Upload medical_prescription_dataset.py with huggingface_hub

medical_prescription_dataset.py

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+"""Medical Prescription OCR Dataset"""
+
+import json
+import os
+
+import datasets
+from PIL import Image
+
+
+_DESCRIPTION = """
+Medical Prescription OCR Dataset - A collection of synthetic handwritten medical prescriptions
+with structured annotations for training OCR models.
+"""
+
+_CITATION = """
+@dataset{shrivastava2024medicalprescription,
+  author = {Chinmay Shrivastava},
+  title = {Medical Prescription OCR Dataset},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/chinmays18/medical-prescription-dataset}
+}
+"""
+
+
+class MedicalPrescriptionDataset(datasets.GeneratorBasedBuilder):
+    """Medical Prescription OCR Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "image": datasets.Image(),
+                "ground_truth": datasets.Value("string"),
+            }),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # The dataset files are already in the repository
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images_path": "train/images",
+                    "annotations_path": "train/annotations",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "images_path": "val/images",
+                    "annotations_path": "val/annotations",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images_path": "test/images",
+                    "annotations_path": "test/annotations",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images_path, annotations_path):
+        # Get all image files
+        image_files = sorted([f for f in os.listdir(images_path) if f.endswith('.png')])
+        
+        for idx, image_file in enumerate(image_files):
+            # Get corresponding annotation file
+            base_name = os.path.splitext(image_file)[0]
+            annotation_file = f"{base_name}.json"
+            
+            # Read image
+            image_path = os.path.join(images_path, image_file)
+            
+            # Read annotation
+            annotation_path = os.path.join(annotations_path, annotation_file)
+            with open(annotation_path, 'r') as f:
+                annotation = json.load(f)
+            
+            yield idx, {
+                "image": image_path,
+                "ground_truth": annotation["ground_truth"],
+            }