feat: update loading script for JSON

patfig.py

@@ -4,6 +4,7 @@ from datasets import load_dataset, Dataset, Value, Sequence, Features, DatasetIn
 from pathlib import Path
 import os
 import pandas as pd
+import json
 
 _DESCRIPTION = """\ The PatFig Dataset is a curated collection of over 18,000 patent images from more than 7,
 000 European patent applications, spanning the year 2020. It aims to provide a comprehensive resource for research 
@@ -14,18 +15,18 @@ hollistic consumption of the visual and textual data.
 
 _BASE_URL = "https://huggingface.co/datasets/lcolonn/patfig/resolve/main/"
 _METADATA_URLS = {
-    "annotations_train": "train/annotations_train.parquet",
-    "annotations_test": "test/annotations_test.parquet"
+    "annotations_train": "train/annotations_train.zip",
+    "annotations_test": "test/annotations_test.zip"
 }
 _IMAGES_URLS = {
-    "images_train": "train/train_images.tar.gz",
-    "images_test": "test/test_images.tar.gz",
+    "test_images": "train/train_images.tar.gz",
+    "train_images": "test/test_images.tar.gz",
 }
 _URLS = {
     "train_images": "train/train_images.tar.gz",
     "test_images": "test/test_images.tar.gz",
-    "annotations_train": "train/annotations_train.parquet",
-    "annotations_test": "test/annotations_test.parquet",
+    "annotations_train": "train/annotations_train.zip",
+    "annotations_test": "test/annotations_test.zip",
 }
 
 
@@ -35,23 +36,23 @@ class PatFig(GeneratorBasedBuilder):
     def _info(self):
         return DatasetInfo(
             description=_DESCRIPTION,
-            features=Features({
-                "image": Image(),
-                "image_name": Value("string"),
-                "pub_number": Value("string"),
-                "title": Value("string"),
-                "figs_norm": Sequence(feature=Value("string"), length=-1),
-                "short_description": Sequence(feature=Value("string"), length=-1),
-                "long_description": Sequence(feature=Value("string"), length=-1),
-                "short_description_token_count": Value("int64"),
-                "long_description_token_count": Value("int64"),
-                "draft_class": Value("string"),
-                "cpc_class": Value("string"),
-                "relevant_terms": [{'element_identifier': Value("string"), "terms": Sequence(feature=Value("string"), length=-1)}],
-                "associated_claims": Value("string"),
-                "compound": Value("bool"),
-                "references": Sequence(feature=Value(dtype='string'), length=-1),
-            }),
+            # features=Features({
+            #     "image": Image(),
+            #     "image_name": Value("string"),
+            #     "pub_number": Value("string"),
+            #     "title": Value("string"),
+            #     "figs_norm": Sequence(feature=Value("string"), length=-1),
+            #     "short_description": Sequence(feature=Value("string"), length=-1),
+            #     "long_description": Sequence(feature=Value("string"), length=-1),
+            #     "short_description_token_count": Value("int64"),
+            #     "long_description_token_count": Value("int64"),
+            #     "draft_class": Value("string"),
+            #     "cpc_class": Value("string"),
+            #     "relevant_terms": [{'element_identifier': Value("string"), "terms": Sequence(feature=Value("string"), length=-1)}],
+            #     "associated_claims": Value("string"),
+            #     "compound": Value("bool"),
+            #     "references": Sequence(feature=Value(dtype='string'), length=-1),
+            # }),
         )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
@@ -62,16 +63,16 @@ class PatFig(GeneratorBasedBuilder):
                 name=datasets.Split.TRAIN, gen_kwargs={"images_dir": downloaded_files["train_images"], "annotations_dir": downloaded_files["annotations_train"]}
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"images_dir": f'{downloaded_files["test_images"]}', "annotations_dir": downloaded_files["annotations_test"]}
+                name=datasets.Split.TEST, gen_kwargs={"images_dir": downloaded_files["test_images"], "annotations_dir": downloaded_files["annotations_test"]}
             ),
         ]
 
     def _generate_examples(self, images_dir: str, annotations_dir: str):
-        df = pd.read_csv(annotations_dir)
-
-        for idx, row in df.iterrows():
-            image_path = os.path.join(images_dir, row["pub_number"], row["image_name"])
-            yield idx, {
-                "image": image_path,
-                **row.to_dict(),
-            }
+        with open(annotations_dir, "r") as f:
+            data = json.load(f)
+            for idx, record in enumerate(data):
+                image_path = os.path.join(images_dir, record["pub_number"], record["image_name"])
+                yield idx, {
+                    "image": image_path,
+                    **record,
+                }