Update renovation.py

renovation.py

@@ -1,56 +1,37 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Beans leaf dataset with images of diseased and health leaves."""
-
+import csv
+import datasets
+import requests
 import os
 
-import datasets
+from PIL import Image
+from io import BytesIO
 from datasets.tasks import ImageClassification
 
 
-_HOMEPAGE = "https://github.com/AI-Lab-Makerere/ibean/"
+_HOMEPAGE = "https://huggingface.co/datasets/rshrott/renovation"
 
 _CITATION = """\
-@ONLINE {beansdata,
-    author="Makerere AI Lab",
-    title="Bean disease dataset",
-    month="January",
-    year="2020",
-    url="https://github.com/AI-Lab-Makerere/ibean/"
+@ONLINE {renovationquality,
+    author="Your Name",
+    title="Renovation Quality Dataset",
+    month="Your Month",
+    year="Your Year",
+    url="https://huggingface.co/datasets/rshrott/renovation"
 }
 """
 
 _DESCRIPTION = """\
-Beans is a dataset of images of beans taken in the field using smartphone
-cameras. It consists of 3 classes: 2 disease classes and the healthy class.
-Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
-by experts from the National Crops Resources Research Institute (NaCRRI) in
-Uganda and collected by the Makerere AI research lab.
+This dataset contains images of various properties, along with labels indicating the quality of renovation - 'cheap', 'average', 'expensive'.
 """
 
-_URLS = {
-    "train": "https://huggingface.co/datasets/beans/resolve/main/data/train.zip",
-    "validation": "https://huggingface.co/datasets/beans/resolve/main/data/validation.zip",
-    "test": "https://huggingface.co/datasets/beans/resolve/main/data/test.zip",
-}
+_URL = "https://huggingface.co/datasets/rshrott/renovation/raw/main/labels.csv"
 
-_NAMES = ["angular_leaf_spot", "bean_rust", "healthy"]
+_NAMES = ["cheap", "average", "expensive"]
 
+class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
+    """Renovation Quality Dataset."""
 
-class Beans(datasets.GeneratorBasedBuilder):
-    """Beans plant leaf images dataset."""
+    VERSION = datasets.Version("1.0.0")
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -59,44 +40,66 @@ class Beans(datasets.GeneratorBasedBuilder):
                 {
                     "image_file_path": datasets.Value("string"),
                     "image": datasets.Image(),
-                    "labels": datasets.features.ClassLabel(names=_NAMES),
+                    "label": datasets.features.ClassLabel(names=_NAMES),
                 }
             ),
-            supervised_keys=("image", "labels"),
+            supervised_keys=("image", "label"),
             homepage=_HOMEPAGE,
             citation=_CITATION,
-            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+            task_templates=[ImageClassification(image_column="image", label_column="label")],
         )
 
     def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(_URLS)
+        csv_path = dl_manager.download(_URL)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["train"]]),
+                    "filepath": csv_path,
+                    "split": "train",
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["validation"]]),
+                    "filepath": csv_path,
+                    "split": "validation",
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["test"]]),
+                    "filepath": csv_path,
+                    "split": "test",
                 },
             ),
         ]
 
-    def _generate_examples(self, files):
-        for i, path in enumerate(files):
-            file_name = os.path.basename(path)
-            if file_name.endswith(".jpg"):
-                yield i, {
-                    "image_file_path": path,
-                    "image": path,
-                    "labels": os.path.basename(os.path.dirname(path)).lower(),
-                }
+    def _generate_examples(self, filepath, split):
+        def url_to_image(url):
+            response = requests.get(url)
+            img = Image.open(BytesIO(response.content))
+            return img
+    
+        with open(filepath, "r") as f:
+            reader = csv.reader(f)
+            next(reader)  # skip header
+            rows = list(reader)
+            if split == 'train':
+                rows = rows[:int(0.8 * len(rows))]
+            elif split == 'validation':
+                rows = rows[int(0.8 * len(rows)):int(0.9 * len(rows))]
+            else: # test
+                rows = rows[int(0.9 * len(rows)):]
+    
+            for id_, row in enumerate(rows):
+                if len(row) < 2:
+                    print(f"Row with id {id_} has less than 2 elements: {row}")
+                else:
+                    image_file_path = str(row[0])
+                    image = url_to_image(image_file_path)
+                    yield id_, {
+                        'image_file_path': image_file_path,
+                        'image': image,
+                        'label': row[1],
+                    }