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flagging.py
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from __future__ import annotations
import csv
import datetime
import io
import json
import os
import uuid
from abc import ABC, abstractmethod
from pathlib import Path
from typing import TYPE_CHECKING, Any, List
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 = [
component.label 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(
component.label 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'."
)
path_to_dataset_repo = huggingface_hub.create_repo(
name=self.dataset_name,
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_foken: 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_foken = hf_foken
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'."
)
path_to_dataset_repo = huggingface_hub.create_repo(
name=self.dataset_name,
token=self.hf_foken,
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_foken,
)
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)