Testing image dataset upload

faceid-data2.py

@@ -1,78 +0,0 @@
-# dataset_script.py
-import os
-import json
-from pathlib import Path
-
-import datasets
-from datasets import (
-    GeneratorBasedBuilder,
-    DatasetInfo,
-    SplitGenerator,
-    Features,
-    Sequence,
-    Value,
-    Image
-)
-
-_CITATION = """\
-@misc{faceid2025,
-  title={FaceID for nanoVLM},
-  author={Your Name},
-  year={2025},
-}
-"""
-
-_DESCRIPTION = """
-FaceID dataset: for each image, a single-question Q&A pair:
-"Question: Who is the person?" → "Answer: <Name>"
-"""
-
-class FaceIDDataset(GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return DatasetInfo(
-            description=_DESCRIPTION,
-            features=Features({
-                # a *list* of images (we only have one per example, but must match your JSONL)
-                "images": Sequence(feature=Image(decode=True)),
-                # a list of QA dicts
-                "texts":  Sequence({
-                    "user":      Value("string"),
-                    "assistant": Value("string"),
-                    "source":    Value("string"),
-                }),
-            }),
-            supervised_keys=None,
-            homepage="https://huggingface.co/datasets/AgtOwad/faceid-data2",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        # we already have JSONL + images in the same folder, no download needed
-        data_dir = Path(__file__).parent
-        return [
-            SplitGenerator(name=datasets.Split.TRAIN, 
-                           gen_kwargs={"jsonl": data_dir / "train.jsonl"}),
-            SplitGenerator(name=datasets.Split.VALIDATION,
-                           gen_kwargs={"jsonl": data_dir / "val.jsonl"}),
-        ]
-
-    def _generate_examples(self, jsonl):
-        base = Path(__file__).parent
-        with open(jsonl, "r", encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                ex = json.loads(line)
-                # images is a list of relative paths under this repo
-                img_list = [ str(base / p) for p in ex["images"] ]
-                yield idx, {
-                    "images": img_list,
-                    "texts":  ex["texts"]
-                }
-
-# if __name__ == "__main__":
-#     # this block is optional—your script will be discovered automatically by load_dataset
-#     from datasets import load_dataset
-#     ds = load_dataset(__file__)
-#     print(ds)

train.parquet

@@ -0,0 +1,3 @@
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+size 3880355

validation.parquet

@@ -0,0 +1,3 @@
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