hfserver.py

from __future__ import annotations

import csv
import datetime
import io
import json
import os
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any, List, Optional

import gradio as gr
from gradio import encryptor, utils

if TYPE_CHECKING:
    from gradio.components import Component


class FlaggingCallback(ABC):
    """
    An abstract class for defining the methods that any FlaggingCallback should have.
    """

    @abstractmethod
    def setup(self, components: List[Component], 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: Optional[str] = None,
        flag_index: Optional[int] = None,
        username: Optional[str] = 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


class SimpleCSVLogger(FlaggingCallback):
    """
    A simple example implementation of the FlaggingCallback abstract class
    provided for illustrative purposes.
    """

    def setup(self, components: List[Component], flagging_dir: str):
        self.components = components
        self.flagging_dir = flagging_dir
        os.makedirs(flagging_dir, exist_ok=True)

    def flag(
        self,
        flag_data: List[Any],
        flag_option: Optional[str] = None,
        flag_index: Optional[int] = None,
        username: Optional[str] = None,
    ) -> int:
        flagging_dir = self.flagging_dir
        log_filepath = os.path.join(flagging_dir, "log.csv")

        csv_data = []
        for component, sample in zip(self.components, flag_data):
            csv_data.append(
                component.save_flagged(
                    flagging_dir,
                    component.label,
                    sample,
                    None,
                )
            )

        with open(log_filepath, "a", newline="") as csvfile:
            writer = csv.writer(csvfile, quoting=csv.QUOTE_NONNUMERIC, quotechar="'")
            writer.writerow(csv_data)

        with open(log_filepath, "r") as csvfile:
            line_count = len([None for row in csv.reader(csvfile)]) - 1
        return line_count


class CSVLogger(FlaggingCallback):
    """
    The default implementation of the FlaggingCallback abstract class.
    Logs the input and output data to a CSV file. Supports encryption.
    """

    def setup(
        self,
        components: List[Component],
        flagging_dir: str,
        encryption_key: Optional[str] = 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: Optional[str] = None,
        flag_index: Optional[int] = None,
        username: Optional[str] = None,
    ) -> int:
        flagging_dir = self.flagging_dir
        log_filepath = os.path.join(flagging_dir, "log.csv")
        is_new = not os.path.exists(log_filepath)

        if flag_index is None:
            csv_data = []
            for component, sample in zip(self.components, flag_data):
                csv_data.append(
                    component.save_flagged(
                        flagging_dir,
                        component.label,
                        sample,
                        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()))
            if is_new:
                headers = [component.label for component in self.components] + [
                    "flag",
                    "username",
                    "timestamp",
                ]

        def replace_flag_at_index(file_content):
            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
            output = io.StringIO()
            writer = csv.writer(output, quoting=csv.QUOTE_NONNUMERIC, quotechar="'")
            writer.writerows(content)
            return output.getvalue()

        if self.encryption_key:
            output = io.StringIO()
            if not is_new:
                with open(log_filepath, "rb") 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)
                    output.write(file_content)
            writer = csv.writer(output, quoting=csv.QUOTE_NONNUMERIC, quotechar="'")
            if flag_index is None:
                if is_new:
                    writer.writerow(headers)
                writer.writerow(csv_data)
            with open(log_filepath, "wb") as csvfile:
                csvfile.write(
                    encryptor.encrypt(self.encryption_key, output.getvalue().encode())
                )
        else:
            if flag_index is None:
                with open(log_filepath, "a", newline="") as csvfile:
                    writer = csv.writer(
                        csvfile, quoting=csv.QUOTE_NONNUMERIC, quotechar="'"
                    )
                    if is_new:
                        writer.writerow(headers)
                    writer.writerow(csv_data)
            else:
                with open(log_filepath) as csvfile:
                    file_content = csvfile.read()
                    file_content = replace_flag_at_index(file_content)
                with open(
                    log_filepath, "w", newline=""
                ) as csvfile:  # newline parameter needed for Windows
                    csvfile.write(file_content)
        with open(log_filepath, "r") as csvfile:
            line_count = len([None for row in csv.reader(csvfile)]) - 1
        return line_count


class HuggingFaceDatasetSaver(FlaggingCallback):
    """
    A FlaggingCallback that saves flagged data to a HuggingFace dataset.
    """

    def __init__(
        self,
        hf_foken: str,
        dataset_name: str,
        organization: Optional[str] = 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[Component], 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,  https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/hf_api.py
            repo_id=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 = os.path.join(flagging_dir, self.dataset_name)
        self.repo = huggingface_hub.Repository(
            local_dir=self.dataset_dir,
            clone_from=path_to_dataset_repo,
            use_auth_token=self.hf_foken,
        )
        self.repo.git_pull()

        # Should filename be user-specified?
        self.log_file = os.path.join(self.dataset_dir, "data.csv")
        self.infos_file = os.path.join(self.dataset_dir, "dataset_infos.json")

    def flag(
        self,
        flag_data: List[Any],
        flag_option: Optional[str] = None,
        flag_index: Optional[int] = None,
        username: Optional[str] = None,
    ) -> int:
        is_new = not os.path.exists(self.log_file)
        infos = {"flagged": {"features": {}}}

        with open(self.log_file, "a", newline="") as csvfile:
            writer = csv.writer(csvfile)

            # File previews for certain input and output types
            file_preview_types = {
                gr.inputs.Audio: "Audio",
                gr.outputs.Audio: "Audio",
                gr.inputs.Image: "Image",
                gr.outputs.Image: "Image",
            }

            # Generate the headers and dataset_infos
            if is_new:
                headers = []

                for component, sample in zip(self.components, flag_data):
                    headers.append(component.label)
                    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 + " file"
                                ] = {"_type": _type}
                                break

                headers.append("flag")
                infos["flagged"]["features"]["flag"] = {
                    "dtype": "string",
                    "_type": "Value",
                }

                writer.writerow(headers)

            # Generate the row corresponding to the flagged sample
            csv_data = []
            for component, sample in zip(self.components, flag_data):
                filepath = component.save_flagged(
                    self.dataset_dir, component.label, sample, 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(csv_data)

        if is_new:
            json.dump(infos, open(self.infos_file, "w"))

        with open(self.log_file, "r") 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