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Commit 74bcfbd

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msampathkumarmsampathkumar
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feat(generative-ai): Add code samples for Gemini models (GoogleCloudPlatform#11016)
* feat(generative_ai): Add code samples for Gemini models * feat(generative_ai): Add Gemini sample for step by step user Guide * feat(generative_ai): Add Gemini sample for step by step user Guide * feat(generative_ai): Add Gemini sample for step by step user Guide * feat(generative_ai): Add Gemini sample for Multi-image example * feat(generative_ai): Add Gemini sample for single turn video example * feat(generative_ai): Add Gemini sample for multiturn chat * feat(generative_ai): Add Gemini sample for safety settings * feat(generative_ai): Add Gemini sample for count token * feat(generative_ai): Add Gemini sample's test cases * fix(generative_ai): lint suggestions * fix(generative_ai): fix test cases
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‎generative_ai/gemini_chat_example.py‎

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# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def chat_text_example(project_id: str, location: str) -> str:
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# [START aiplatform_gemini_multiturn_chat]
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import vertexai
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from vertexai.preview.generative_models import GenerativeModel, ChatSession
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# TODO(developer): Update and un-comment below lines
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# project_id = "PROJECT_ID"
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# location = "us-central1"
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vertexai.init(project=project_id, location=location)
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model = GenerativeModel("gemini-pro")
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chat = model.start_chat()
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def get_chat_response(chat: ChatSession, prompt: str) -> str:
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response = chat.send_message(prompt)
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return response.text
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prompt = "Hello."
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print(get_chat_response(chat, prompt))
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prompt = "What are all the colors in a rainbow?"
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print(get_chat_response(chat, prompt))
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prompt = "Why does it appear when it rains?"
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print(get_chat_response(chat, prompt))
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# [END aiplatform_gemini_multiturn_chat]
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return get_chat_response(chat, "Hello")
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def chat_stream_example(project_id: str, location: str) -> str:
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# [START aiplatform_gemini_multiturn_chat_stream]
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import vertexai
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from vertexai.preview.generative_models import GenerativeModel, ChatSession
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# TODO(developer): Update and un-comment below lines
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# project_id = "PROJECT_ID"
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# location = "us-central1"
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vertexai.init(project=project_id, location=location)
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model = GenerativeModel("gemini-pro")
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chat = model.start_chat()
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def get_chat_response(chat: ChatSession, prompt: str) -> str:
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text_response = []
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responses = chat.send_message(prompt, stream=True)
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for chunk in responses:
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text_response.append(chunk.text)
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return "".join(text_response)
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prompt = "Hello."
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print(get_chat_response(chat, prompt))
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prompt = "What are all the colors in a rainbow?"
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print(get_chat_response(chat, prompt))
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prompt = "Why does it appear when it rains?"
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print(get_chat_response(chat, prompt))
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# [END aiplatform_gemini_multiturn_chat_stream]
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return get_chat_response(chat, "Hello")
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‎generative_ai/gemini_count_token_example.py‎

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# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START aiplatform_gemini_token_count]
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import vertexai
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from vertexai.preview.generative_models import GenerativeModel
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def generate_text(project_id: str, location: str) -> str:
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# Initialize Vertex AI
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vertexai.init(project=project_id, location=location)
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# Load the model
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model = GenerativeModel("gemini-pro")
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# prompt tokens count
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print(model.count_tokens("why is sky blue?"))
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# Load example images
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response = model.generate_content("why is sky blue?")
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# response tokens count
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print(response._raw_response.usage_metadata)
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return response.text
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# [END aiplatform_gemini_token_count]
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‎generative_ai/gemini_guide_example.py‎

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# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START aiplatform_gemini_get_started]
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# [START aiplatform_gemini_guide_step1]
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# TODO(developer): Vertex AI SDK - uncomment below & run
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# pip3 install --upgrade --user google-cloud-aiplatform
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# gcloud auth application-default login
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# [END aiplatform_gemini_guide_step1]
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def generate_text(project_id: str, location: str) -> str:
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# [START aiplatform_gemini_guide_step2]
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# Initialize Vertex AI
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import vertexai
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# TODO(developer): Update and un-comment below lines
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# project_id = "PROJECT_ID"
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# location = "us-central1"
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vertexai.init(project=project_id, location=location)
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# [END aiplatform_gemini_guide_step2]
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# [START aiplatform_gemini_guide_step3]
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from vertexai.preview.generative_models import GenerativeModel, Part
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# [END aiplatform_gemini_guide_step3]
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# [START aiplatform_gemini_guide_step4]
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multimodal_model = GenerativeModel("gemini-pro-vision")
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# [END aiplatform_gemini_guide_step4]
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# [START aiplatform_gemini_guide_step5]
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response = multimodal_model.generate_content(
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[
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"what is shown in this image?",
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Part.from_uri(
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"gs://generativeai-downloads/images/scones.jpg", mime_type="image/jpeg"
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),
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]
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)
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print(response)
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# [END aiplatform_gemini_guide_step5]
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return response.text
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# [END aiplatform_gemini_get_started]
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‎generative_ai/gemini_multi_image_example.py‎

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# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import vertexai
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def generate_text_multimodal(project_id: str, location: str) -> str:
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# Initialize Vertex AI
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vertexai.init(project=project_id, location=location)
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# [START aiplatform_gemini_single_turn_multi_image]
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import http.client
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import typing
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import urllib.request
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from vertexai.preview.generative_models import GenerativeModel, Image
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# create helper function
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def load_image_from_url(image_url: str) -> Image:
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with urllib.request.urlopen(image_url) as response:
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response = typing.cast(http.client.HTTPResponse, response)
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image_bytes = response.read()
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return Image.from_bytes(image_bytes)
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# Load images from Cloud Storage URI
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landmark1 = load_image_from_url("https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png")
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landmark2 = load_image_from_url("https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png")
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landmark3 = load_image_from_url("https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png")
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# Pass multimodal prompt
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model = GenerativeModel("gemini-pro-vision")
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response = model.generate_content(
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[landmark1, "city: Rome, Landmark: the Colosseum",
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landmark2, "city: Beijing, Landmark: Forbidden City",
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landmark3, ]
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)
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print(response)
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# [END aiplatform_gemini_single_turn_multi_image]
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return response.text
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‎generative_ai/gemini_safety_config_example.py‎

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# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import vertexai
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# [START aiplatform_gemini_safety_settings]
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from vertexai.preview import generative_models
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def generate_text(project_id: str, location: str, image: str) -> str:
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# Initialize Vertex AI
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vertexai.init(project=project_id, location=location)
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# Load the model
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model = generative_models.GenerativeModel("gemini-pro-vision")
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# Generation config
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config = {"max_output_tokens": 2048, "temperature": 0.4, "top_p": 1, "top_k": 32}
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# Safety config
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safety_config = {
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generative_models.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: generative_models.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
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generative_models.HarmCategory.HARM_CATEGORY_HARASSMENT: generative_models.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
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}
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# Generate content
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responses = model.generate_content(
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[image, "Add your prompt here"],
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generation_config=config,
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stream=True,
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safety_settings=safety_config,
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)
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text_responses = []
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for response in responses:
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print(response.text)
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text_responses.append(response.text)
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return "".join(text_responses)
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# [END aiplatform_gemini_safety_settings]
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# if __name__ == '__main__':
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# import base64
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# base64_image_data = base64.b64encode(open('scones.jpg', 'rb').read()).decode("utf-8")
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# image = generative_models.Part.from_data(data=base64.b64decode(base64_image_data), mime_type="image/png")
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# generate_text(image)
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‎generative_ai/gemini_single_turn_video_example.py‎

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# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START aiplatform_gemini_single_turn_video]
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import vertexai
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from vertexai.preview.generative_models import GenerativeModel, Part
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def generate_text(project_id: str, location: str) -> str:
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# Initialize Vertex AI
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vertexai.init(project=project_id, location=location)
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# Load the model
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vision_model = GenerativeModel("gemini-pro-vision")
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# Generate text
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response = vision_model.generate_content(
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[
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"What is in the video?",
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Part.from_uri(
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"gs://cloud-samples-data/video/animals.mp4", mime_type="video/mp4"
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),
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]
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)
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print(response)
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return response.text
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# [END aiplatform_gemini_single_turn_video]
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‎generative_ai/requirements.txt‎

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pandas==1.3.5; python_version == '3.7'
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pandas==2.0.1; python_version > '3.7'
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google-cloud-aiplatform[pipelines]==1.31.0
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google-cloud-aiplatform[pipelines]==1.38.0
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google-auth==2.17.3

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