Upload CommyTesting.py

CommyTesting.py

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+import csv
+import json
+import os
+import pdb
+import datasets
+import pandas as pd
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Dataset for commy test eye diabetic
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "NawinCom/CommyTesting"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URL = "https://huggingface.co/datasets/NawinCom/CommyTesting/blob/main/images.zip"
+# classes = [0,0]
+# create class
+train = pd.read_csv('Train.csv')
+lis1 = train['id_code']
+lis2 = train['diagnosis']
+dic = dict(zip(lis1, lis2))
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class ImagesDemo(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "image":  datasets.Image(),
+                    "label": datasets.Value("string"),
+                }
+            ),  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+
+        data_dir = dl_manager.download(_URL)
+        image_iters = dl_manager.iter_archive(data_dir)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "images": image_iters
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, images):
+        idx = 0
+        for filepath, image in images:
+            print(filepath)
+            check = filepath.split('/')[-1].replace('.jpg', '')
+            yield idx, {
+                "image" : {"path": filepath, "bytes": image.read()},
+                "label" : dic[check]
+            }
+            idx+=1