import requests
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

cer_langs = ["ja", "zh-CN", "zh-HK", "zh-TW"]


def make_clickable(model_name):
    link = "https://huggingface.co/" + model_name
    return f'<a target="_blank" href="{link}">{model_name}</a>'


def get_model_ids():
    api = HfApi()
    models = api.list_models(filter="robust-speech-event")
    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 requests.exceptions.HTTPError:
        # 404 README.md not found
        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, float) and value < 1.1:
        # assuming that WER is given in 0.xx format
        value = 100 * value
    elif isinstance(value, list):
        if len(value) > 0:
            value = value[0]
        else:
            value = None
    value = round(value, 2) if value is not None else None
    return value


def parse_metrics_rows(meta):
    if "model-index" not in meta or "language" not in meta:
        return None
    lang = meta["language"]
    lang = lang[0] if isinstance(lang, list) else lang
    for result in meta["model-index"][0]["results"]:
        if "dataset" not in result or "metrics" not in result:
            continue
        dataset = result["dataset"]["type"]
        if "args" not in result["dataset"]:
            continue
        dataset_config = result["dataset"]["args"]
        row = {"dataset": dataset, "lang": lang}
        for metric in result["metrics"]:
            type = metric["type"].lower().strip()
            if type not in ["wer", "cer"]:
                continue
            value = parse_metric_value(metric["value"])
            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
        if "wer" in row or "cer" in row:
            yield row


@st.cache(ttl=600)
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)
    return pd.DataFrame.from_records(data)


dataframe = get_data()
dataframe = dataframe.fillna("")
dataframe["model_id"] = dataframe["model_id"].apply(make_clickable)

_, col_center = st.columns([3, 6])
with col_center:
    st.image("logo.png", width=200)
st.markdown("# Speech Models Leaderboard")

lang = st.selectbox(
    "Language",
    sorted(dataframe["lang"].unique()),
    index=0,
)
lang_df = dataframe[dataframe.lang == lang]

dataset = st.selectbox(
    "Dataset",
    sorted(lang_df["dataset"].unique()),
    index=0,
)
dataset_df = lang_df[lang_df.dataset == dataset]
if lang in cer_langs:
    dataset_df = dataset_df[["model_id", "cer"]]
    dataset_df.sort_values("cer", inplace=True)
else:
    dataset_df = dataset_df[["model_id", "wer"]]
    dataset_df.sort_values("wer", inplace=True)
dataset_df.rename(
    columns={
        "model_id": "Model",
        "wer": "WER (lower is better)",
        "cer": "CER (lower is better)",
    },
    inplace=True,
)

st.write(dataset_df.to_html(escape=False, index=None), unsafe_allow_html=True)