Delete data_loader.py

data_loader.py

@@ -1,52 +0,0 @@
-import json
-import datasets
-import pandas as pd
-
-logger = datasets.logging.get_logger(__name__)
-
-labels = pd.read_csv('/Users/Atharv/All scripts/Project/Multi label classification/train_classes.csv')
-
-_URL = "https://huggingface.co/datasets/subhuatharva/amazon_from_space/resolve/main/train-jpg.tar.gz?download=true"
-
-
-class amazon_from_space(datasets.GeneratorBasedBuilder):
-    """Planet Dataset: Understanding Amazon Rainforest from Space"""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description = _DESCRIPTION,
-            features = datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "labels": datasets.Value("string"),
-                }
-            ),
-            supervised_keys = None,
-            homepage = "https://huggingface.co/datasets/subhuatharva/amazon_from_space",
-        )
-
-    def _split_generators(self, dl_manager):
-        path = dl_manager.download_and_extract(_URL)
-        image_iters = dl_manager.iter_archive(path)
-        
-        return [
-            datasets.SplitGenerator(
-                name = datasets.Split.TRAIN,
-                gen_kwargs = {
-                    "images": image_iters
-                }
-            ),
-        ]
-    def _generate_examples(self, images):
-        """This function returns the examples in the raw (text) form."""
-
-        idx = 0
-
-        # iterate through images
-        for filepath, image in images:
-            yield idx, {
-                "image": {"filepath": filepath, "image": image.read()},
-                "text": labels['tags'][idx],
-            }
-            
-            idx+=1