refactor(generative_ai): Reduced hidden variables and refactored code (GoogleCloudPlatform#12231)

Thoughtseize1 · web-flow · commit c11ebad50737 · 2024-08-14T14:03:47.000+02:00
* Reduced hidden variables and refactored code
diff --git a/generative_ai/chat_completions/chat_completions_credentials_refresher.py b/generative_ai/chat_completions/chat_completions_credentials_refresher.py
@@ -22,6 +22,7 @@
 import google.auth.transport.requests
 import openai
 
+
 class OpenAICredentialsRefresher:
     def __init__(self, **kwargs: Any) -> None:
         # Set a dummy key here
@@ -40,6 +41,8 @@ def __getattr__(self, name: str) -> Any:
 
             self.client.api_key = self.creds.token
         return getattr(self.client, name)
+
+
 # [END generativeaionvertexai_credentials_refresher_class]
 
 
diff --git a/generative_ai/ideation.py b/generative_ai/ideation.py
@@ -39,8 +39,10 @@ def interview() -> str:
         **parameters,
     )
     print(f"Response from Model: {response.text}")
+    # [END generativeaionvertexai_sdk_ideation]
 
     return response.text
 
 
-# [END generativeaionvertexai_sdk_ideation]
+if __name__ == "__main__":
+    interview()
diff --git a/generative_ai/inference/non_stream_multimodality_basic.py b/generative_ai/inference/non_stream_multimodality_basic.py
@@ -40,7 +40,11 @@ def generate_content() -> object:
         ]
     )
 
-    print(response)
+    print(response.text)
     # [END generativeaionvertexai_non_stream_multimodality_basic]
 
     return response
+
+
+if __name__ == "__main__":
+    generate_content()
diff --git a/generative_ai/inference/non_stream_text_basic.py b/generative_ai/inference/non_stream_text_basic.py
@@ -29,7 +29,11 @@ def generate_content() -> object:
     model = GenerativeModel(MODEL_ID)
     response = model.generate_content("Write a story about a magic backpack.")
 
-    print(response)
+    print(response.text)
     # [END generativeaionvertexai_non_stream_text_basic]
 
     return response
+
+
+if __name__ == "__main__":
+    generate_content()
diff --git a/generative_ai/inference/stream_text_basic.py b/generative_ai/inference/stream_text_basic.py
@@ -32,7 +32,11 @@ def generate_content() -> object:
     )
 
     for response in responses:
-        print(response)
+        print(response.text)
     # [END generativeaionvertexai_stream_text_basic]
 
     return responses
+
+
+if __name__ == "__main__":
+    generate_content()
diff --git a/generative_ai/multimodal_embedding_image_video_text.py b/generative_ai/multimodal_embedding_image_video_text.py
@@ -41,12 +41,12 @@ def get_image_video_text_embeddings() -> MultiModalEmbeddingResponse:
     video = Video.load_from_file(
         "gs://cloud-samples-data/vertex-ai-vision/highway_vehicles.mp4"
     )
-    contextual_text = "Cars on Highway"
+
     embeddings = model.get_embeddings(
         image=image,
         video=video,
         video_segment_config=VideoSegmentConfig(end_offset_sec=1),
-        contextual_text=contextual_text,
+        contextual_text="Cars on Highway",
     )
 
     print(f"Image Embedding: {embeddings.image_embedding}")
diff --git a/generative_ai/multimodal_embedding_video.py b/generative_ai/multimodal_embedding_video.py
@@ -34,15 +34,13 @@ def get_video_embeddings() -> MultiModalEmbeddingResponse:
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
     model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")
-    video_path = "gs://cloud-samples-data/vertex-ai-vision/highway_vehicles.mp4"
-    contextual_text = "Cars on Highway"
-    video = Video.load_from_file(video_path)
-    video_segment_config = VideoSegmentConfig(end_offset_sec=1)
 
     embeddings = model.get_embeddings(
-        video=video,
-        video_segment_config=video_segment_config,
-        contextual_text=contextual_text,
+        video=Video.load_from_file(
+            "gs://cloud-samples-data/vertex-ai-vision/highway_vehicles.mp4"
+        ),
+        video_segment_config=VideoSegmentConfig(end_offset_sec=1),
+        contextual_text="Cars on Highway",
     )
 
     # Video Embeddings are segmented based on the video_segment_config.
diff --git a/generative_ai/tune_code_generation_model.py b/generative_ai/tune_code_generation_model.py
@@ -15,22 +15,15 @@
 # [START generativeaionvertexai_sdk_tune_code_generation_model]
 from __future__ import annotations
 
-import os
-
-PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
-
 
 def tune_code_generation_model(project_id: str) -> None:
     # [START generativeaionvertexai_tune_code_generation_model]
-    from google.auth import default
-
     import vertexai
     from vertexai.language_models import CodeGenerationModel
 
     # Initialize Vertex AI
-    # TODO(developer): Update project
-    credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
-    vertexai.init(project=PROJECT_ID, location="us-central1", credentials=credentials)
+    # TODO(developer): Update project_id
+    vertexai.init(project=project_id, location="us-central1")
 
     model = CodeGenerationModel.from_pretrained("code-bison@002")
 
@@ -47,3 +40,9 @@ def tune_code_generation_model(project_id: str) -> None:
 
 
 # [END generativeaionvertexai_sdk_tune_code_generation_model]
+
+if __name__ == "__main__":
+    import google.auth
+
+    PROJECT_ID = google.auth.default()[1]
+    tune_code_generation_model(PROJECT_ID)
diff --git a/generative_ai/tune_code_generation_model_test.py b/generative_ai/tune_code_generation_model_test.py
@@ -11,13 +11,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import os
 
 from google.cloud import aiplatform
 import pytest
 from vertexai.language_models import TextGenerationModel
 
 import tune_code_generation_model
 
+PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
+
 
 def teardown_model(tuned_model: TextGenerationModel) -> None:
     for tuned_model_name in tuned_model.list_tuned_model_names():
@@ -35,7 +38,7 @@ def teardown_model(tuned_model: TextGenerationModel) -> None:
 @pytest.mark.skip("Blocked on b/277959219")
 def test_tuning_code_generation_model() -> None:
     """Takes approx. 20 minutes."""
-    tuned_model = tune_code_generation_model.tune_code_generation_model()
+    tuned_model = tune_code_generation_model.tune_code_generation_model(PROJECT_ID)
     try:
         assert tuned_model
     finally:
diff --git a/generative_ai/tuning.py b/generative_ai/tuning.py
@@ -20,9 +20,7 @@
 PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
 
 
-def tuning(
-    project_id: str,
-) -> None:
+def tuning() -> None:
     # [START generativeaionvertexai_tuning]
     import vertexai
     from vertexai.language_models import TextGenerationModel

Original file line number	Diff line number	Diff line change
`@@ -39,8 +39,10 @@ def interview() -> str:`
`39`	`39`	`**parameters,`
`40`	`40`	`)`
`41`	`41`	`print(f"Response from Model: {response.text}")`
	`42`	`+ # [END generativeaionvertexai_sdk_ideation]`
`42`	`43`
`43`	`44`	`return response.text`
`44`	`45`
`45`	`46`
`46`		`-# [END generativeaionvertexai_sdk_ideation]`
	`47`	`+if __name__ == "__main__":`
	`48`	`+ interview()`
Original file line number	Diff line number	Diff line change
`@@ -40,7 +40,11 @@ def generate_content() -> object:`
`40`	`40`	`]`
`41`	`41`	`)`
`42`	`42`
`43`		`- print(response)`
	`43`	`+ print(response.text)`
`44`	`44`	`# [END generativeaionvertexai_non_stream_multimodality_basic]`
`45`	`45`
`46`	`46`	`return response`
	`47`	`+`
	`48`	`+`
	`49`	`+if __name__ == "__main__":`
	`50`	`+ generate_content()`
Original file line number	Diff line number	Diff line change
`@@ -41,12 +41,12 @@ def get_image_video_text_embeddings() -> MultiModalEmbeddingResponse:`
`41`	`41`	`video = Video.load_from_file(`
`42`	`42`	`"gs://cloud-samples-data/vertex-ai-vision/highway_vehicles.mp4"`
`43`	`43`	`)`
`44`		`- contextual_text = "Cars on Highway"`
	`44`	`+`
`45`	`45`	`embeddings = model.get_embeddings(`
`46`	`46`	`image=image,`
`47`	`47`	`video=video,`
`48`	`48`	`video_segment_config=VideoSegmentConfig(end_offset_sec=1),`
`49`		`- contextual_text=contextual_text,`
	`49`	`+ contextual_text="Cars on Highway",`
`50`	`50`	`)`
`51`	`51`
`52`	`52`	`print(f"Image Embedding: {embeddings.image_embedding}")`