import requests
import json
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
import streamlit.components.v1 as components
def make_clickable(model_name):
link = "https://huggingface.co/" + model_name
return f'{model_name}'
def get_model_ids():
api = HfApi()
models = api.list_models(filter="llama-leaderboard")
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_metrics_accuracy(meta):
if "model-index" not in meta:
return None
result = meta["model-index"][0]["results"]
metrics = result[0]["metrics"]
accuracy = metrics[0]["value"]
return accuracy
@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
row = {}
row["Model"] = model_id
row["Accuracy"] = parse_metrics_accuracy(meta)
data.append(row)
return pd.DataFrame.from_records(data)
dataframe = get_data()
dataframe = dataframe.fillna("")
st.markdown("# The 🦙 Leaderboard")
st.markdown(
f"This is a leaderboard of **{len(dataframe)}** llama classification models.\n\n"
)
st.markdown(
"This is the most comprehensive leaderboard of llama image classifier models published. You can try out the different models below"
)
st.markdown(
"Please click on the model's name to be redirected to its model card which includes documentation."
)
# turn the model ids into clickable links
dataframe["Model"] = dataframe["Model"].apply(make_clickable)
dataframe = dataframe.sort_values(by=['Accuracy'], ascending=False)
table_html = dataframe.to_html(escape=False)
table_html = table_html.replace("| ", ' | ') # left-align the headers
st.write(table_html, unsafe_allow_html=True)
embed_gradio = components.html(
"""
""",
height=1200,
width=700
) |