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")

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."
    )