wip huggingface register

llama_stack/providers/inline/huggingface/datasetio/huggingface.py

@@ -7,13 +7,27 @@ from typing import List, Optional
 
 from llama_stack.apis.datasetio import *  # noqa: F403
 
-from datasets import load_dataset
+
+from datasets import Dataset, load_dataset
 from llama_stack.providers.datatypes import DatasetsProtocolPrivate
 
 from .config import HuggingfaceDatasetIOConfig
 from .dataset_defs.llamastack_mmlu import llamastack_mmlu
 
 
+def load_hf_dataset(dataset_def: DatasetDef):
+    if dataset_def.metadata.get("dataset_path", None):
+        return load_dataset(**dataset_def.metadata)
+
+    df = get_dataframe_from_url(dataset_def.url)
+
+    if df is None:
+        raise ValueError(f"Failed to load dataset from {dataset_def.url}")
+
+    dataset = Dataset.from_pandas(df)
+    return dataset
+
+
 class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
     def __init__(self, config: HuggingfaceDatasetIOConfig) -> None:
         self.config = config
@@ -31,6 +45,8 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
         self,
         dataset_def: DatasetDef,
     ) -> None:
+
+        print("registering dataset", dataset_def)
         self.dataset_infos[dataset_def.identifier] = dataset_def
 
     async def list_datasets(self) -> List[DatasetDef]:
@@ -44,7 +60,7 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
         filter_condition: Optional[str] = None,
     ) -> PaginatedRowsResult:
         dataset_def = self.dataset_infos[dataset_id]
-        loaded_dataset = load_dataset(**dataset_def.metadata)
+        loaded_dataset = load_hf_dataset(dataset_def)
 
         if page_token and not page_token.isnumeric():
             raise ValueError("Invalid page_token")

llama_stack/providers/tests/eval/test_eval.py

@@ -44,11 +44,11 @@ class Testeval:
         provider = datasetio_impl.routing_table.get_provider_impl(
             "test_dataset_for_eval"
         )
-        if provider.__provider_spec__.provider_type != "meta-reference":
+        # if provider.__provider_spec__.provider_type != "meta-reference":
-            pytest.skip("Only meta-reference provider supports registering datasets")
+        #     pytest.skip("Only meta-reference provider supports registering datasets")
 
         response = await datasets_impl.list_datasets()
-        assert len(response) == 1
+        assert len(response) >= 1
         rows = await datasetio_impl.get_rows_paginated(
             dataset_id="test_dataset_for_eval",
             rows_in_page=3,

llama_stack/providers/utils/datasetio/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/utils/datasetio/url_utils.py

@@ -0,0 +1,26 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import base64
+import mimetypes
+import os
+
+from llama_models.llama3.api.datatypes import URL
+
+
+def data_url_from_file(file_path: str) -> URL:
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}")
+
+    with open(file_path, "rb") as file:
+        file_content = file.read()
+
+    base64_content = base64.b64encode(file_content).decode("utf-8")
+    mime_type, _ = mimetypes.guess_type(file_path)
+
+    data_url = f"data:{mime_type};base64,{base64_content}"
+
+    return URL(uri=data_url)

llama_stack/providers/utils/memory/file_utils.py

@@ -5,22 +5,41 @@
 # the root directory of this source tree.
 
 import base64
-import mimetypes
+import io
-import os
+from urllib.parse import unquote
+
+import pandas
 
 from llama_models.llama3.api.datatypes import URL
 
+from llama_stack.providers.utils.memory.vector_store import parse_data_url
 
-def data_url_from_file(file_path: str) -> URL:
-    if not os.path.exists(file_path):
-        raise FileNotFoundError(f"File not found: {file_path}")
 
-    with open(file_path, "rb") as file:
+def get_dataframe_from_url(url: URL):
-        file_content = file.read()
+    df = None
+    if url.uri.endswith(".csv"):
+        df = pandas.read_csv(url.uri)
+    elif url.uri.endswith(".xlsx"):
+        df = pandas.read_excel(url.uri)
+    elif url.uri.startswith("data:"):
+        parts = parse_data_url(url.uri)
+        data = parts["data"]
+        if parts["is_base64"]:
+            data = base64.b64decode(data)
+        else:
+            data = unquote(data)
+            encoding = parts["encoding"] or "utf-8"
+            data = data.encode(encoding)
 
-    base64_content = base64.b64encode(file_content).decode("utf-8")
+        mime_type = parts["mimetype"]
-    mime_type, _ = mimetypes.guess_type(file_path)
+        mime_category = mime_type.split("/")[0]
+        data_bytes = io.BytesIO(data)
 
-    data_url = f"data:{mime_type};base64,{base64_content}"
+        if mime_category == "text":
+            df = pandas.read_csv(data_bytes)
+        else:
+            df = pandas.read_excel(data_bytes)
+    else:
+        raise ValueError(f"Unsupported file type: {url}")
 
-    return URL(uri=data_url)
+    return df