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import boto3
import logging
import sagemaker
from sagemaker.model import Model
import argparse
import os
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def deploy_app(acc_id, region_name, role_arn, ecr_repo_name, endpoint_name="customer-support-chatbot"):
"""
Deploys a Gradio app as a SageMaker endpoint using an ECR image.
Args:
acc_id (str): AWS account ID
region_name (str): AWS region name
role_arn (str): IAM role ARN for SageMaker
ecr_repo_name (str): ECR repository name
endpoint_name (str): SageMaker endpoint name (default: "customer-support-chatbot")
"""
# Initialize SageMaker session
sagemaker_session = sagemaker.Session()
# Define the image URI in ECR
ecr_image = f"{acc_id}.dkr.ecr.{region_name}.amazonaws.com/{ecr_repo_name}:latest"
# Define model
model = Model(
image_uri=ecr_image,
role=role_arn,
sagemaker_session=sagemaker_session
)
# Deploy model as a SageMaker endpoint
logger.info(f"Starting deployment of Gradio app to SageMaker endpoint {endpoint_name}...")
predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.m5.xlarge",
endpoint_name=endpoint_name
)
logger.info(f"Gradio app deployed successfully to endpoint: {endpoint_name}")
if __name__ == "__main__":
# Parse arguments from CLI
parser = argparse.ArgumentParser(description="Deploy Gradio app to SageMaker")
parser.add_argument("--account_id", type=str, required=True, help="AWS Account ID")
parser.add_argument("--region", type=str, required=True, help="AWS Region")
parser.add_argument("--role_arn", type=str, required=True, help="IAM Role ARN for SageMaker")
parser.add_argument("--ecr_repo_name", type=str, required=True, help="ECR Repository name")
parser.add_argument("--endpoint_name", type=str, default="customer-support-chatbot", help="SageMaker Endpoint Name")
args = parser.parse_args()
# Deploy the Gradio app to SageMaker
deploy_app(args.account_id, args.region, args.role_arn, args.ecr_repo_name, args.endpoint_name)
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