stage1 done, exported old lambda as yaml

Project-2/Part-1/p1-video-splitting.yaml

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+# This AWS SAM template has been generated from your function's configuration. If
+# your function has one or more triggers, note that the AWS resources associated
+# with these triggers aren't fully specified in this template and include
+# placeholder values. Open this template in AWS Application Composer or your
+# favorite IDE and modify it to specify a serverless application with other AWS
+# resources.
+AWSTemplateFormatVersion: '2010-09-09'
+Transform: AWS::Serverless-2016-10-31
+Description: An AWS Serverless Application Model template describing your function.
+Resources:
+  p1videosplitting:
+    Type: AWS::Serverless::Function
+    Properties:
+      CodeUri: .
+      Description: ''
+      MemorySize: 512
+      Timeout: 60
+      Architectures:
+        - x86_64
+      EphemeralStorage:
+        Size: 512
+      EventInvokeConfig:
+        MaximumEventAgeInSeconds: 21600
+        MaximumRetryAttempts: 2
+      ImageUri: >-
+        146064153251.dkr.ecr.us-east-1.amazonaws.com/546-proj2-p1@sha256:f3e8b79ffa429bfeb24b7b3a1786bb206dd89c2c7f01f3ff9fbb0d1379c9e238
+      PackageType: Image
+      Policies:
+        - Statement:
+            - Effect: Allow
+              Action:
+                - logs:PutLogEvents
+                - logs:CreateLogGroup
+                - logs:CreateLogStream
+              Resource: arn:aws:logs:*:*:*
+            - Effect: Allow
+              Action:
+                - s3:GetObject
+              Resource: arn:aws:s3:::*/*
+            - Effect: Allow
+              Action:
+                - s3:PutObject
+              Resource: arn:aws:s3:::*/*
+      SnapStart:
+        ApplyOn: None
+      Events:
+        BucketEvent1:
+          Type: S3
+          Properties:
+            Bucket:
+              Ref: Bucket1
+            Events:
+              - s3:ObjectCreated:*
+  Bucket1:
+    Type: AWS::S3::Bucket
+    Properties:
+      VersioningConfiguration:
+        Status: Enabled
+      BucketEncryption:
+        ServerSideEncryptionConfiguration:
+          - ServerSideEncryptionByDefault:
+              SSEAlgorithm: AES256
+  BucketPolicy1:
+    Type: AWS::S3::BucketPolicy
+    Properties:
+      Bucket: Bucket1
+      PolicyDocument:
+        Statement:
+          - Action: s3:*
+            Effect: Deny
+            Principal: '*'
+            Resource:
+              - arn:aws:s3:::Bucket1/*
+              - arn:aws:s3:::Bucket1
+            Condition:
+              Bool:
+                aws:SecureTransport: false

Project-2/Part-2/src/video-splitting/Dockerfile

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+#__copyright__   = "Copyright 2024, VISA Lab"
+#__license__     = "MIT"
+
+# Define global args
+ARG FUNCTION_DIR="/home/app/"
+ARG RUNTIME_VERSION="3.8"
+ARG DISTRO_VERSION="3.12"
+
+FROM alpine:latest
+FROM python:${RUNTIME_VERSION} AS python-alpine
+
+RUN python${RUNTIME_VERSION} -m pip install --upgrade pip
+
+FROM python-alpine AS build-image
+
+# Include global args in this stage of the build
+ARG FUNCTION_DIR
+ARG RUNTIME_VERSION
+# Create function directory
+RUN mkdir -p ${FUNCTION_DIR}
+
+# Install Lambda Runtime Interface Client for Python
+RUN python${RUNTIME_VERSION} -m pip install awslambdaric --target ${FUNCTION_DIR}
+
+# Stage 3 - final runtime image
+# Grab a fresh copy of the Python image
+FROM python-alpine
+# Include global arg in this stage of the build
+ARG FUNCTION_DIR
+# Set working directory to function root directory
+WORKDIR ${FUNCTION_DIR}
+# Copy in the built dependencies
+COPY --from=build-image ${FUNCTION_DIR} ${FUNCTION_DIR}
+# (Optional) Add Lambda Runtime Interface Emulator and use a script in the ENTRYPOINT for simpler local runs
+ADD https://github.com/aws/aws-lambda-runtime-interface-emulator/releases/latest/download/aws-lambda-rie /usr/bin/aws-lambda-rie
+RUN chmod 755 /usr/bin/aws-lambda-rie
+
+# Install ffmpeg
+RUN apt-get update 
+RUN apt-get install -y ffmpeg
+
+# Install other dependencies
+COPY requirements.txt ${FUNCTION_DIR}
+RUN python${RUNTIME_VERSION} -m pip install -r requirements.txt --target ${FUNCTION_DIR}
+
+# Copy function code
+COPY entry.sh /
+COPY handler.py video_splitting_cmdline.py dummy_s3_trigger_event.json ${FUNCTION_DIR}
+RUN chmod 777 /entry.sh
+
+# Set the CMD to your handler (could also be done as a parameter override outside of the Dockerfile)
+ENTRYPOINT [ "/entry.sh" ]
+CMD [ "handler.handler" ]

Project-2/Part-2/src/video-splitting/dummy_s3_trigger_event.json

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+{
+"Records":[
+{
+"eventVersion":"2.0",
+"eventSource":"aws:s3",
+"awsRegion":"us-east-1",
+"eventTime":"1970-01-01T00:00:00.000Z",
+"eventName":"ObjectCreated:Put",
+"userIdentity":{
+"principalId":"EXAMPLE"
+},
+"requestParameters":{
+"sourceIPAddress":"127.0.0.1"
+},
+"responseElements":{
+"x-amz-request-id":"EXAMPLE123456789",
+"x-amz-id-2":"EXAMPLE123/5678abcdefghijklambdaisawesome/mnopqrstuvwxyzABCDEFGH"
+},
+"s3":{
+"s3SchemaVersion":"1.0",
+"configurationId":"testConfigRule",
+"bucket":{
+"name":"1229569564-input",
+"ownerIdentity":{
+"principalId":"EXAMPLE"
+},
+"arn":"arn:aws:s3:::1229569564-input"
+},
+"object":{
+"key":"jellyfish_jam.mp4",
+"size":1024,
+"eTag":"0123456789abcdef0123456789abcdef",
+"sequencer":"0A1B2C3D4E5F678901"
+}
+}
+}
+]
+}

Project-2/Part-2/src/video-splitting/entry.sh

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+#!/bin/sh
+if [ -z "${AWS_LAMBDA_RUNTIME_API}" ]; then
+    exec /usr/bin/aws-lambda-rie /usr/local/bin/python -m awslambdaric $1
+else
+    exec /usr/local/bin/python -m awslambdaric $1
+fi

Project-2/Part-2/src/video-splitting/handler.py

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+import json
+import urllib.parse
+import boto3
+from video_splitting_cmdline import video_splitting_cmdline
+
+print("Loading function")
+
+# attach execution policies and IAM roles in deployment lambda
+sesh = boto3.Session()
+
+s3_client = sesh.client("s3", region_name="us-east-1")
+lambda_client = sesh.client("lambda", region_name="us-east-1")
+
+
+def handler(event, context):
+    for record in event["Records"]:
+        # get uploaded object
+        in_bucket = record["s3"]["bucket"]["name"]
+        if in_bucket != "1229569564-input":
+            continue
+        key = urllib.parse.unquote_plus(record["s3"]["object"]["key"], encoding="utf-8")
+        tmpkey = key.replace("/", "")
+        download_path = "/tmp/{}".format(tmpkey)
+        s3_client.download_file(in_bucket, key, download_path)
+
+        # process it
+        out_file = video_splitting_cmdline(download_path)
+
+        # upload output object
+        s3_client.upload_file(
+            "/tmp/" + out_file,
+            "1229569564-stage-1",
+            out_file,
+        )
+
+        # invoke face recognition lambda
+        # https://docs.aws.amazon.com/lambda/latest/dg/invocation-async.html
+        # (note: better to use SNS or some dedicated event handling mechanism rather than direct invocation?)
+        invocation_params = {
+            "bucket_name": "1229569564-stage-1",
+            "image_file_name": out_file,
+        }
+
+        invoke_response = lambda_client.invoke(
+            FunctionName="face-recognition",
+            InvocationType="Event",
+            Payload=json.dumps(invocation_params),
+        )
+        print(invoke_response)
+    return
+
+
+if __name__ == "__main__":
+    with open("dummy_s3_trigger_event.json", "r") as dummy_event:
+        event = json.loads(dummy_event.read())
+    handler(event, None)

Project-2/Part-2/src/video-splitting/requirements.txt

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+boto3

Project-2/Part-2/src/video-splitting/stage1_policy.json

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+{
+	"Version": "2012-10-17",
+	"Statement": [
+		{
+			"Effect": "Allow",
+			"Action": [
+				"logs:PutLogEvents",
+				"logs:CreateLogGroup",
+				"logs:CreateLogStream"
+			],
+			"Resource": "arn:aws:logs:*:*:*"
+		},
+		{
+			"Effect": "Allow",
+			"Action": [
+				"s3:GetObject",
+				"s3:PutObject"
+			],
+			"Resource": "arn:aws:s3:::*/*"
+		},
+		{
+			"Effect": "Allow",
+			"Action": "lambda:InvokeFunction",
+			"Resource": "*"
+		}
+	]
+}

Project-2/Part-2/src/video-splitting/video_splitting_cmdline.py

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+import os
+import subprocess
+
+
+def video_splitting_cmdline(video_filename):
+    filename = os.path.basename(video_filename)
+    outfile = os.path.splitext(filename)[0] + ".jpg"
+
+    split_cmd = "ffmpeg -y -i " + video_filename + " -vframes 1 " + "/tmp/" + outfile
+    try:
+        subprocess.check_call(split_cmd, shell=True)
+    except subprocess.CalledProcessError as e:
+        print(e.returncode)
+        print(e.output)
+
+    return outfile