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from huggingface_hub import HfApi
import pandas as pd
import os
import streamlit as st
import altair as alt
import numpy as np
import datetime
from transformers.models.auto.configuration_auto import CONFIG_MAPPING_NAMES
from huggingface_hub import Repository
today = datetime.date.today()
year, week, _ = today.isocalendar()
DATASET_REPO_URL = "https://huggingface.co/datasets/patrickvonplaten/model-archs-downloads-space-data"
DATA_FILENAME = f"data_{week}_{year}.csv"
DATA_FILE = os.path.join("data", DATA_FILENAME)
def retrieve_model_stats():
hf_api = HfApi()
all_stats = {}
total_downloads = 0
for model_name in list(CONFIG_MAPPING_NAMES.keys()):
model_stats = {"num_downloads": 0, "%_of_all_downloads": 0, "num_models": 0, "download_per_model": 0}
models = hf_api.list_models(filter=model_name)
model_stats["num_models"] = len(models)
model_stats["num_downloads"] = sum([m.downloads for m in models if hasattr(m, "downloads")])
if len(models) > 0:
model_stats["download_per_model"] = round(model_stats["num_downloads"] / len(models), 2)
total_downloads += model_stats["num_downloads"]
# save in overall dict
all_stats[model_name] = model_stats
for model_name in list(CONFIG_MAPPING_NAMES.keys()):
all_stats[model_name]["%_of_all_downloads"] = round(all_stats[model_name]["num_downloads"] / total_downloads, 5) * 100 # noqa: E501
downloads = all_stats[model_name]["num_downloads"]
all_stats[model_name]["num_downloads"] = f"{downloads:,}"
sorted_results = dict(reversed(sorted(all_stats.items(), key=lambda d: d[1]["%_of_all_downloads"])))
dataframe = pd.DataFrame.from_dict(sorted_results, orient="index")
# give header to model names
result = "model_names" + dataframe.to_csv()
return result
repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL)
if not os.path.isfile(DATA_FILE):
print("Create datafile...")
result = retrieve_model_stats()
if not os.path.isfile(DATA_FILE):
with open(DATA_FILE, "w") as f:
f.write(result)
commit_url = repo.push_to_hub()
print(commit_url)
with open(DATA_FILE, "r") as f:
dataframe = pd.read_csv(DATA_FILE)
int_downloads = np.array([int(x.replace(",", "")) for x in dataframe["num_downloads"].values])
# print top 20 downloads
source = pd.DataFrame({
'Number of total downloads': int_downloads[:20],
'Model architecture name': dataframe["model_names"].values[:20],
})
bar_chart = alt.Chart(source).mark_bar().encode(
y="Number of total downloads",
x=alt.X("Model architecture name", sort=None),
)
st.title(f'Top 20 downloads for year {year} and week {week}')
st.altair_chart(bar_chart, use_container_width=True)
# print bottom 20 downloads
source = pd.DataFrame({
'Number of total downloads': int_downloads[-20:],
'Model architecture name': dataframe["model_names"].values[-20:],
})
bar_chart = alt.Chart(source).mark_bar().encode(
y="Number of total downloads",
x=alt.X("Model architecture name", sort=None),
)
st.title(f'Bottom 20 downloads for year {year} and week {week}')
st.altair_chart(bar_chart, use_container_width=True)
# print all stats
st.title(f'All downloads for year {year} and week {week}')
st.table(dataframe)
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