add food vision script

food_vision_199_classes.py

@@ -14,6 +14,10 @@ import pandas as pd
 
 from datasets.tasks import ImageClassification
 
+# Set verbosity to 10
+datasets.logging.set_verbosity(10)
+print(f"Verbosity level: {datasets.logging.get_verbosity()}")
+
 _HOMEPAGE = "https://www.nutrify.app"
 _LICENSE = "TODO"
 _CITATION = "TODO"
@@ -250,34 +254,41 @@ class Food199(datasets.GeneratorBasedBuilder):
         """
         This function returns the logic to split the dataset into different splits as well as labels.
         """
-        csv = dl_manager.download("annotations_with_links.csv")
-        df = pd.read_csv(csv, low_memory=False)
-        # print("Downloaded annotations.csv")
-        df_train_annotations = df[["image", "label"]][df["split"] == "train"].to_dict(orient="records")
-        # print(df_train_annotations[:5])
-        df_test_annotations = df[["image", "label"]][df["split"] == "test"].to_dict(orient="records")
+        annotations_csv = dl_manager.download("annotations_with_links.csv")
+        print(annotations_csv)
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "annotations": df_train_annotations,
-                }),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TEST,
-            #     gen_kwargs={
-            #         "annotations": df_test_annotations,
-            #     })
-                ]
-
+                    "annotations": annotations_csv,
+                    "split": "train"
+                }
+            ),
+        #     datasets.SplitGenerator(
+        #         name=datasets.Split.TEST,
+        #         gen_kwargs={
+        #             "annotations": annotations_csv,
+        #             "split": "test"
+        #         }
+        #     )
+        ]
 
-    def _generate_examples(self, annotations):
+    def _generate_examples(self, annotations, split):
         """
         This function takes in the kwargs from the _split_generators method and can then yield information from them.
         """
+        annotations_df = pd.read_csv(annotations, low_memory=False)
+
+        if split == "train":
+            annotations = annotations_df[["image", "label"]][annotations_df["split"] == "train"].to_dict(orient="records")
+        elif split == "test":
+            annotations = annotations_df[["image", "label"]][annotations_df["split"] == "test"].to_dict(orient="records")
+
         for id_, row in enumerate(annotations):
+            # print(row["image"])
             row["image"] = str(row.pop("image"))
             row["label"] = row.pop("label")
-            print(id_, row)
+            # print(id_, row)
             yield id_, row