dataset-card update

dataset.py

@@ -1,7 +1,61 @@
-from datasets import load_dataset
+import os
+import pandas as pd
+from datasets import GeneratorBasedBuilder, DatasetInfo, Features, Image, Value, SplitGenerator
 
-# Load your dataset (make sure this path is correct and the dataset loads successfully)
-dataset = load_dataset("imagefolder", data_dir="C:/Users/nave1/Desktop/Final-project/Urban-Climate/data")
+class UrbanClimateDataset(GeneratorBasedBuilder):
+    """Urban Climate Dataset for building detection."""
 
-# Push the dataset to the Hugging Face Hub
-dataset.push_to_hub("nave1616/urban_climate_dataset")
+    VERSION = "1.0.0"
+
+    def _info(self):
+        return DatasetInfo(
+            description="Dataset for building detection in urban environments using image segmentation.",
+            features=Features({
+                "file_name": Value("string"),
+                "image": Image(),
+                "mask": Image(),
+                "type": Value("string"),
+            }),
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/nave1616/urban_climate_dataset",
+            license="CC BY-SA 4.0",
+            citation="""@article{your_citation}"""
+        )
+
+    def _split_generators(self, dl_manager):
+        """Specify data splits."""
+        base_path = dl_manager.download_and_extract(self.config.data_dir)
+        
+        return [
+            SplitGenerator(
+                name="train",
+                gen_kwargs={"files_dir": os.path.join(base_path, "train")},
+            ),
+            SplitGenerator(
+                name="validation",
+                gen_kwargs={"files_dir": os.path.join(base_path, "validation")},
+            ),
+            SplitGenerator(
+                name="test",
+                gen_kwargs={"files_dir": os.path.join(base_path, "test")},
+            ),
+        ]
+
+    def _generate_examples(self, files_dir):
+        """Generate examples from metadata CSV files."""
+        metadata_path = os.path.join(files_dir, 'metadata.csv')
+        df = pd.read_csv(metadata_path)
+        for idx, row in df.iterrows():
+            image_path = os.path.join(files_dir, row["file_name"])
+            # Assume that mask images are in the same directory and have the same name with a different prefix
+            if row['type'] == 'image':
+                mask_path = image_path.replace('image', 'mask')  # Replace to get the corresponding mask path
+            else:
+                mask_path = image_path
+
+            yield idx, {
+                "file_name": row["file_name"],
+                "image": image_path if row['type'] == 'image' else None,
+                "mask": mask_path if row['type'] == 'mask' else None,
+                "type": row["type"],
+            }