Create script.py

script.py

@@ -0,0 +1,84 @@
+# 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.
+"""Dataset class for Shoe40k dataset."""
+
+import datasets
+from datasets.tasks import ImageClassification
+import pandas as pd
+import json
+import requests
+
+
+_HOMEPAGE = "https://huggingface.co/datasets/aidystark/shoe41k"
+
+_DESCRIPTION = (
+"----------------------------------------"    
+)
+
+_CITATION = """\
+
+"""
+
+_LICENSE = """\
+LICENSE AGREEMENT
+=================
+"""
+
+_NAMES      = ['Dressing Shoe', 'Boot', 'Crocs', 'Heels', 'Sandals', 'Sneakers']
+_CSV   =  "https://huggingface.co/datasets/aidystark/shoe41k/resolve/main/FOOT40K.csv"
+_URL   =  "https://huggingface.co/datasets/aidystark/shoe41k/resolve/main/shoe40k"
+
+
+
+df = pd.read_csv(_CSV)
+imgLabels = df['Label']
+
+
+class shoe40k(datasets.GeneratorBasedBuilder):
+    """-------"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[ImageClassification(image_column="image", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        path        = dl_manager.download(_URL)
+        image_iters = dl_manager.iter_archive(path)
+        return [datasets.SplitGenerator(datasets.Split.TRAIN,gen_kwargs={"images":image_iters,})]
+    
+
+    def _generate_examples(self, images):
+        """Generate images and labels for splits."""
+        idx = 0
+        #Iterate through images
+        for filepath,image in images:
+            yield idx, {
+                "image":{"path":filepath, "bytes":image.read()},
+                "label":imgLabels[idx]
+            }
+            idx += 1
+