Spaces:
Runtime error
Runtime error
File size: 7,174 Bytes
a7de579 e51e8f8 58113ed e51e8f8 85576c8 6e67bdd e51e8f8 a7de579 85576c8 a7de579 85576c8 968af70 a7de579 85576c8 a7de579 85576c8 a7de579 85576c8 a7de579 85576c8 a7de579 85576c8 a7de579 85576c8 a7de579 85576c8 a7de579 85576c8 e51e8f8 a7de579 85576c8 e51e8f8 a7de579 85576c8 e51e8f8 968af70 58113ed a7de579 8410cc1 85576c8 8410cc1 58113ed 70a5579 a7de579 85576c8 70a5579 85576c8 70a5579 cc8cf29 85576c8 cc8cf29 85576c8 cc8cf29 a7de579 968af70 a7de579 85576c8 968af70 a7de579 85576c8 a7de579 6e67bdd a7de579 6e67bdd a7de579 6e67bdd a7de579 6e67bdd 85576c8 a7de579 85576c8 a7de579 6e67bdd 85576c8 6e67bdd a7de579 6e67bdd a7de579 6e67bdd a7de579 6e67bdd a7de579 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
import dataclasses
import datetime
import operator
import pathlib
import pandas as pd
import tqdm.auto
import yaml
from huggingface_hub import HfApi
from constants import (
OWNER_CHOICES,
SLEEP_TIME_INT_TO_STR,
SLEEP_TIME_STR_TO_INT,
WHOAMI,
)
@dataclasses.dataclass(frozen=True)
class DemoInfo:
space_id: str
url: str
title: str
owner: str
sdk: str
sdk_version: str
likes: int
status: str
last_modified: str
sleep_time: int
replicas: int
private: bool
hardware: str
suggested_hardware: str
created: str = ""
arxiv: list[str] = dataclasses.field(default_factory=list)
github: list[str] = dataclasses.field(default_factory=list)
tags: list[str] = dataclasses.field(default_factory=list)
def __post_init__(self):
object.__setattr__(self, "last_modified", DemoInfo.convert_timestamp(self.last_modified))
object.__setattr__(self, "created", DemoInfo.convert_timestamp(self.created))
@staticmethod
def convert_timestamp(timestamp: str) -> str:
try:
return datetime.datetime.strptime(timestamp, "%Y-%m-%dT%H:%M:%S.%fZ").strftime("%Y/%m/%d %H:%M:%S")
except ValueError:
return timestamp
@classmethod
def from_space_id(cls, space_id: str) -> "DemoInfo":
api = HfApi()
space_info = api.space_info(repo_id=space_id)
card = space_info.cardData
runtime = space_info.runtime
resources = runtime["resources"]
return cls(
space_id=space_id,
url=f"https://huggingface.co/spaces/{space_id}",
title=card["title"] if "title" in card else "",
owner=space_id.split("/")[0],
sdk=card["sdk"],
sdk_version=card.get("sdk_version", ""),
likes=space_info.likes,
status=runtime["stage"],
last_modified=space_info.lastModified,
sleep_time=runtime["gcTimeout"] or 0,
replicas=resources["replicas"] if resources is not None else 0,
private=space_info.private,
hardware=runtime["hardware"]["current"] or runtime["hardware"]["requested"],
suggested_hardware=card.get("suggested_hardware", ""),
)
def get_df_from_yaml(path: pathlib.Path | str) -> pd.DataFrame:
with pathlib.Path(path).open() as f:
data = yaml.safe_load(f)
demo_info = []
for space_id in tqdm.auto.tqdm(list(data)):
base_info = DemoInfo.from_space_id(space_id)
info = DemoInfo(**(dataclasses.asdict(base_info) | data[space_id]))
demo_info.append(info)
return pd.DataFrame([dataclasses.asdict(info) for info in demo_info])
class Prettifier:
@staticmethod
def get_arxiv_link(links: list[str]) -> str:
links = [Prettifier.create_link(link.split("/")[-1], link) for link in links]
return "\n".join(links)
@staticmethod
def get_github_link(links: list[str]) -> str:
links = [Prettifier.create_link("github", link) for link in links]
return "\n".join(links)
@staticmethod
def get_tag_list(tags: list[str]) -> str:
return ", ".join(tags)
@staticmethod
def create_link(text: str, url: str) -> str:
return f'<a href={url} target="_blank">{text}</a>'
@staticmethod
def to_div(text: str | None, category_name: str) -> str:
if text is None:
text = ""
class_name = f"{category_name}-{text.lower()}"
return f'<div class="{class_name}">{text}</div>'
@staticmethod
def add_div_tag_to_replicas(replicas: int) -> str:
if replicas == 0:
return ""
if replicas == 1:
return "1"
return f'<div class="multiple-replicas">{replicas}</div>'
@staticmethod
def add_div_tag_to_sleep_time(sleep_time_s: str, hardware: str) -> str:
if hardware == "cpu-basic":
return f'<div class="sleep-time-cpu-basic">{sleep_time_s}</div>'
s = sleep_time_s.replace(" ", "-")
return f'<div class="sleep-time-{s}">{sleep_time_s}</div>'
def __call__(self, df: pd.DataFrame) -> pd.DataFrame:
new_rows = []
for _, row in df.iterrows():
new_row = dict(row) | {
"status": self.to_div(row.status, "status"),
"hardware": self.to_div(row.hardware, "hardware"),
"suggested_hardware": self.to_div(row.suggested_hardware, "hardware"),
"title": self.create_link(row.title, row.url),
"owner": self.create_link(row.owner, f"https://huggingface.co/{row.owner}"),
"sdk": self.to_div(row.sdk, "sdk"),
"sleep_time": self.add_div_tag_to_sleep_time(SLEEP_TIME_INT_TO_STR[row.sleep_time], row.hardware),
"replicas": self.add_div_tag_to_replicas(row.replicas),
"arxiv": self.get_arxiv_link(row.arxiv),
"github": self.get_github_link(row.github),
"tags": self.get_tag_list(row.tags),
}
new_rows.append(new_row)
return pd.DataFrame(new_rows, columns=df.columns)
class DemoList:
COLUMN_INFO = [
["status", "markdown"],
["hardware", "markdown"],
["title", "markdown"],
["owner", "markdown"],
["arxiv", "markdown"],
["github", "markdown"],
["likes", "number"],
["tags", "str"],
["last_modified", "str"],
["created", "str"],
["sdk", "markdown"],
["sdk_version", "str"],
["suggested_hardware", "markdown"],
["sleep_time", "markdown"],
["replicas", "markdown"],
["private", "bool"],
]
def __init__(self, df: pd.DataFrame):
self.df_raw = df
self._prettifier = Prettifier()
self.df_prettified = self._prettifier(df).loc[:, self.column_names]
@property
def column_names(self):
return list(map(operator.itemgetter(0), self.COLUMN_INFO))
@property
def column_datatype(self):
return list(map(operator.itemgetter(1), self.COLUMN_INFO))
def filter(
self,
status: list[str],
hardware: list[str],
sleep_time: list[str],
multiple_replicas: bool,
sdk: list[str],
visibility: list[str],
owner: list[str],
) -> pd.DataFrame:
df = self.df_raw.copy()
if multiple_replicas:
df = df[self.df_raw.replicas > 1]
if visibility == ["public"]:
df = df[~self.df_raw.private]
elif visibility == ["private"]:
df = df[self.df_raw.private]
df = df[
(self.df_raw.status.isin(status)) & (self.df_raw.hardware.isin(hardware)) & (self.df_raw.sdk.isin(sdk))
]
sleep_time_int = [SLEEP_TIME_STR_TO_INT[s] for s in sleep_time]
df = df[self.df_raw.sleep_time.isin(sleep_time_int)]
if set(owner) == set(OWNER_CHOICES):
pass
elif WHOAMI in owner:
df = df[self.df_raw.owner == WHOAMI]
else:
df = df[self.df_raw.owner != WHOAMI]
return self._prettifier(df).loc[:, self.column_names]
|