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Handwritten_Khatt.py

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+import datasets
+import pandas as pd
+import os
+import logging
+
+_DESCRIPTION = """\
+Arabic Handwritten dataset.
+"""
+
+_REPO = "https://huggingface.co/datasets/eDaraty/Handwritten_Khatt"
+
+
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+
+
+class Khatt(datasets.GeneratorBasedBuilder):
+    """Handwritten arabic image-text pairs"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    'image': datasets.Image(),
+                    'text': datasets.Value("string"),
+                }
+            ),
+            # supervised_keys=None,
+            homepage=_REPO,
+            # citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_archive = dl_manager.download(f"{_REPO}/resolve/main/data/train.zip")
+        test_archive = dl_manager.download(f"{_REPO}/resolve/main/data/test.zip")
+        val_archive = dl_manager.download(f"{_REPO}/resolve/main/data/validation.zip")
+        # split_metadata_paths = dl_manager.download(_METADATA_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(train_archive)
+                },
+            ),
+
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(test_archive)
+                },
+            ),
+
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(val_archive)
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images):
+        """ This function returns the examples in the raw (text) form."""
+        df = pd.read_csv(f"{_REPO}/resolve/main/data/metadata.csv")
+
+        for idx, (filepath, image) in enumerate(images):
+            image_name = os.path.basename(filepath)
+            # logger.info(" filename of image is '%s' ", image_name)
+
+            description = df[df["file_name"] == image_name]['text'].values.tolist()[0]
+            # logger.info(" text of image is '%s' ", description)
+            # logger.info(" type of image is '%s' ", type(description))
+            yield idx, {
+                "image": {"path": filepath, "bytes": image.read()},
+                "text": description,
+            }