Create new file

snowflaketest.py

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+"""A dataset script that will hit Snowflake DB and return the results."""
+
+try:
+    import snowflake.connector as connector
+except:
+    ModuleNotFoundError("Please install snowflake connector: pip install snowflake-connector-python")
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        conn = connector.connect(
+            user='rajiv',
+            password='Password123!',
+            account='VUA92284',
+            warehouse='RAJIV',
+            database='HUGGINGFACE',
+            schema='PUBLIC',
+            role = 'RAJIV'
+        )
+        
+        curr = conn.cursor()
+        
+        
+        sql = "select * from EMOTION"
+        curr = curr.execute(sql)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"cursor": curr},
+            )
+        ]
+    
+    def _generate_examples(self, cursor):
+        for i, ex in enumerate(cursor):
+            yield str(i), {
+                "text": ex[0],
+                "label": ex[1],
+            }
+        # Probably not necessary but just in case...we close the connection which we can find within the cursor object
+        cursor.connection.close()