Update pidray-semantics.py

pidray-semantics.py

@@ -1,3 +1,4 @@
+
 import json
 
 import datasets
@@ -7,25 +8,23 @@ from datasets.tasks import QuestionAnsweringExtractive
 logger = datasets.logging.get_logger(__name__)
 
 _URL = "https://huggingface.co/datasets/jaradat/pidray-semantics/resolve/main/pixel_values.tar.gz"
-descriptions = ['aaa',
-                'bbb',
-                'ccc',
-                'ddd',
-                'eee' ]
+_URL2 = "https://huggingface.co/datasets/jaradat/pidray-semantics/resolve/main/label.tar.gz"
+
 
 
-class PIDrayTargz(datasets.GeneratorBasedBuilder):
+class pidraySemantics(datasets.GeneratorBasedBuilder):
 
     def _info(self):
         return datasets.DatasetInfo(
             features=datasets.Features(
                 {
-                    "text": datasets.Value("string"),
-                    "image": datasets.Image(),
+                                                      
+                    "pixel_values": datasets.Image(),
+                    "label": datasets.Image(),
                 }
             ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
+                                                                          
+                                    
             supervised_keys=None,
             homepage="https://huggingface.co/datasets/jaradat/pidray-semantics",
 
@@ -33,28 +32,39 @@ class PIDrayTargz(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         path = dl_manager.download(_URL)
-        image_iters = dl_manager.iter_archive(path)        
+        image_iters = dl_manager.iter_archive(path)
 
+        
+        path2 = dl_manager.download(_URL2)
+        label_file = dl_manager.extract(path2)
+        with open(label_file, "r") as f:
+            label_data = json.load(f)
+        
         return [
             datasets.SplitGenerator(
-            name=datasets.Split.TRAIN, 
-            gen_kwargs={
-                "images": image_iters,
+                name=datasets.Split.TRAIN, 
+                gen_kwargs={
+                    "images": image_iters,
+                    "label_data": label_data,
                 }
-            ),
+            )
             
         ]
 
-    def _generate_examples(self, images):
-        """This function returns the examples in the raw (text) form."""
+    def _generate_examples(self, images, label_data):
+                                                                        
         idx = 0
-        # iterate through images
+                                
         for filepath, image in images:
+            label_info = label_data[idx]
+            label_filepath = label_info["filepath"]
+            with open(label_filepath, "rb") as f:
+                label_image = f.read()
             
+                                 
             yield idx, {
-                "image": {"path": filepath, "bytes": image.read()},
-                "text": descriptions[idx],
+                "pixel_values": {"path": filepath, "bytes": image.read()},
+                "label": {"path": label_filepath, "bytes": label_image},
             }
+
             idx += 1
-            if idx == 4:
-                break