Upload wiki_art.py with huggingface_hub

wiki_art.py

@@ -0,0 +1,111 @@
+import os
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {WikiArt},
+author={Medellín AI.
+},
+year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+Este dataset fue creado para el workshop de Medellin AI y Bancolombia con fines educativos.
+"""
+
+_HOMEPAGE = "https://www.meetup.com/medellin-ai/"
+
+_LICENSE = "mit"
+
+_URLS = {
+    "train": "https://workshophuggingface.blob.core.windows.net/wikiart/train.zip",
+    "test": "https://workshophuggingface.blob.core.windows.net/wikiart/test.zip"
+}
+
+_NAMES = ["Baroque", "Realism"]
+
+
+
+
+class WikiArt(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+    DEFAULT_WRITER_BATCH_SIZE = 200
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="All", version=VERSION, description="This contains the whole dataset"),
+        datasets.BuilderConfig(name="Baroque", version=VERSION, description="This part of the dataset contains only Baroque style"),
+        datasets.BuilderConfig(name="Realism", version=VERSION, description="This part of the dataset contains only Realism style"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "style": datasets.features.ClassLabel(names=_NAMES),
+                "artwork": datasets.Value("string"),
+                "image": datasets.Image(decode=True)
+            }
+        )
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("image", "style"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+
+    def _split_generators(self, dl_manager):
+        
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        
+        data_dir = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "folderpath" : data_dir['train'],
+                    "csv_file": 'wikiart_scraped_train.csv',
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "folderpath" : data_dir['test'],
+                    "csv_file": 'wikiart_scraped_test.csv',
+                    "split": "test"
+                },
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, folderpath, csv_file, split):
+
+
+        df_wiki_art = pd.read_csv(os.path.join(folderpath,split,csv_file), header=0)
+
+        if self.config.name != 'All':
+            df_wiki_art.query(f"Style == '{self.config.name}'", inplace=True)
+
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        for index, row in df_wiki_art.iterrows():
+
+            image_path = os.path.join(folderpath,split,row['Link'].split('/')[-1])
+            # Yields examples as (key, example) tuples
+            yield index, {
+                "style": row["Style"],
+                "artwork": row["Artwork"],
+                "image": image_path
+            }