Upload huggingface-images.py

huggingface-images.py

@@ -0,0 +1,77 @@
+import os
+import datasets
+
+# Constants for your dataset
+_DESCRIPTION = """\
+This dataset includes images with associated IDs, titles, and URLs. There are two types of images: 'Listing Image' and 'Search-image'.
+"""
+_LABEL_MAP = {
+    'Listing Image': 'listing_image',
+    'Search-image': 'search_image',
+}
+
+class MyDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for MyDataset."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for MyDataset.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MyDatasetConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+
+
+class MyDataset(datasets.GeneratorBasedBuilder):
+    """My custom dataset."""
+
+    BUILDER_CONFIGS = [
+        MyDatasetConfig(
+            name="default",
+            description="This version of the dataset contains two types of images with metadata.",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "listing_title": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "listing_image": datasets.Image(),
+                    "search_image": datasets.Image(),
+                }
+            ),
+            supervised_keys=None,
+            homepage="Your dataset homepage here",
+            license="Your dataset's license here",
+            citation="Your dataset's citation here",
+        )
+
+    def _split_generators(self, dl_manager):
+        # You would have a way to access and download your data, for example, from a Google Cloud Storage Bucket
+        # For simplicity, we are assuming your data is already downloaded and accessible
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "datapath": "path_to_your_downloaded_data",
+                },
+            )
+        ]
+
+    def _generate_examples(self, datapath):
+        # Here you will write the logic to read your dataset's contents
+        # For example, let's say you have a CSV file with all the metadata and the links to the images
+        # You would read the CSV file and for each row, yield the following:
+        with open(datapath, encoding="utf-8") as csv_file:
+            reader = csv.DictReader(csv_file)
+            for idx, row in enumerate(reader):
+                yield idx, {
+                    "id": row['id'],
+                    "listing_title": row['listing-title'],
+                    "url": row['url'],
+                    "listing_image": row['Listing Image'],
+                    "search_image": row['Search-image'],
+                }