mirror of
https://github.com/gradio-app/gradio.git
synced 2025-02-11 11:19:58 +08:00
588 lines
22 KiB
Python
588 lines
22 KiB
Python
from __future__ import annotations
|
|
|
|
import csv
|
|
import datetime
|
|
import io
|
|
import json
|
|
import os
|
|
import uuid
|
|
from abc import ABC, abstractmethod
|
|
from distutils.version import StrictVersion
|
|
from pathlib import Path
|
|
from typing import TYPE_CHECKING, Any, List
|
|
|
|
import pkg_resources
|
|
|
|
import gradio as gr
|
|
from gradio import encryptor, utils
|
|
from gradio.documentation import document, set_documentation_group
|
|
|
|
if TYPE_CHECKING:
|
|
from gradio.components import IOComponent
|
|
|
|
set_documentation_group("flagging")
|
|
|
|
|
|
def _get_dataset_features_info(is_new, components):
|
|
"""
|
|
Takes in a list of components and returns a dataset features info
|
|
|
|
Parameters:
|
|
is_new: boolean, whether the dataset is new or not
|
|
components: list of components
|
|
|
|
Returns:
|
|
infos: a dictionary of the dataset features
|
|
file_preview_types: dictionary mapping of gradio components to appropriate string.
|
|
header: list of header strings
|
|
|
|
"""
|
|
infos = {"flagged": {"features": {}}}
|
|
# File previews for certain input and output types
|
|
file_preview_types = {gr.Audio: "Audio", gr.Image: "Image"}
|
|
headers = []
|
|
|
|
# Generate the headers and dataset_infos
|
|
if is_new:
|
|
|
|
for component in components:
|
|
headers.append(component.label)
|
|
infos["flagged"]["features"][component.label] = {
|
|
"dtype": "string",
|
|
"_type": "Value",
|
|
}
|
|
if isinstance(component, tuple(file_preview_types)):
|
|
headers.append(component.label + " file")
|
|
for _component, _type in file_preview_types.items():
|
|
if isinstance(component, _component):
|
|
infos["flagged"]["features"][
|
|
(component.label or "") + " file"
|
|
] = {"_type": _type}
|
|
break
|
|
|
|
headers.append("flag")
|
|
infos["flagged"]["features"]["flag"] = {
|
|
"dtype": "string",
|
|
"_type": "Value",
|
|
}
|
|
|
|
return infos, file_preview_types, headers
|
|
|
|
|
|
class FlaggingCallback(ABC):
|
|
"""
|
|
An abstract class for defining the methods that any FlaggingCallback should have.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def setup(self, components: List[IOComponent], flagging_dir: str):
|
|
"""
|
|
This method should be overridden and ensure that everything is set up correctly for flag().
|
|
This method gets called once at the beginning of the Interface.launch() method.
|
|
Parameters:
|
|
components: Set of components that will provide flagged data.
|
|
flagging_dir: A string, typically containing the path to the directory where the flagging file should be storied (provided as an argument to Interface.__init__()).
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def flag(
|
|
self,
|
|
flag_data: List[Any],
|
|
flag_option: str | None = None,
|
|
flag_index: int | None = None,
|
|
username: str | None = None,
|
|
) -> int:
|
|
"""
|
|
This method should be overridden by the FlaggingCallback subclass and may contain optional additional arguments.
|
|
This gets called every time the <flag> button is pressed.
|
|
Parameters:
|
|
interface: The Interface object that is being used to launch the flagging interface.
|
|
flag_data: The data to be flagged.
|
|
flag_option (optional): In the case that flagging_options are provided, the flag option that is being used.
|
|
flag_index (optional): The index of the sample that is being flagged.
|
|
username (optional): The username of the user that is flagging the data, if logged in.
|
|
Returns:
|
|
(int) The total number of samples that have been flagged.
|
|
"""
|
|
pass
|
|
|
|
|
|
@document()
|
|
class SimpleCSVLogger(FlaggingCallback):
|
|
"""
|
|
A simplified implementation of the FlaggingCallback abstract class
|
|
provided for illustrative purposes. Each flagged sample (both the input and output data)
|
|
is logged to a CSV file on the machine running the gradio app.
|
|
Example:
|
|
import gradio as gr
|
|
def image_classifier(inp):
|
|
return {'cat': 0.3, 'dog': 0.7}
|
|
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
|
|
flagging_callback=SimpleCSVLogger())
|
|
"""
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
def setup(self, components: List[IOComponent], flagging_dir: str | Path):
|
|
self.components = components
|
|
self.flagging_dir = flagging_dir
|
|
os.makedirs(flagging_dir, exist_ok=True)
|
|
|
|
def flag(
|
|
self,
|
|
flag_data: List[Any],
|
|
flag_option: str | None = None,
|
|
flag_index: int | None = None,
|
|
username: str | None = None,
|
|
) -> int:
|
|
flagging_dir = self.flagging_dir
|
|
log_filepath = Path(flagging_dir) / "log.csv"
|
|
|
|
csv_data = []
|
|
for component, sample in zip(self.components, flag_data):
|
|
save_dir = Path(flagging_dir) / utils.strip_invalid_filename_characters(
|
|
component.label or ""
|
|
)
|
|
csv_data.append(
|
|
component.deserialize(
|
|
sample,
|
|
save_dir,
|
|
None,
|
|
)
|
|
)
|
|
|
|
with open(log_filepath, "a", newline="") as csvfile:
|
|
writer = csv.writer(csvfile)
|
|
writer.writerow(utils.sanitize_list_for_csv(csv_data))
|
|
|
|
with open(log_filepath, "r") as csvfile:
|
|
line_count = len([None for row in csv.reader(csvfile)]) - 1
|
|
return line_count
|
|
|
|
|
|
@document()
|
|
class CSVLogger(FlaggingCallback):
|
|
"""
|
|
The default implementation of the FlaggingCallback abstract class. Each flagged
|
|
sample (both the input and output data) is logged to a CSV file with headers on the machine running the gradio app.
|
|
Example:
|
|
import gradio as gr
|
|
def image_classifier(inp):
|
|
return {'cat': 0.3, 'dog': 0.7}
|
|
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
|
|
flagging_callback=CSVLogger())
|
|
Guides: using_flagging
|
|
"""
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
def setup(
|
|
self,
|
|
components: List[IOComponent],
|
|
flagging_dir: str | Path,
|
|
encryption_key: bytes | None = None,
|
|
):
|
|
self.components = components
|
|
self.flagging_dir = flagging_dir
|
|
self.encryption_key = encryption_key
|
|
os.makedirs(flagging_dir, exist_ok=True)
|
|
|
|
def flag(
|
|
self,
|
|
flag_data: List[Any],
|
|
flag_option: str | None = None,
|
|
flag_index: int | None = None,
|
|
username: str | None = None,
|
|
) -> int:
|
|
flagging_dir = self.flagging_dir
|
|
log_filepath = Path(flagging_dir) / "log.csv"
|
|
is_new = not Path(log_filepath).exists()
|
|
headers = [
|
|
getattr(component, "label", None) or f"component {idx}"
|
|
for idx, component in enumerate(self.components)
|
|
] + [
|
|
"flag",
|
|
"username",
|
|
"timestamp",
|
|
]
|
|
|
|
csv_data = []
|
|
for idx, (component, sample) in enumerate(zip(self.components, flag_data)):
|
|
save_dir = Path(flagging_dir) / utils.strip_invalid_filename_characters(
|
|
getattr(component, "label", None) or f"component {idx}"
|
|
)
|
|
if utils.is_update(sample):
|
|
csv_data.append(str(sample))
|
|
else:
|
|
csv_data.append(
|
|
component.deserialize(
|
|
sample,
|
|
save_dir=save_dir,
|
|
encryption_key=self.encryption_key,
|
|
)
|
|
if sample is not None
|
|
else ""
|
|
)
|
|
csv_data.append(flag_option if flag_option is not None else "")
|
|
csv_data.append(username if username is not None else "")
|
|
csv_data.append(str(datetime.datetime.now()))
|
|
|
|
def replace_flag_at_index(file_content: str, flag_index: int):
|
|
file_content_ = io.StringIO(file_content)
|
|
content = list(csv.reader(file_content_))
|
|
header = content[0]
|
|
flag_col_index = header.index("flag")
|
|
content[flag_index][flag_col_index] = flag_option # type: ignore
|
|
output = io.StringIO()
|
|
writer = csv.writer(output)
|
|
writer.writerows(utils.sanitize_list_for_csv(content))
|
|
return output.getvalue()
|
|
|
|
if self.encryption_key:
|
|
output = io.StringIO()
|
|
if not is_new:
|
|
with open(log_filepath, "rb", encoding="utf-8") as csvfile:
|
|
encrypted_csv = csvfile.read()
|
|
decrypted_csv = encryptor.decrypt(
|
|
self.encryption_key, encrypted_csv
|
|
)
|
|
file_content = decrypted_csv.decode()
|
|
if flag_index is not None:
|
|
file_content = replace_flag_at_index(file_content, flag_index)
|
|
output.write(file_content)
|
|
writer = csv.writer(output)
|
|
if flag_index is None:
|
|
if is_new:
|
|
writer.writerow(utils.sanitize_list_for_csv(headers))
|
|
writer.writerow(utils.sanitize_list_for_csv(csv_data))
|
|
with open(log_filepath, "wb", encoding="utf-8") as csvfile:
|
|
csvfile.write(
|
|
encryptor.encrypt(self.encryption_key, output.getvalue().encode())
|
|
)
|
|
else:
|
|
if flag_index is None:
|
|
with open(log_filepath, "a", newline="", encoding="utf-8") as csvfile:
|
|
writer = csv.writer(csvfile)
|
|
if is_new:
|
|
writer.writerow(utils.sanitize_list_for_csv(headers))
|
|
writer.writerow(utils.sanitize_list_for_csv(csv_data))
|
|
else:
|
|
with open(log_filepath, encoding="utf-8") as csvfile:
|
|
file_content = csvfile.read()
|
|
file_content = replace_flag_at_index(file_content, flag_index)
|
|
with open(
|
|
log_filepath, "w", newline="", encoding="utf-8"
|
|
) as csvfile: # newline parameter needed for Windows
|
|
csvfile.write(file_content)
|
|
with open(log_filepath, "r", encoding="utf-8") as csvfile:
|
|
line_count = len([None for row in csv.reader(csvfile)]) - 1
|
|
return line_count
|
|
|
|
|
|
@document()
|
|
class HuggingFaceDatasetSaver(FlaggingCallback):
|
|
"""
|
|
A callback that saves each flagged sample (both the input and output data)
|
|
to a HuggingFace dataset.
|
|
Example:
|
|
import gradio as gr
|
|
hf_writer = gr.HuggingFaceDatasetSaver(HF_API_TOKEN, "image-classification-mistakes")
|
|
def image_classifier(inp):
|
|
return {'cat': 0.3, 'dog': 0.7}
|
|
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
|
|
allow_flagging="manual", flagging_callback=hf_writer)
|
|
Guides: using_flagging
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hf_token: str,
|
|
dataset_name: str,
|
|
organization: str | None = None,
|
|
private: bool = False,
|
|
):
|
|
"""
|
|
Parameters:
|
|
hf_token: The HuggingFace token to use to create (and write the flagged sample to) the HuggingFace dataset.
|
|
dataset_name: The name of the dataset to save the data to, e.g. "image-classifier-1"
|
|
organization: The organization to save the dataset under. The hf_token must provide write access to this organization. If not provided, saved under the name of the user corresponding to the hf_token.
|
|
private: Whether the dataset should be private (defaults to False).
|
|
"""
|
|
self.hf_token = hf_token
|
|
self.dataset_name = dataset_name
|
|
self.organization_name = organization
|
|
self.dataset_private = private
|
|
|
|
def setup(self, components: List[IOComponent], flagging_dir: str):
|
|
"""
|
|
Params:
|
|
flagging_dir (str): local directory where the dataset is cloned,
|
|
updated, and pushed from.
|
|
"""
|
|
try:
|
|
import huggingface_hub
|
|
except (ImportError, ModuleNotFoundError):
|
|
raise ImportError(
|
|
"Package `huggingface_hub` not found is needed "
|
|
"for HuggingFaceDatasetSaver. Try 'pip install huggingface_hub'."
|
|
)
|
|
hh_version = pkg_resources.get_distribution("huggingface_hub").version
|
|
try:
|
|
if StrictVersion(hh_version) < StrictVersion("0.6.0"):
|
|
raise ImportError(
|
|
"The `huggingface_hub` package must be version 0.6.0 or higher"
|
|
"for HuggingFaceDatasetSaver. Try 'pip install huggingface_hub --upgrade'."
|
|
)
|
|
except ValueError:
|
|
pass
|
|
repo_id = huggingface_hub.get_full_repo_name(
|
|
self.dataset_name, token=self.hf_token
|
|
)
|
|
path_to_dataset_repo = huggingface_hub.create_repo(
|
|
repo_id=repo_id,
|
|
token=self.hf_token,
|
|
private=self.dataset_private,
|
|
repo_type="dataset",
|
|
exist_ok=True,
|
|
)
|
|
self.path_to_dataset_repo = path_to_dataset_repo # e.g. "https://huggingface.co/datasets/abidlabs/test-audio-10"
|
|
self.components = components
|
|
self.flagging_dir = flagging_dir
|
|
self.dataset_dir = Path(flagging_dir) / self.dataset_name
|
|
self.repo = huggingface_hub.Repository(
|
|
local_dir=str(self.dataset_dir),
|
|
clone_from=path_to_dataset_repo,
|
|
use_auth_token=self.hf_token,
|
|
)
|
|
self.repo.git_pull(lfs=True)
|
|
|
|
# Should filename be user-specified?
|
|
self.log_file = Path(self.dataset_dir) / "data.csv"
|
|
self.infos_file = Path(self.dataset_dir) / "dataset_infos.json"
|
|
|
|
def flag(
|
|
self,
|
|
flag_data: List[Any],
|
|
flag_option: str | None = None,
|
|
flag_index: int | None = None,
|
|
username: str | None = None,
|
|
) -> int:
|
|
self.repo.git_pull(lfs=True)
|
|
|
|
is_new = not Path(self.log_file).exists()
|
|
|
|
with open(self.log_file, "a", newline="", encoding="utf-8") as csvfile:
|
|
writer = csv.writer(csvfile)
|
|
|
|
# File previews for certain input and output types
|
|
infos, file_preview_types, headers = _get_dataset_features_info(
|
|
is_new, self.components
|
|
)
|
|
|
|
# Generate the headers and dataset_infos
|
|
if is_new:
|
|
writer.writerow(utils.sanitize_list_for_csv(headers))
|
|
|
|
# Generate the row corresponding to the flagged sample
|
|
csv_data = []
|
|
for component, sample in zip(self.components, flag_data):
|
|
save_dir = Path(
|
|
self.dataset_dir
|
|
) / utils.strip_invalid_filename_characters(component.label or "")
|
|
filepath = component.deserialize(sample, save_dir, None)
|
|
csv_data.append(filepath)
|
|
if isinstance(component, tuple(file_preview_types)):
|
|
csv_data.append(
|
|
"{}/resolve/main/{}".format(self.path_to_dataset_repo, filepath)
|
|
)
|
|
csv_data.append(flag_option if flag_option is not None else "")
|
|
writer.writerow(utils.sanitize_list_for_csv(csv_data))
|
|
|
|
if is_new:
|
|
json.dump(infos, open(self.infos_file, "w"))
|
|
|
|
with open(self.log_file, "r", encoding="utf-8") as csvfile:
|
|
line_count = len([None for row in csv.reader(csvfile)]) - 1
|
|
|
|
self.repo.push_to_hub(commit_message="Flagged sample #{}".format(line_count))
|
|
|
|
return line_count
|
|
|
|
|
|
class HuggingFaceDatasetJSONSaver(FlaggingCallback):
|
|
"""
|
|
A FlaggingCallback that saves flagged data to a Hugging Face dataset in JSONL format.
|
|
|
|
Each data sample is saved in a different JSONL file,
|
|
allowing multiple users to use flagging simultaneously.
|
|
Saving to a single CSV would cause errors as only one user can edit at the same time.
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hf_token: str,
|
|
dataset_name: str,
|
|
organization: str | None = None,
|
|
private: bool = False,
|
|
verbose: bool = True,
|
|
):
|
|
"""
|
|
Params:
|
|
hf_token (str): The token to use to access the huggingface API.
|
|
dataset_name (str): The name of the dataset to save the data to, e.g.
|
|
"image-classifier-1"
|
|
organization (str): The name of the organization to which to attach
|
|
the datasets. If None, the dataset attaches to the user only.
|
|
private (bool): If the dataset does not already exist, whether it
|
|
should be created as a private dataset or public. Private datasets
|
|
may require paid huggingface.co accounts
|
|
verbose (bool): Whether to print out the status of the dataset
|
|
creation.
|
|
"""
|
|
self.hf_token = hf_token
|
|
self.dataset_name = dataset_name
|
|
self.organization_name = organization
|
|
self.dataset_private = private
|
|
self.verbose = verbose
|
|
|
|
def setup(self, components: List[IOComponent], flagging_dir: str):
|
|
"""
|
|
Params:
|
|
components List[Component]: list of components for flagging
|
|
flagging_dir (str): local directory where the dataset is cloned,
|
|
updated, and pushed from.
|
|
"""
|
|
try:
|
|
import huggingface_hub
|
|
except (ImportError, ModuleNotFoundError):
|
|
raise ImportError(
|
|
"Package `huggingface_hub` not found is needed "
|
|
"for HuggingFaceDatasetJSONSaver. Try 'pip install huggingface_hub'."
|
|
)
|
|
hh_version = pkg_resources.get_distribution("huggingface_hub").version
|
|
try:
|
|
if StrictVersion(hh_version) < StrictVersion("0.6.0"):
|
|
raise ImportError(
|
|
"The `huggingface_hub` package must be version 0.6.0 or higher"
|
|
"for HuggingFaceDatasetSaver. Try 'pip install huggingface_hub --upgrade'."
|
|
)
|
|
except ValueError:
|
|
pass
|
|
repo_id = huggingface_hub.get_full_repo_name(
|
|
self.dataset_name, token=self.hf_token
|
|
)
|
|
path_to_dataset_repo = huggingface_hub.create_repo(
|
|
repo_id=repo_id,
|
|
token=self.hf_token,
|
|
private=self.dataset_private,
|
|
repo_type="dataset",
|
|
exist_ok=True,
|
|
)
|
|
self.path_to_dataset_repo = path_to_dataset_repo # e.g. "https://huggingface.co/datasets/abidlabs/test-audio-10"
|
|
self.components = components
|
|
self.flagging_dir = flagging_dir
|
|
self.dataset_dir = Path(flagging_dir) / self.dataset_name
|
|
self.repo = huggingface_hub.Repository(
|
|
local_dir=str(self.dataset_dir),
|
|
clone_from=path_to_dataset_repo,
|
|
use_auth_token=self.hf_token,
|
|
)
|
|
self.repo.git_pull(lfs=True)
|
|
|
|
self.infos_file = Path(self.dataset_dir) / "dataset_infos.json"
|
|
|
|
def flag(
|
|
self,
|
|
flag_data: List[Any],
|
|
flag_option: str | None = None,
|
|
flag_index: int | None = None,
|
|
username: str | None = None,
|
|
) -> str:
|
|
self.repo.git_pull(lfs=True)
|
|
|
|
# Generate unique folder for the flagged sample
|
|
unique_name = self.get_unique_name() # unique name for folder
|
|
folder_name = (
|
|
Path(self.dataset_dir) / unique_name
|
|
) # unique folder for specific example
|
|
os.makedirs(folder_name)
|
|
|
|
# Now uses the existence of `dataset_infos.json` to determine if new
|
|
is_new = not Path(self.infos_file).exists()
|
|
|
|
# File previews for certain input and output types
|
|
infos, file_preview_types, _ = _get_dataset_features_info(
|
|
is_new, self.components
|
|
)
|
|
|
|
# Generate the row and header corresponding to the flagged sample
|
|
csv_data = []
|
|
headers = []
|
|
|
|
for component, sample in zip(self.components, flag_data):
|
|
headers.append(component.label)
|
|
|
|
try:
|
|
save_dir = Path(folder_name) / utils.strip_invalid_filename_characters(
|
|
component.label or ""
|
|
)
|
|
filepath = component.deserialize(sample, save_dir, None)
|
|
except Exception:
|
|
# Could not parse 'sample' (mostly) because it was None and `component.save_flagged`
|
|
# does not handle None cases.
|
|
# for example: Label (line 3109 of components.py raises an error if data is None)
|
|
filepath = None
|
|
|
|
if isinstance(component, tuple(file_preview_types)):
|
|
headers.append(component.label or "" + " file")
|
|
|
|
csv_data.append(
|
|
"{}/resolve/main/{}/{}".format(
|
|
self.path_to_dataset_repo, unique_name, filepath
|
|
)
|
|
if filepath is not None
|
|
else None
|
|
)
|
|
|
|
csv_data.append(filepath)
|
|
headers.append("flag")
|
|
csv_data.append(flag_option if flag_option is not None else "")
|
|
|
|
# Creates metadata dict from row data and dumps it
|
|
metadata_dict = {
|
|
header: _csv_data for header, _csv_data in zip(headers, csv_data)
|
|
}
|
|
self.dump_json(metadata_dict, Path(folder_name) / "metadata.jsonl")
|
|
|
|
if is_new:
|
|
json.dump(infos, open(self.infos_file, "w"))
|
|
|
|
self.repo.push_to_hub(commit_message="Flagged sample {}".format(unique_name))
|
|
return unique_name
|
|
|
|
def get_unique_name(self):
|
|
id = uuid.uuid4()
|
|
return str(id)
|
|
|
|
def dump_json(self, thing: dict, file_path: str | Path) -> None:
|
|
with open(file_path, "w+", encoding="utf8") as f:
|
|
json.dump(thing, f)
|
|
|
|
|
|
class FlagMethod:
|
|
"""
|
|
Helper class that contains the flagging button option and callback
|
|
"""
|
|
|
|
def __init__(self, flagging_callback: FlaggingCallback, flag_option=None):
|
|
self.flagging_callback = flagging_callback
|
|
self.flag_option = flag_option
|
|
self.__name__ = "Flag"
|
|
|
|
def __call__(self, *flag_data):
|
|
self.flagging_callback.flag(list(flag_data), flag_option=self.flag_option)
|