Spaces:
Build error
Build error
| 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]) | |
| st.title(f"Transformers stats for year {year} and week {week}") | |
| # 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("Top 20 downloads last 30 days") | |
| 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("Bottom 20 downloads last 30 days") | |
| st.altair_chart(bar_chart, use_container_width=True) | |
| # print all stats | |
| st.title("All stats last 30 days") | |
| st.table(dataframe) | |