Upload images-targz.py

images-targz.py

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+"""Residential Floorplans and City Scapes dataset for Urban planning"""
+
+
+import json
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
+}
+"""
+
+_DESCRIPTION = """\
+Text-to-image model to build an AI architect
+"""
+
+_URL = "https://huggingface.co/datasets/wheres-my-python/image-trial/resolve/main/images.tar.gz"
+
+# descriptions = [] #optional text data
+
+class ImagesTrial(datasets.GeneratorBasedBuilder):
+    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                # Option to use any Apache arrow feature other than "string"
+                {
+                    "text": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    # "prompt": datasets.Value("string"),   (optional)
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/wheres-my-python/floorplans-cityscapes",
+            citation=_CITATION,
+            
+        )
+
+    def _split_generators(self, dl_manager):
+        #download manager - hf utility
+        path = dl_manager.download_and_extract(_URL)
+        image_iters = dl_manager.iter_archive(path)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN
+                , gen_kwargs={
+                    "images": image_iters
+                }
+            ),
+        ]
+
+    def _generate_examples(self, images):
+        """This function returns the examples in the raw (text) form."""
+        idx = 0
+        #iteratre through images
+        for filepath, image in images:
+            yield idx, {
+                "image": {"filepath":filepath, "image":image.read()},
+                #Option to map text
+
+                # "text": descriptions[idx],
+            }
+            idx +=1