Rename dataset.py to dataset_loader.py

dataset.py

@@ -1,55 +0,0 @@
-import csv
-import datasets
-
-_CITATION = """\
-@dataset{qasim2025animalsounds,
-  title = {Animal Sound Classification Dataset},
-  author = {Muhammad Qasim},
-  year = {2025},
-  url = {https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification}
-}
-"""
-
-_DESCRIPTION = """\
-A meticulously curated dataset of labeled animal sounds (dogs, cats, cows) for audio classification tasks. The dataset contains trimmed and cleaned audio clips with labels.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification"
-
-_LICENSE = "mit"
-
-_URLS = {
-    "train": "ML1_features.csv"
-}
-
-class AnimalSoundClassification(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                "audio": datasets.Value("string"),  # path to audio file or URL
-                "label": datasets.ClassLabel(names=["dog", "cat", "cow"]),
-            }),
-            supervised_keys=("audio", "label"),
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_path = dl_manager.download_and_extract(_URLS["train"])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": data_path},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        with open(filepath, encoding="utf-8") as csv_file:
-            reader = csv.DictReader(csv_file)
-            for id_, row in enumerate(reader):
-                yield id_, {
-                    "audio": row["audio"],  # Make sure column name matches your CSV
-                    "label": row["label"].lower(),  # Lowercasing to match ClassLabel names
-                }

dataset_loader.py

@@ -0,0 +1,9 @@
+from datasets import load_dataset
+
+def load_data():
+    dataset = load_dataset('csv', data_files='ML1_features.csv')
+    return dataset
+
+# Example usage
+dataset = load_data()
+print(dataset)