docci.py

# coding=utf-8
# Copyright 2022 the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import pandas as pd 
import datasets
from huggingface_hub import hf_hub_url

_INPUT_CSV = "docci.csv"  # Update the CSV file name
_INPUT_IMAGES = "docci_images"    # Update the images directory name
_REPO_ID = "yonatanbitton/docci"         # Update the repository ID
_SUFFIX = 'jpg'

class Dataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="docci", version=VERSION, description="Docci dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features({
                "image_key": datasets.Value("string"),
                "description": datasets.Value('string'),
                "image": datasets.Image(),
            }),
            supervised_keys=None,  # Update or remove if your dataset is supervised
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # hf_auth_token = dl_manager.download_config.use_auth_token
        # if hf_auth_token is None:
        #     raise ConnectionError(
        #         "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset"
        #     )

        data_dir = dl_manager.download_and_extract({
            "examples_csv": hf_hub_url(repo_id=_REPO_ID, repo_type='dataset', filename=_INPUT_CSV),
            "images_dir": hf_hub_url(repo_id=_REPO_ID, repo_type='dataset', filename=f"{_INPUT_IMAGES}.zip")
        })

        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=data_dir)]

    def _generate_examples(self, examples_csv, images_dir):
        """Yields examples."""
        df = pd.read_csv(examples_csv)

        for r_idx, r in df.iterrows():
            image_path = os.path.join(images_dir, _INPUT_IMAGES, f"{r['image_key']}.{_SUFFIX}")  
            yield r_idx, {
                "image_key": r['image_key'],
                "description": r['description'],
                "image": image_path
            }