fixes #51: Add get_blob_client

fixes #51: fix OEF

Issue #51: Correct typo

app.py

@@ -22,6 +22,7 @@
     CreateDirectoryRequestError,
     ServerError,
     PipelineNotFoundError,
+    ConnectionStringError
 )
 
 load_dotenv()
@@ -65,12 +66,11 @@ async def delete_directory():
     """
     try:
         data = await request.get_json()
-        connection_string: str = os.environ["NACHET_AZURE_STORAGE_CONNECTION_STRING"]
         container_name = data["container_name"]
         folder_name = data["folder_name"]
         if container_name and folder_name:
             container_client = await azure_storage_api.mount_container(
-                connection_string, container_name, create_container=False
+                app.config["BLOB_CLIENT"], container_name, create_container=False
             )
             if container_client:
                 folder_uuid = await azure_storage_api.get_folder_uuid(
@@ -101,11 +101,10 @@ async def list_directories():
     """
     try:
         data = await request.get_json()
-        connection_string: str = os.environ["NACHET_AZURE_STORAGE_CONNECTION_STRING"]
         container_name = data["container_name"]
         if container_name:
             container_client = await azure_storage_api.mount_container(
-                connection_string, container_name, create_container=True
+                app.config["BLOB_CLIENT"], container_name, create_container=True
             )
             response = await azure_storage_api.get_directories(container_client)
             return jsonify(response), 200
@@ -124,12 +123,11 @@ async def create_directory():
     """
     try:
         data = await request.get_json()
-        connection_string: str = os.environ["NACHET_AZURE_STORAGE_CONNECTION_STRING"]
         container_name = data["container_name"]
         folder_name = data["folder_name"]
         if container_name and folder_name:
             container_client = await azure_storage_api.mount_container(
-                connection_string, container_name, create_container=False
+                app.config["BLOB_CLIENT"], container_name, create_container=False
             )
             response = await azure_storage_api.create_folder(
                 container_client, folder_name
@@ -164,6 +162,7 @@ async def inference_request():
         image_base64 = data["image"]
 
         pipelines_endpoints = CACHE.get("pipelines")
+        blob_service_client = app.config.get("BLOB_CLIENT")
 
         if not (folder_name and container_name and imageDims and image_base64):
             raise InferenceRequestError(
@@ -181,7 +180,7 @@ async def inference_request():
         image_bytes = base64.b64decode(encoded_data)
 
         container_client = await azure_storage_api.mount_container(
-            connection_string, container_name, create_container=True
+            blob_service_client, container_name, create_container=True
         )
         hash_value = await azure_storage_api.generate_hash(image_bytes)
         blob_name = await azure_storage_api.upload_image(
@@ -220,14 +219,10 @@ async def inference_request():
         print(f"Took: {'{:10.4f}'.format(time.perf_counter() - seconds)} seconds")
         return jsonify(processed_result_json), 200
 
-    except InferenceRequestError as error:
+    except (KeyError, InferenceRequestError) as error:
         print(error)
         return jsonify(["InferenceRequestError: " + error.args[0]]), 400
 
-    except Exception as error:
-        print(error)
-        return jsonify(["Unexpected error occured"]), 500
-
 
 @app.get("/seed-data/<seed_name>")
 async def get_seed_data(seed_name):
@@ -318,21 +313,21 @@ async def get_pipelines():
     - list: A list of dictionaries representing the pipelines.
     """
     try:
-        # TO DO instantiate Blob Service Client
-        result_json = await azure_storage_api.get_pipeline_info(connection_string, PIPELINE_BLOB_NAME, PIPELINE_VERSION)
+        app.config["BLOB_CLIENT"] = await azure_storage_api.get_blob_client(connection_string)
+        result_json = await azure_storage_api.get_pipeline_info(app.config["BLOB_CLIENT"], PIPELINE_BLOB_NAME, PIPELINE_VERSION)
         cipher_suite = Fernet(FERNET_KEY)
-    except PipelineNotFoundError as error:
+    except (ConnectionStringError, PipelineNotFoundError) as error:
         print(error)
-        raise ServerError("Server errror: Pipelines were not found") from error
+        raise ServerError("server errror: could not retrieve the pipelines") from error
 
 
     models = ()
     for model in result_json.get("models"):
         m = Model(
             request_function.get(model.get("api_call_function")),
             model.get("model_name"),
-            # To protect sensible data (API key and model endpoint), we crypt them when
-            # they are pushed into the blob storage. Once we retrieve the data here in the
+            # To protect sensible data (API key and model endpoint), we encrypt it when
+            # it's pushed into the blob storage. Once we retrieve the data here in the
             # backend, we need to decrypt the byte format to recover the original
             # data.
             cipher_suite.decrypt(model.get("endpoint").encode()).decode(),

azure_storage/azure_storage_api.py

@@ -39,37 +39,49 @@ async def generate_hash(image):
     except GenerateHashError as error:
         print(error)
 
+async def get_blob_client(connection_string: str):
+    """
+    given a connection string, returns the blob client object
+    """
+    try:
+        blob_service_client = BlobServiceClient.from_connection_string(
+            connection_string
+        )
+        if blob_service_client is None:
+            raise ValueError(f"the given connection string is invalid: {connection_string}")
+        return blob_service_client
+
+    except ValueError as error:
+        print(error)
+        raise ConnectionStringError(error.args[0]) from error
 
-async def mount_container(connection_string, container_uuid, create_container=True):
+
+async def mount_container(
+        blob_service_client: BlobServiceClient,
+        container_uuid: str,
+        create_container: bool =True):
     """
-    given a connection string and a container name, mounts the container and
+    given a connection string and a container uuid, mounts the container and
     returns the container client as an object that can be used in other
     functions. if a specified container doesnt exist, it creates one with the
     provided uuid, if create_container is True
     """
     try:
-        blob_service_client = BlobServiceClient.from_connection_string(
-            connection_string
-        )
-        if blob_service_client:
-            container_name = "user-{}".format(container_uuid)
-            container_client = blob_service_client.get_container_client(container_name)
-            if container_client.exists():
+        container_name = "user-{}".format(container_uuid)
+        container_client = blob_service_client.get_container_client(container_name)
+        if container_client.exists():
+            return container_client
+        elif create_container and not container_client.exists():
+            container_client = blob_service_client.create_container(container_name)
+            # create general directory for new user container
+            response = await create_folder(container_client, "General")
+            if response:
                 return container_client
-            elif create_container and not container_client.exists():
-                container_client = blob_service_client.create_container(container_name)
-                # create general directory for new user container
-                response = await create_folder(container_client, "General")
-                if response:
-                    return container_client
-                else:
-                    return False
-        else:
-            raise ConnectionStringError("Invalid connection string")
-
+            else:
+                raise MountContainerError(f"could not create general directory: {container_name}")
     except MountContainerError as error:
         print(error)
-        return False
+        raise
 
 
 async def get_blob(container_client: ContainerClient, blob_name: str):
@@ -237,7 +249,7 @@ async def get_directories(container_client):
         return []
 
 async def get_pipeline_info(
-        connection_string: str,
+        blob_service_client: BlobServiceClient,
         pipeline_container_name: str,
         pipeline_version: str
     ) -> json:
@@ -246,10 +258,10 @@ async def get_pipeline_info(
     provided parameters.
 
     Args:
-        connection_string (str): The connection string for the Azure Blob
-        Storage. pipeline_container_name (str): The name of the container where
-        the pipeline files are stored. pipeline_version (str): The version of
-        the pipeline to retrieve.
+        blob_service_client (BlobServiceClient): The BlobServiceClient object
+        pipeline_container_name (str): The name of the container where
+        the pipeline files are stored.
+        pipeline_version (str): The version of the pipeline to retrieve.
 
     Returns:
         json: The pipeline information in JSON format.
@@ -259,13 +271,6 @@ async def get_pipeline_info(
         found.
     """
     try:
-        blob_service_client = BlobServiceClient.from_connection_string(
-            connection_string
-        )
-
-        if blob_service_client is None:
-            raise PipelineNotFoundError("No Blob Service Client found with the connection string.")
-
         container_client = blob_service_client.get_container_client(
             pipeline_container_name
         )
@@ -274,5 +279,5 @@ async def get_pipeline_info(
         pipeline = json.loads(blob)
         return pipeline
 
-    except (ValueError, GetBlobError, PipelineNotFoundError) as error:
+    except GetBlobError as error:
         raise PipelineNotFoundError(f"This version {pipeline_version} was not found") from error

model/inference.py

@@ -1,6 +1,6 @@
 
 """
-This file contain the generic inference function that process the data at the end
+This file contains the generic inference function that processes the data at the end
 of a given pipeline.
 """
 

model/seed_detector.py

@@ -1,5 +1,5 @@
 """
-This file contain the function to request the inference and process the data from
+This file contains the function that requests the inference and processes the data from
 the seed detector model.
 """
 
@@ -59,7 +59,7 @@ def process_image_slicing(image_bytes: bytes, result_json: dict) -> list:
 
 async def request_inference_from_seed_detector(model: namedtuple, previous_result: str):
     """
-    Requests inference from the seed detector model using the provided previous result.
+    Requests inference from the seed detector model using the previously provided result.
 
     Args:
         model (namedtuple): The seed detector model.

model/six_seeds.py

@@ -1,5 +1,5 @@
 """
-This file contain the function to request the inference and process the data from
+This file contains the function that requests the inference and processes the data from
 the nachet-6seeds model.
 """
 
@@ -48,4 +48,4 @@ async def request_inference_from_nachet_6seeds(model: namedtuple, previous_resul
         return result_object
 
     except HTTPError as e:
-        raise InferenceRequestError(f"An error occurred while processing the request:\n {str(e)}") from None
+        raise InferenceRequestError(f"An error occurred while processing the request:\n {str(e)}") from None

model/swin.py

@@ -1,6 +1,6 @@
 """
-This file contain the function to request the inference and process the data from
-the nachet-6seeds model.
+This file contains the function that requests the inference and processes the data from
+the swin model.
 """
 
 import json
@@ -59,4 +59,4 @@ async def request_inference_from_swin(model: namedtuple, previous_result: list[b
 
         return process_swin_result(previous_result.get("result_json"), results)
     except HTTPError as e:
-       raise InferenceRequestError(f"An error occurred while processing the request:\n {str(e)}") from None
+       raise InferenceRequestError(f"An error occurred while processing the request:\n {str(e)}") from None