Update renovation.py

renovation.py

@@ -55,58 +55,58 @@ class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
             task_templates=[ImageClassification(image_column="image", label_column="labels")],
         )
 
-     def _split_generators(self, dl_manager):
-            # Download and extract images
-            image_paths = []
-            for label, url in _URLS.items():
-                extract_path = dl_manager.download_and_extract(url)
-                print(f"Extracted files for label {label} to path: {extract_path}")
-    
-                # Get image paths
-                for root, _, files in os.walk(extract_path):
-                    for file in files:
-                        if file.endswith(".jpeg"):  # Assuming all images are .jpeg
-                            image_paths.append((os.path.join(root, file), label))
-    
-            print(f"Collected a total of {len(image_paths)} image paths.")
-    
-            # Shuffle image paths
-            random.shuffle(image_paths)
-    
-            # 80% for training, 10% for validation, 10% for testing
-            train_end = int(0.8 * len(image_paths))
-            val_end = int(0.9 * len(image_paths))
-    
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "rows": image_paths[:train_end],
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "rows": image_paths[train_end:val_end],
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "rows": image_paths[val_end:],
-                    },
-                ),
-            ]
-    
-        def _generate_examples(self, rows):
-            def file_to_image(file_path):
-                img = Image.open(file_path)
-                return np.array(img)
-    
-            for id_, (image_file_path, label) in enumerate(rows):
-                image = file_to_image(image_file_path)
-                yield id_, {
-                    'image_file_path': image_file_path,
-                    'image': image,
-                    'labels': label,
-                }
+    def _split_generators(self, dl_manager):
+        # Download and extract images
+        image_paths = []
+        for label, url in _URLS.items():
+            extract_path = dl_manager.download_and_extract(url)
+            print(f"Extracted files for label {label} to path: {extract_path}")
+
+            # Get image paths
+            for root, _, files in os.walk(extract_path):
+                for file in files:
+                    if file.endswith(".jpeg"):  # Assuming all images are .jpeg
+                        image_paths.append((os.path.join(root, file), label))
+
+        print(f"Collected a total of {len(image_paths)} image paths.")
+
+        # Shuffle image paths
+        random.shuffle(image_paths)
+
+        # 80% for training, 10% for validation, 10% for testing
+        train_end = int(0.8 * len(image_paths))
+        val_end = int(0.9 * len(image_paths))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "rows": image_paths[:train_end],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "rows": image_paths[train_end:val_end],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "rows": image_paths[val_end:],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, rows):
+        def file_to_image(file_path):
+            img = Image.open(file_path)
+            return np.array(img)
+
+        for id_, (image_file_path, label) in enumerate(rows):
+            image = file_to_image(image_file_path)
+            yield id_, {
+                'image_file_path': image_file_path,
+                'image': image,
+                'labels': label,
+            }