Instructions to use Pankaj001/Watchtower_sample_files with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use Pankaj001/Watchtower_sample_files with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("Pankaj001/Watchtower_sample_files") - Notebooks
- Google Colab
- Kaggle
| # Copyright 2025 Google LLC | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # 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 vertexai | |
| from vertexai import agent_engines | |
| from vertexai.preview.reasoning_engines import AdkApp | |
| from rag.agent import root_agent | |
| import logging | |
| import os | |
| from dotenv import set_key | |
| logging.basicConfig(level=logging.DEBUG) | |
| logger = logging.getLogger(__name__) | |
| GOOGLE_CLOUD_PROJECT = os.getenv("GOOGLE_CLOUD_PROJECT") | |
| GOOGLE_CLOUD_LOCATION = os.getenv("GOOGLE_CLOUD_LOCATION") | |
| STAGING_BUCKET = os.getenv("STAGING_BUCKET") | |
| # Define the path to the .env file relative to this script | |
| ENV_FILE_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".env")) | |
| vertexai.init( | |
| project=GOOGLE_CLOUD_PROJECT, | |
| location=GOOGLE_CLOUD_LOCATION, | |
| staging_bucket=STAGING_BUCKET, | |
| ) | |
| # Function to update the .env file | |
| def update_env_file(agent_engine_id, env_file_path): | |
| """Updates the .env file with the agent engine ID.""" | |
| try: | |
| set_key(env_file_path, "AGENT_ENGINE_ID", agent_engine_id) | |
| print(f"Updated AGENT_ENGINE_ID in {env_file_path} to {agent_engine_id}") | |
| except Exception as e: | |
| print(f"Error updating .env file: {e}") | |
| logger.info("deploying app...") | |
| app = AdkApp( | |
| agent=root_agent, | |
| enable_tracing=True, | |
| ) | |
| logging.debug("deploying agent to agent engine:") | |
| remote_app = agent_engines.create( | |
| app, | |
| requirements=[ | |
| "google-cloud-aiplatform[adk,agent-engines]==1.108.0", | |
| "google-adk==1.10.0", | |
| "python-dotenv", | |
| "google-auth", | |
| "tqdm", | |
| "requests", | |
| "llama-index", | |
| ], | |
| extra_packages=[ | |
| "./rag", | |
| ], | |
| ) | |
| # log remote_app | |
| logging.info(f"Deployed agent to Vertex AI Agent Engine successfully, resource name: {remote_app.resource_name}") | |
| # Update the .env file with the new Agent Engine ID | |
| update_env_file(remote_app.resource_name, ENV_FILE_PATH) |