refactor(generative-ai): style update (#11619)

generative_ai/gemini_chat_example.py

@@ -13,18 +13,19 @@
 # limitations under the License.
 
 
-def chat_text_example(project_id: str, location: str) -> str:
+def chat_text_example(project_id: str) -> str:
     # [START generativeaionvertexai_gemini_multiturn_chat]
     import vertexai
 
     from vertexai.generative_models import GenerativeModel, ChatSession
 
     # TODO(developer): Update and un-comment below lines
     # project_id = "PROJECT_ID"
-    # location = "us-central1"
-    vertexai.init(project=project_id, location=location)
+
+    vertexai.init(project=project_id, location="us-central1")
 
     model = GenerativeModel(model_name="gemini-1.0-pro-002")
+
     chat = model.start_chat()
 
     def get_chat_response(chat: ChatSession, prompt: str) -> str:
@@ -43,17 +44,19 @@ def get_chat_response(chat: ChatSession, prompt: str) -> str:
     return get_chat_response(chat, "Hello")
 
 
-def chat_stream_example(project_id: str, location: str) -> str:
+def chat_stream_example(project_id: str) -> str:
     # [START generativeaionvertexai_gemini_multiturn_chat_stream]
     import vertexai
 
     from vertexai.generative_models import GenerativeModel, ChatSession
 
-    # TODO(developer): Update and un-comment below lines
+    # TODO(developer): Update and un-comment below line
     # project_id = "PROJECT_ID"
-    # location = "us-central1"
-    vertexai.init(project=project_id, location=location)
+
+    vertexai.init(project=project_id, location="us-central1")
+
     model = GenerativeModel(model_name="gemini-1.0-pro-002")
+
     chat = model.start_chat()
 
     def get_chat_response(chat: ChatSession, prompt: str) -> str:

generative_ai/gemini_count_token_example.py

@@ -13,25 +13,21 @@
 # limitations under the License.
 
 
-def generate_text(project_id: str, location: str) -> str:
+def count_tokens(project_id: str) -> str:
     # [START generativeaionvertexai_gemini_token_count]
     import vertexai
 
     from vertexai.generative_models import GenerativeModel
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
 
-    # Load the model
-    model = GenerativeModel(model_name="gemini-1.0-pro-002")
+    vertexai.init(project=project_id, location="us-central1")
 
-    # prompt tokens count
-    print(model.count_tokens("why is sky blue?"))
+    model = GenerativeModel(model_name="gemini-1.0-pro-002")
 
-    # Load example images
-    response = model.generate_content("why is sky blue?")
+    response = model.count_tokens("Why is sky blue?")
+    print(response)
 
-    # response tokens count
-    print(response._raw_response.usage_metadata)
     # [END generativeaionvertexai_gemini_token_count]
-    return response.text
+    return response

generative_ai/gemini_grounding_example.py

@@ -16,18 +16,18 @@
 from vertexai.preview.generative_models import GenerationResponse
 
 
-def generate_text_with_grounding_web(
-    project_id: str, location: str
-) -> GenerationResponse:
+def generate_text_with_grounding_web(project_id: str) -> GenerationResponse:
     # [START generativeaionvertexai_gemini_grounding_with_web]
     import vertexai
+
     from vertexai.preview.generative_models import grounding
     from vertexai.generative_models import GenerationConfig, GenerativeModel, Tool
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
+
+    vertexai.init(project=project_id, location="us-central1")
 
-    # Load the model
     model = GenerativeModel(model_name="gemini-1.0-pro-002")
 
     # Use Google Search for grounding
@@ -49,18 +49,19 @@ def generate_text_with_grounding_web(
 
 
 def generate_text_with_grounding_vertex_ai_search(
-    project_id: str, location: str, data_store_path: str
+    project_id: str, data_store_path: str
 ) -> GenerationResponse:
     # [START generativeaionvertexai_gemini_grounding_with_vais]
     import vertexai
 
     from vertexai.preview.generative_models import grounding
     from vertexai.generative_models import GenerationConfig, GenerativeModel, Tool
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
+
+    vertexai.init(project=project_id, location="us-central1")
 
-    # Load the model
     model = GenerativeModel(model_name="gemini-1.0-pro-002")
 
     # Use Vertex AI Search data store

generative_ai/gemini_guide_example.py

@@ -13,29 +13,30 @@
 # limitations under the License.
 
 
-def generate_text(project_id: str, location: str) -> str:
+def generate_text(project_id: str) -> str:
     # [START generativeaionvertexai_gemini_get_started]
-
     import vertexai
+
     from vertexai.generative_models import GenerativeModel, Part
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
-    # Load the model
-    multimodal_model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
-    # Query the model
-    response = multimodal_model.generate_content(
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
+
+    vertexai.init(project=project_id, location="us-central1")
+
+    model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
+
+    response = model.generate_content(
         [
-            # Add an example image
             Part.from_uri(
-                "gs://generativeai-downloads/images/scones.jpg", mime_type="image/jpeg"
+                "gs://cloud-samples-data/generative-ai/image/scones.jpg",
+                mime_type="image/jpeg",
             ),
-            # Add an example query
-            "what is shown in this image?",
+            "What is shown in this image?",
         ]
     )
-    print(response)
-    return response.text
 
+    print(response.text)
+    # [END generativeaionvertexai_gemini_get_started]
 
-# [END generativeaionvertexai_gemini_get_started]
+    return response.text

generative_ai/gemini_multi_image_example.py

@@ -13,47 +13,42 @@
 # limitations under the License.
 
 
-def generate_text_multimodal(project_id: str, location: str) -> str:
+def generate_text_multimodal(project_id: str) -> str:
     # [START generativeaionvertexai_gemini_single_turn_multi_image]
-    import http.client
-    import typing
-    import urllib.request
     import vertexai
 
-    from vertexai.generative_models import GenerativeModel, Image
+    from vertexai.generative_models import GenerativeModel, Part
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
 
-    # create helper function
-    def load_image_from_url(image_url: str) -> Image:
-        with urllib.request.urlopen(image_url) as response:
-            response = typing.cast(http.client.HTTPResponse, response)
-            image_bytes = response.read()
-        return Image.from_bytes(image_bytes)
+    vertexai.init(project=project_id, location="us-central1")
 
     # Load images from Cloud Storage URI
-    landmark1 = load_image_from_url(
-        "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+    image_file1 = Part.from_uri(
+        "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png",
+        mime_type="image/png",
     )
-    landmark2 = load_image_from_url(
-        "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png"
+    image_file2 = Part.from_uri(
+        "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark2.png",
+        mime_type="image/png",
     )
-    landmark3 = load_image_from_url(
-        "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png"
+    image_file3 = Part.from_uri(
+        "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark3.png",
+        mime_type="image/png",
     )
 
-    # Pass multimodal prompt
     model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
     response = model.generate_content(
         [
-            landmark1,
+            image_file1,
             "city: Rome, Landmark: the Colosseum",
-            landmark2,
+            image_file2,
             "city: Beijing, Landmark: Forbidden City",
-            landmark3,
+            image_file3,
         ]
     )
-    print(response)
+    print(response.text)
+
     # [END generativeaionvertexai_gemini_single_turn_multi_image]
     return response.text

generative_ai/gemini_pro_basic_example.py

@@ -13,25 +13,26 @@
 # limitations under the License.
 
 
-def generate_text(project_id: str, location: str) -> None:
+def generate_text(project_id: str) -> None:
     # [START generativeaionvertexai_gemini_pro_example]
     import vertexai
 
     from vertexai.generative_models import GenerativeModel, Part
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
+
+    vertexai.init(project=project_id, location="us-central1")
 
-    # Load the model
     model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
 
-    # Load example image
-    image_url = "gs://generativeai-downloads/images/scones.jpg"
-    image_content = Part.from_uri(image_url, "image/jpeg")
+    image_file = Part.from_uri(
+        "gs://cloud-samples-data/generative-ai/image/scones.jpg", "image/jpeg"
+    )
 
     # Query the model
-    response = model.generate_content([image_content, "what is this image?"])
-    print(response)
+    response = model.generate_content([image_file, "what is this image?"])
+    print(response.text)
 
     # [END generativeaionvertexai_gemini_pro_example]
     return response.text

generative_ai/gemini_pro_config_example.py

@@ -13,17 +13,18 @@
 # limitations under the License.
 
 
-def generate_text(project_id: str, location: str) -> None:
+def generate_text(project_id: str) -> None:
     # [START generativeaionvertexai_gemini_pro_config_example]
     import base64
     import vertexai
 
     from vertexai.generative_models import GenerationConfig, GenerativeModel, Part
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
+
+    vertexai.init(project=project_id, location="us-central1")
 
-    # Load the model
     model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
 
     # Load example image from local storage

generative_ai/gemini_safety_config_example.py

@@ -15,16 +15,17 @@
 from vertexai.generative_models import Part
 
 
-def generate_text(project_id: str, location: str, image: Part) -> str:
+def generate_text(project_id: str) -> str:
     # [START generativeaionvertexai_gemini_safety_settings]
     import vertexai
 
     from vertexai import generative_models
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
+
+    vertexai.init(project=project_id, location="us-central1")
 
-    # Load the model
     model = generative_models.GenerativeModel(model_name="gemini-1.0-pro-vision-001")
 
     # Generation config
@@ -44,17 +45,22 @@ def generate_text(project_id: str, location: str, image: Part) -> str:
         ),
     ]
 
+    image_file = Part.from_uri(
+        "gs://cloud-samples-data/generative-ai/image/scones.jpg", "image/jpeg"
+    )
+
     # Generate content
     responses = model.generate_content(
-        [image, "Add your prompt here"],
+        [image_file, "What is in this image?"],
         generation_config=generation_config,
-        stream=True,
         safety_settings=safety_config,
+        stream=True,
     )
 
     text_responses = []
     for response in responses:
         print(response.text)
         text_responses.append(response.text)
     # [END generativeaionvertexai_gemini_safety_settings]
+
     return "".join(text_responses)

generative_ai/gemini_single_turn_video_example.py

@@ -13,16 +13,19 @@
 # limitations under the License.
 
 
-def generate_text(project_id: str, location: str) -> str:
+def generate_text(project_id: str) -> str:
     # [START generativeaionvertexai_gemini_single_turn_video]
     import vertexai
 
     from vertexai.generative_models import GenerativeModel, Part
 
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
-    # Load the model
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
+
+    vertexai.init(project=project_id, location="us-central1")
+
     vision_model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
+
     # Generate text
     response = vision_model.generate_content(
         [
@@ -32,7 +35,7 @@ def generate_text(project_id: str, location: str) -> str:
             "What is in the video?",
         ]
     )
-    print(response)
+    print(response.text)
     # [END generativeaionvertexai_gemini_single_turn_video]
 
     return response.text