if value < metric_ranges[name][0] or value > metric_ranges[name][1]:
                            incorrect_results = True
        if no_results or incorrect_results:
            continue
        yield row

@st.cache(ttl=0)
def get_data_wrapper():

    def get_data(dataframe=None, verified_dataframe=None):
        data = []
        verified_data = []
        print("getting model infos")
        model_infos = get_model_infos()
        print("got model infos")
        for model_info in model_infos:
            meta = model_info.cardData
            if meta is None:
                continue
            for row in parse_metrics_rows(meta):
                if row is None:
                    continue
                row["model_id"] = model_info.id
                row["pipeline_tag"] = model_info.pipeline_tag
                row["only_verified"] = False
                data.append(row)
            for row in parse_metrics_rows(meta, only_verified=True):
                if row is None:
                    continue
                row["model_id"] = model_info.id
                row["pipeline_tag"] = model_info.pipeline_tag
                row["only_verified"] = True
                data.append(row)
        dataframe = pd.DataFrame.from_records(data)
        dataframe.to_pickle("cache.pkl")

    if exists("cache.pkl"):
        # If we have saved the results previously, call an asynchronous process
        # to fetch the results and update the saved file. Don't make users wait
        # while we fetch the new results. Instead, display the old results for
        # now. The new results should be loaded when this method
        # is called again.
        dataframe = pd.read_pickle("cache.pkl")
        t = threading.Thread(name="get_data procs", target=get_data)
        t.start()
    else:
        # We have to make the users wait during the first startup of this app.
        get_data()
        dataframe = pd.read_pickle("cache.pkl")

    return dataframe

# dataframe = get_data_wrapper()

st.markdown("# 🤗 Leaderboards")
st.warning(
    "**⚠️ This project has been archived. If you want to evaluate LLMs, checkout [this collection](https://huggingface.co/collections/clefourrier/llm-leaderboards-and-benchmarks-✨-64f99d2e11e92ca5568a7cce) of leaderboards.**"
)

# query_params = st.experimental_get_query_params()
# if "first_query_params" not in st.session_state:
#     st.session_state.first_query_params = query_params
# first_query_params = st.session_state.first_query_params

# default_task = first_query_params.get("task", [None])[0]
# default_only_verified = bool(int(first_query_params.get("only_verified", [0])[0]))
# print(default_only_verified)
# default_dataset = first_query_params.get("dataset", [None])[0]
# default_split = first_query_params.get("split", [None])[0]
# default_config = first_query_params.get("config", [None])[0]
# default_metric = first_query_params.get("metric", [None])[0]

# only_verified_results = st.sidebar.checkbox(
#     "Filter for Verified Results",
#     value=default_only_verified,
#     help="Select this checkbox if you want to see only results produced by the Hugging Face model evaluator, and no self-reported results."
# )

# selectable_tasks = list(set(dataframe.pipeline_tag))
# if None in selectable_tasks:
#     selectable_tasks.remove(None)
# selectable_tasks.sort(key=lambda name: name.lower())
# selectable_tasks = ["-any-"] + selectable_tasks

# task = st.sidebar.selectbox(
#     "Task",
#     selectable_tasks,
#     index=(selectable_tasks).index(default_task) if default_task in selectable_tasks else 0,
#     help="Filter the selectable datasets by task. Leave as \"-any-\" to see all selectable datasets."
# )

# if task != "-any-":
#     dataframe = dataframe[dataframe.pipeline_tag == task]

# selectable_datasets = ["-any-"] + sorted(list(set(dataframe.dataset.tolist())), key=lambda name: name.lower())
# if "" in selectable_datasets:
#     selectable_datasets.remove("")

# dataset = st.sidebar.selectbox(
#     "Dataset",
#     selectable_datasets,
#     index=selectable_datasets.index(default_dataset) if default_dataset in selectable_datasets else 0,
#     help="Select a dataset to see the leaderboard!"
# )

# dataframe = dataframe[dataframe.only_verified == only_verified_results]

# current_query_params = {"dataset": [dataset], "only_verified": [int(only_verified_results)], "task": [task]}

# st.experimental_set_query_params(**current_query_params)

# if dataset != "-any-":
#     dataset_df = dataframe[dataframe.dataset == dataset]
# else:
#     dataset_df = dataframe

# dataset_df = dataset_df.dropna(axis="columns", how="all")

# if len(dataset_df) > 0:
#     selectable_configs = list(set(dataset_df["config"]))
#     selectable_configs.sort(key=lambda name: name.lower())

#     if "-unspecified-" in selectable_configs:
#         selectable_configs.remove("-unspecified-")
#         selectable_configs = ["-unspecified-"] + selectable_configs

#     if dataset != "-any-":
#         config = st.sidebar.selectbox(
#             "Config",
#             selectable_configs,
#             index=selectable_configs.index(default_config) if default_config in selectable_configs else 0,
#             help="Filter the results on the current leaderboard by the dataset config. Self-reported results might not report the config, which is why \"-unspecified-\" is an option."
#         )
#         dataset_df = dataset_df[dataset_df.config == config]

#         selectable_splits = list(set(dataset_df["split"]))
#         selectable_splits.sort(key=lambda name: name.lower())

#         if "-unspecified-" in selectable_splits:
#             selectable_splits.remove("-unspecified-")
#             selectable_splits = ["-unspecified-"] + selectable_splits

#         split = st.sidebar.selectbox(
#             "Split",
#             selectable_splits,
#             index=selectable_splits.index(default_split) if default_split in selectable_splits else 0,
#             help="Filter the results on the current leaderboard by the dataset split. Self-reported results might not report the split, which is why \"-unspecified-\" is an option."
#         )

#         current_query_params.update({"config": [config], "split": [split]})

#         st.experimental_set_query_params(**current_query_params)

#         dataset_df = dataset_df[dataset_df.split == split]

#     not_selectable_metrics = ["model_id", "dataset", "split", "config", "pipeline_tag", "only_verified"]
#     selectable_metrics = list(filter(lambda column: column not in not_selectable_metrics, dataset_df.columns))

#     dataset_df = dataset_df.filter(["model_id"] + (["dataset"] if dataset == "-any-" else []) + selectable_metrics)
#     dataset_df = dataset_df.dropna(thresh=2)  # Want at least two non-na values (one for model_id and one for a metric).

#     sorting_metric = st.sidebar.radio(
#         "Sorting Metric",
#         selectable_metrics,
#         index=selectable_metrics.index(default_metric) if default_metric in selectable_metrics else 0,
#         help="Select the metric to sort the leaderboard by. Click on the metric name in the leaderboard to reverse the sorting order."
#     )

#     current_query_params.update({"metric": [sorting_metric]})

#     st.experimental_set_query_params(**current_query_params)

#     st.markdown(
#         "Please click on the model's name to be redirected to its model card."
#     )

#     st.markdown(
#         "Want to beat the leaderboard? Don't see your model here? Simply request an automatic evaluation [here](https://huggingface.co/spaces/autoevaluate/model-evaluator)."
#     )

#     st.markdown(
#         "If you do not see your self-reported results here, ensure that your results are in the expected range for all metrics. E.g., accuracy is 0-1, not 0-100."
#     )

#     if dataset == "-any-":
#         st.info(
#             "Note: you haven't chosen a dataset, so the leaderboard is showing the best scoring model for a random sample of the datasets available."
#         )

#     # Make the default metric appear right after model names and dataset names
#     cols = dataset_df.columns.tolist()
#     cols.remove(sorting_metric)
#     sorting_metric_index = 1 if dataset != "-any-" else 2
#     cols = cols[:sorting_metric_index] + [sorting_metric] + cols[sorting_metric_index:]
#     dataset_df = dataset_df[cols]

#     # Sort the leaderboard, giving the sorting metric highest priority and then ordering by other metrics in the case of equal values.
#     dataset_df = dataset_df.sort_values(by=cols[sorting_metric_index:], ascending=[metric in ascending_metrics for metric in cols[sorting_metric_index:]])
#     dataset_df = dataset_df.replace(np.nan, '-')

#     # If dataset is "-any-", only show the best model for a random sample of 100 datasets.
#     # Otherwise The leaderboard is way too long and doesn't give the users a feel for all of
#     # the datasets available for a task.
#     if dataset == "-any-":
#         filtered_dataset_df_dict = {column: [] for column in dataset_df.columns}
#         seen_datasets = set()
#         for _, row in dataset_df.iterrows():
#             if row["dataset"] not in seen_datasets:
#                 for column in dataset_df.columns:
#                     filtered_dataset_df_dict[column].append(row[column])
#                 seen_datasets.add(row["dataset"])
#         dataset_df = pd.DataFrame(filtered_dataset_df_dict)
#         dataset_df = dataset_df.sample(min(100, len(dataset_df)))

#     # Make the leaderboard
#     gb = GridOptionsBuilder.from_dataframe(dataset_df)
#     gb.configure_default_column(sortable=False)
#     gb.configure_column(
#         "model_id",
#         cellRenderer=JsCode('''function(params) {return '<a target="_blank" href="https://huggingface.co/'+params.value+'">'+params.value+'</a>'}'''),
#     )
#     if dataset == "-any-":
#         gb.configure_column(
#             "dataset",
#             cellRenderer=JsCode('''function(params) {return '<a target="_blank" href="https://huggingface.co/spaces/autoevaluate/leaderboards?dataset='+params.value+'">'+params.value+'</a>'}'''),
#         )
#     for name in selectable_metrics:
#         gb.configure_column(name, type=["numericColumn","numberColumnFilter","customNumericFormat"], precision=4, aggFunc='sum')

#     gb.configure_column(
#         sorting_metric,
#         sortable=True,
#         cellStyle=JsCode('''function(params) { return {'backgroundColor': '#FFD21E'}}''')
#     )

#     go = gb.build()
#     fit_columns = len(dataset_df.columns) < 10
#     AgGrid(dataset_df, gridOptions=go, height=28*len(dataset_df) + (35 if fit_columns else 41), allow_unsafe_jscode=True, fit_columns_on_grid_load=fit_columns, enable_enterprise_modules=False)

# else:
#     st.markdown(
#         "No " + ("verified" if only_verified_results else "unverified") + " results to display. Try toggling the verified results filter."
#     )