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import gradio as gr
import pandas as pd
import json
from datasets import load_dataset
import requests
from huggingface_hub import list_datasets, list_models, list_spaces
from collections import Counter
import numpy as np
def compute_ranking(df, column, method="sum", keep="last"):
df_rank = df.groupby("author").aggregate({column: method})[[column]]
df_rank = df_rank.sort_values(by=column)
df_rank.reset_index(drop=True, inplace=True)
df_rank["top_perc"] = df_rank.apply(lambda x: f"{100 * (1-(x.name/len(df_rank))):.2f}", axis=1)
df_rank = df_rank.drop_duplicates(subset=column, keep=keep)
df_rank = df_rank.rename({column: "value"}, axis='columns')
return df_rank
class NpEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
if isinstance(obj, np.floating):
return float(obj)
if isinstance(obj, np.ndarray):
return obj.tolist()
return super(NpEncoder, self).default(obj)
ds = load_dataset("open-source-metrics/model-repos-stats", split="train")
df = ds.to_pandas()
df_ranks = {}
df_ranks["likes"] = compute_ranking(df, "likes")
df_ranks["downloads"] = compute_ranking(df, "downloads_30d")
df_ranks["repos"] = compute_ranking(df, "repo_id", method="count")
with open("./html_template.html", "r") as f:
template = f.read()
def create_user_summary(user_name):
summary = {}
df_user = df.loc[df["author"]==user_name]
if len(df_user) == 0:
return """<br><p style="text-align: center;color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 24px;">Unfortunately there is not enough data for your report.</p><br>"""
r = requests.get(f'https://huggingface.co/api/users/{user_name}/likes')
user_datasets = [dataset for dataset in list_datasets(author=user_name)]
user_spaces = [space for space in list_spaces(author=user_name)]
summary["likes_user_total"] = df_user["likes"].sum()
summary["likes_user_given"] = len(r.json())
summary["likes_user_top"] = df_ranks["likes"][df_ranks["likes"]["value"]>=summary["likes_user_total"]].iloc[0]["top_perc"]
summary["likes_repo_most"] = df_user.sort_values(by="likes", ascending=False).iloc[0]["repo_id"]
summary["likes_repo_most_n"] = df_user.sort_values(by="likes", ascending=False).iloc[0]["likes"]
summary["downloads_user_total"] = df_user["downloads_30d"].sum()
summary["downloads_user_top"] = df_ranks["downloads"][df_ranks["downloads"]["value"]>=summary["downloads_user_total"]].iloc[0]["top_perc"]
summary["downlods_repo_most"] = df_user.sort_values(by="downloads_30d", ascending=False).iloc[0]["repo_id"]
summary["downlods_repo_most_n"] = df_user.sort_values(by="downloads_30d", ascending=False).iloc[0]["downloads_30d"]
summary["repos_model_total"] = len(df_user)
summary["repos_model_top"] = df_ranks["repos"][df_ranks["repos"]["value"]>=summary["repos_model_total"]].iloc[0]["top_perc"]
summary["repos_model_fav_type"] = Counter(df_user["model_type"].dropna()).most_common(1)[0][0]
summary["repos_datasets_total"] = len(user_datasets)
summary["repos_spaces_total"] = len(user_spaces)
summary["repos_spaces_fav_sdk"] = Counter([getattr(info, "sdk", "n/a") for info in user_spaces]).most_common(1)[0][0]
return dict_to_html(summary)
def dict_to_html(summary):
report = template
for key in summary:
report = report.replace("{{" + key + "}}", str(summary[key]))
return report
demo = gr.Blocks(
css=".gradio-container {background-color: #000000}"
)
with demo:
with gr.Row():
gr.HTML(value="""<p style="text-align: center; color: rgb(255, 210, 31); font-family: 'Consolas', monospace; font-size: 24px;"> <b>Enter your HF user name:</b></p>""")
with gr.Row():
username = gr.Textbox(lines=1, max_lines=1, label="User name")
with gr.Row():
run = gr.Button()
with gr.Row():
output = gr.HTML(label="Generated code")
event = run.click(create_user_summary, [username], output)
demo.launch() |