Upload data_loader.py

data_loader.py

@@ -0,0 +1,52 @@
+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