Delete loading script

beans.py

@@ -1,102 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""Beans leaf dataset with images of diseased and health leaves."""
-
-import os
-
-import datasets
-from datasets.tasks import ImageClassification
-
-
-_HOMEPAGE = "https://github.com/AI-Lab-Makerere/ibean/"
-
-_CITATION = """\
-@ONLINE {beansdata,
-    author="Makerere AI Lab",
-    title="Bean disease dataset",
-    month="January",
-    year="2020",
-    url="https://github.com/AI-Lab-Makerere/ibean/"
-}
-"""
-
-_DESCRIPTION = """\
-Beans is a dataset of images of beans taken in the field using smartphone
-cameras. It consists of 3 classes: 2 disease classes and the healthy class.
-Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
-by experts from the National Crops Resources Research Institute (NaCRRI) in
-Uganda and collected by the Makerere AI research lab.
-"""
-
-_URLS = {
-    "train": "https://huggingface.co/datasets/beans/resolve/main/data/train.zip",
-    "validation": "https://huggingface.co/datasets/beans/resolve/main/data/validation.zip",
-    "test": "https://huggingface.co/datasets/beans/resolve/main/data/test.zip",
-}
-
-_NAMES = ["angular_leaf_spot", "bean_rust", "healthy"]
-
-
-class Beans(datasets.GeneratorBasedBuilder):
-    """Beans plant leaf images dataset."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image_file_path": datasets.Value("string"),
-                    "image": datasets.Image(),
-                    "labels": datasets.features.ClassLabel(names=_NAMES),
-                }
-            ),
-            supervised_keys=("image", "labels"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            task_templates=[ImageClassification(image_column="image", label_column="labels")],
-        )
-
-    def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["train"]]),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["validation"]]),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["test"]]),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, files):
-        for i, path in enumerate(files):
-            file_name = os.path.basename(path)
-            if file_name.endswith(".jpg"):
-                yield i, {
-                    "image_file_path": path,
-                    "image": path,
-                    "labels": os.path.basename(os.path.dirname(path)).lower(),
-                }