app.py

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


def get_model_ids():
    api = HfApi()
    models = api.list_models(filter="model-index")
    model_ids = [x.modelId for x in models]
    return model_ids


def get_metadata(model_id):
    try:
        readme_path = hf_hub_download(model_id, filename="README.md")
        return metadata_load(readme_path)
    except Exception:
        # 404 README.md not found or problem loading it
        return None


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, 2) if isinstance(value, float) else None
    return value


def parse_metrics_rows(meta):
    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 "dataset" not in result or "metrics" not in result or "type" not in result["dataset"]:
            continue
        dataset = result["dataset"]["type"]
        if "args" not in result["dataset"]:
            continue
        row = {"dataset": dataset}
        for metric in result["metrics"]:
            type = metric["type"].lower().strip()
            value = parse_metric_value(metric.get("value", None))
            if value is None:
                continue
            if type not in row or value < row[type]:
                # overwrite the metric if the new value is lower (e.g. with LM)
                row[type] = value
        yield row

@st.cache(ttl=3600)
def get_data_wrapper():

    def get_data():
        data = []
        model_ids = get_model_ids()
        for model_id in tqdm(model_ids):
            meta = get_metadata(model_id)
            if meta is None:
                continue
            for row in parse_metrics_rows(meta):
                if row is None:
                    continue
                row["model_id"] = model_id
                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()

selectable_datasets = list(set(dataframe.dataset.tolist()))

st.markdown("# 🤗 Leaderboards")

query_params = st.experimental_get_query_params()
default_dataset = "common_voice"
if "dataset" in query_params:
    if len(query_params["dataset"]) > 0 and query_params["dataset"][0] in selectable_datasets:
        default_dataset = query_params["dataset"][0]

dataset = st.sidebar.selectbox(
    "Dataset",
    selectable_datasets,
    index=selectable_datasets.index(default_dataset),
)
st.experimental_set_query_params(**{"dataset": [dataset]})

dataset_df = dataframe[dataframe.dataset == dataset]
dataset_df = dataset_df.dropna(axis="columns", how="all")

selectable_metrics = list(filter(lambda column: column not in ("model_id", "dataset"), dataset_df.columns))
sorting_metric = st.sidebar.radio(
    "Sorting Metric",
    selectable_metrics,
)

dataset_df = dataset_df.filter(["model_id"] + 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).

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/autoevaluate)."
)

# Make the default metric appear right after model names
cols = dataset_df.columns.tolist()
cols.remove(sorting_metric)
cols = cols[:1] + [sorting_metric] + cols[1:]
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[1:], ascending=[metric in ascending_metrics for metric in cols[1:]])
dataset_df = dataset_df.replace(np.nan, '-')

# 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>'}'''),
)
for name in selectable_metrics:
    gb.configure_column(name, type=["numericColumn","numberColumnFilter","customNumericFormat"], precision=2, aggFunc='sum')

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

go = gb.build()
AgGrid(dataset_df, gridOptions=go, allow_unsafe_jscode=True, fit_columns_on_grid_load=True)