Merge pull request #50 from Hestia-Homes/master

fixed docker file and added instructions

README.md

@@ -40,3 +40,45 @@ For example, for the branch sap_change-dev, the prefix=SAP_CHANGE_DEV, and the f
 - {prefix}_DATA_BUCKET, is the name of the s3 data bucket where data to be scored by the model is stored
 - {prefix}_MODEL_BUCKET, is the name of the s3 bucket where the model is stored
 - {prefix}_PREDICTIONS_BUCKET, is the name of the s3 bucket where the predictions are stored
+
+
+# Building and Testing the Prediction Lambda Function Locally
+TODO: Generalise these instructions for the various different pipelines
+
+This guide outlines the steps to build and test the Lambda function locally using Docker. These instructions assume you're working with a machine that has Docker installed.
+
+### Prerequisites
+Docker: Make sure Docker is installed and running on your machine.
+AWS Credentials: Ensure you have AWS credentials set up on your local machine, typically stored
+in ~/.aws/credentials.
+Root Directory: All commands should be run from the root directory of the repository.
+Step-by-Step Guide
+1. Building the Docker Image
+First, navigate to the root directory of the repository. Open a terminal and execute the following
+2. command to build the Docker image:
+
+```bash
+docker build -t sap_change -f deployment/Dockerfile.prediction.lambda .
+```
+
+This will build a Docker image tagged as sap_change using the Dockerfile.prediction.lambda located
+in the deployment directory.
+
+2. Running the Docker Image
+Once the image is built, you can run it using the following command:
+
+```bash
+docker run -p 9000:8080 -v ~/.aws/credentials:/root/.aws/credentials:ro -e RUNTIME_ENVIRONMENT=dev sap_change
+```
+This command does the following:
+
+Maps port 9000 on your local machine to port 8080 on the Docker container.
+Mounts your AWS credentials into the Docker container in read-only mode.
+Sets the RUNTIME_ENVIRONMENT variable to dev.
+3. Testing the Lambda Function
+To test the Lambda function, use the following curl command:
+
+```json
+curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{"body": "{\"file_location\": \"s3://retrofit-data-dev/model_build_data/change_data/rdsap_full/test_data_with_id.parquet\", \"property_id\": 1, \"portfolio_id\": 4, \"created_at\": \"now\"}"'
+```
+This will send a POST request to the running Lambda function and pass in the required data as JSON.

deployment/Dockerfile.prediction.lambda

@@ -12,7 +12,7 @@ ENV RUNTIME_ENVIRONMENT=${RUNTIME_ENVIRONMENT}
 RUN yum install -y gcc python3-devel
 
 # Install python packages
-COPY modules/ml-pipeline/src/requirements/predictions/requirements.txt ./requirements.txt
+COPY modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt ./requirements.txt
 RUN pip install --no-cache-dir -r ./requirements.txt
 
 # Copy the project code
@@ -22,4 +22,4 @@ COPY deployment/handlers/prediction_app.py prediction_app.py
 # Get the model
 # RUN dvc pull -r ${RUNTIME_ENVIRONMENT}
 
-CMD [ "prediction_handler.handler" ]
+CMD [ "prediction_app.handler" ]

deployment/handlers/prediction_app.py

@@ -57,8 +57,6 @@ def handler(event, context):
             else event["body"]
         )
 
-        logger.info("Inside handler with body: " + str(body))
-
         data_path = body["file_location"]
         property_id = body["property_id"]
         portfolio_id = body["portfolio_id"]