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data/metadata.jsonl

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+{"file_name": "alvan-nee-9M0tSjb-cpA-unsplash.jpeg", "text": "cute dog"}
+{"file_name": "alvan-nee-Id1DBHv4fbg-unsplash.jpeg", "text": "cute dog"}
+{"file_name": "alvan-nee-bQaAJCbNq3g-unsplash.jpeg", "text": "cute dog"}
+{"file_name": "alvan-nee-eoqnr8ikwFE-unsplash.jpeg", "text": "cute dog"}
+{"file_name": "alvan-nee-brFsZ7qszSY-unsplash.jpeg", "text": "cute dog"}

dataset.py

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+from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, DownloadManager
+from typing import Any, Dict, List, Tuple
+import os
+import json
+
+class ProcessedImageDataset(GeneratorBasedBuilder):
+    VERSION = "1.0.0" 
+
+    def _info(self) -> DatasetInfo:
+        # Specify dataset info here
+        return DatasetInfo(
+            # You can add description, citation, homepage, etc.
+            features=self._features(),
+            supervised_keys=("image", "text"),
+        )
+
+    def _features(self):
+        # Define the features of your dataset: image file, text, etc.
+        from datasets import Features, Image, Value
+        return Features({"image_file": Image(), "text": Value("string")})
+
+    def _split_generators(self, dl_manager) -> List[SplitGenerator]:
+        # This method is tasked with downloading/extracting the data and defining the splits
+        print(self.config.data_dir)
+        if self.config.data_dir is None:
+            raise ValueError(f'Data directory unspecified. Correct usage is: load_dataset(script_path, data_dir=data_dir_path)')
+        return [SplitGenerator(name="train", gen_kwargs={"data_dir": self.config.data_dir})]
+
+    def _generate_examples(self, data_dir):
+        # def _generate_examples(self, data_dir: str) -> Tuple[int, Dict[str, Any]]:
+        # This method will read the data and yield examples
+        metadata_file_path = os.path.join(data_dir, "metadata.jsonl")
+
+        # Read metadata and store it in a dictionary
+        metadata = {}
+        with open(metadata_file_path, "r") as f:
+            for line in f:
+                item = json.loads(line)
+                metadata[item["file_name"]] = item
+
+        # Iterate through each file in the data directory
+        for filename in os.listdir(data_dir):
+            if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".jpeg"):
+                if filename in metadata:
+                    metadata_entry = metadata[filename]
+                    yield filename, {
+                        "image_file": os.path.join(data_dir, filename),
+                        "text": metadata_entry["text"],
+                    }