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diff --git a/Tooling/Project_2/grading_scripts/README.md b/Tooling/Project_2/grading_scripts/README.md
new file mode 100644
index 0000000..66cd7a6
--- /dev/null
+++ b/Tooling/Project_2/grading_scripts/README.md
@@ -0,0 +1,75 @@
+### Grading Scripts
+
+ - How to run the script: 
+    ```
+    usage: grader_script_p1.py [-h] [--access_key ACCESS_KEY] [--secret_key SECRET_KEY] [--input_bucket INPUT_BUCKET] [--lambda_name LAMBDA_NAME]
+    Grading Script
+    options:
+    -h, --help          show this help message and exit
+    --access_key ACCESS_KEY
+                        ACCCESS KEY ID of the grading IAM user
+    --secret_key SECRET_KEY
+                        SECRET KEY of the grading IAM user
+    --input_bucket INPUT_BUCKET
+                        Name of the S3 Input Bucket
+    --output_bucket OUTPUT_BUCKET
+                        Name of the S3 Output Bucket
+    --lambda_name LAMBDA_NAME
+                        Name of the Lambda function
+
+    ```
+    **Note**: We will follow the naming conventions for S3 Bucket and Lambda function names as described in the project document to grade your submission
+
+**Examples**:
+
+We will show examples for each option below.
+
+**Validate Lambda**: 
+
+  - Successful Test
+   ```
+   python grading_scripts/grader_script_p1.py --access_key XXXX --secret_key XXXX --input_bucket 1234567890-input --lambda_name Video-splitting
+   
+   =============================================================================
+======== Welcome to CSE546 Cloud Computing AWS Console ======================
+=============================================================================
+IAM ACESS KEY ID: XXXX
+IAM SECRET ACCESS KEY: XXXX
+=============================================================================
+1 - Validate 1 Lambda function
+2 - Validate S3 Buckets names and initial states
+3 - Validate S3 output bucket subfolders
+4 - Validate S3 output objects
+5 - Check lambda average duration
+6 - Check lambda concurrency
+0 - Exit
+Enter a choice: 1
+
+Lambda function Video-splitting HTTPStatusCode 200
+Test status of Test_1 : PASS
+
+
+
+   ```
+   - Failed Test
+   ```
+   python grading_scripts/grader_script_p1.py --access_key XXXX --secret_key XXXX --input_bucket 1234567890-input --lambda_name Video-splitting
+   
+   =============================================================================
+======== Welcome to CSE546 Cloud Computing AWS Console ======================
+=============================================================================
+IAM ACESS KEY ID: XXXX
+IAM SECRET ACCESS KEY: XXXX
+=============================================================================
+1 - Validate 1 Lmabda function
+2 - Validate S3 Buckets names and initial states
+3 - Validate S3 output bucket subfolders
+4 - Validate S3 output objects
+5 - Check lambda average latency
+6 - Check lambda concurrency
+0 - Exit
+Enter a choice: 1
+
+Error An error occurred (ResourceNotFoundException) when calling the GetFunction operation: Function not found: arn:aws:lambda:us-east-1:252116767176:function:Video-splitting
+Test status of Test_1 : FAIL
+   ```
diff --git a/Tooling/Project_2/grading_scripts/grader_script_p1.py b/Tooling/Project_2/grading_scripts/grader_script_p1.py
new file mode 100644
index 0000000..2a1cd34
--- /dev/null
+++ b/Tooling/Project_2/grading_scripts/grader_script_p1.py
@@ -0,0 +1,270 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import pdb
+import time
+import botocore
+import argparse
+import textwrap
+import boto3
+from boto3 import client as boto3_client
+from botocore.exceptions import ClientError
+from datetime import datetime,timezone,timedelta
+
+class aws_grader():
+    def __init__(self, access_key, secret_key, input_bucket, output_bucket, lambda_name, region):
+
+        self.access_key = access_key
+        self.secret_key  = secret_key
+        self.region = region
+        self.s3 = boto3_client('s3', aws_access_key_id=self.access_key,
+                          aws_secret_access_key=self.secret_key, region_name=region)
+        self.cloudwatch = boto3_client('cloudwatch', aws_access_key_id=self.access_key,
+                          aws_secret_access_key=self.secret_key, region_name=region)
+        self.iam_session = boto3.Session(aws_access_key_id=self.access_key,
+                                         aws_secret_access_key=self.secret_key)
+        self.s3_resources = self.iam_session.resource('s3', region)
+        self.lambda_function = boto3_client('lambda', aws_access_key_id=self.access_key,
+                               aws_secret_access_key=self.secret_key, region_name=region)
+        self.in_bucket_name = input_bucket
+        self.out_bucket_name = output_bucket
+        self.lambda_name = lambda_name
+        self.test_result = {}
+
+    def validate_lambda_exists(self, TC_num):
+        try:
+            response = self.lambda_function.get_function(
+            FunctionName=self.lambda_name
+            )
+            print(f"Lambda function {self.lambda_name} HTTPStatusCode {response['ResponseMetadata']['HTTPStatusCode']}")
+            self.test_result[TC_num] = "PASS"
+        except self.lambda_function.exceptions.ResourceNotFoundException as e:
+            print(f"Error {e}")
+            self.test_result[TC_num] = "FAIL"
+        print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+
+    def validate_s3_subfolders(self, TC_num):
+        in_objects = self.s3.list_objects_v2(Bucket=self.in_bucket_name)
+        if in_objects['KeyCount']==0:
+            self.test_result[TC_num] = "FAIL"
+            print(f"Empty bucket {self.in_bucket_name}")
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+            return
+        self.test_result[TC_num] = "PASS"
+        for obj in in_objects['Contents']:
+            folder_name = obj['Key'].rsplit('.',1)[0]
+            out_objects = self.s3.list_objects_v2(Bucket=self.out_bucket_name, Prefix=folder_name, Delimiter='/')
+            if out_objects['KeyCount'] == 1 or out_objects['KeyCount'] == 11:
+                folder_name = out_objects['CommonPrefixes'][0]['Prefix'].rsplit("/")[0]
+                prefix_name = out_objects['Prefix']
+                if folder_name == prefix_name:
+                    print(f"{prefix_name} matches with {folder_name}")
+            else:
+                prefix_name = out_objects['Prefix']
+                self.test_result[TC_num] = "FAIL"
+                print(f"NO folder named {prefix_name}")
+                print(out_objects)
+        print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+
+    def validate_s3_output_objects(self, TC_num):
+        bucket = self.s3_resources.Bucket(self.out_bucket_name)
+        in_bucket = self.s3_resources.Bucket(self.in_bucket_name)
+
+        try:
+            objects = list(bucket.objects.all())
+            print(f"Got {len(objects)} objects {[o.key for o in objects]} from bucket {bucket.name}")
+            in_objects = list(in_bucket.objects.all())
+            self.test_result[TC_num] = "PASS"
+
+            for i,folder_n in enumerate(in_objects):
+                if len(in_objects) * 10 == len(objects) or len(in_objects) * 11 == len(objects):
+                    print(f"Number of objects matches for given input {folder_n}")
+                    self.test_result[TC_num] = "PASS"
+                else:
+                    self.test_result[TC_num] = "FAIL"
+                    break
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+
+
+        except ClientError:
+            print(f"Couldn't get objects for bucket {bucket.name}")
+            raise
+        else:
+            return
+
+    # You have to make sure to run the workload generator and it executes within 15 mins
+    # of polling for cloudwatch metrics.
+    def check_lambda_duration(self, TC_num):
+        response = self.cloudwatch.get_metric_data(
+            MetricDataQueries=[
+                {
+                    'Id': 'testDuration',
+                    'MetricStat': {
+                        'Metric': {
+                            'Namespace': 'AWS/Lambda',
+                            'MetricName': 'Duration'
+                        },
+                        'Period': 600,
+                        'Stat': 'Average'
+                    },
+                    'ReturnData': True,
+                },
+            ],
+            StartTime=datetime.now().utcnow() - timedelta(minutes=15),
+            EndTime=datetime.now().utcnow(),
+            ScanBy='TimestampAscending'
+        )
+        print(response['MetricDataResults'][0]['Values'])
+        values = response['MetricDataResults'][0]['Values']
+        if not values:
+            self.test_result[TC_num] = "FAIL"
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+            return
+        if max(values) > 10000:
+            self.test_result[TC_num] = "FAIL"
+        else:
+            self.test_result[TC_num] = "PASS"
+        print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+
+    def check_lambda_concurrency(self,TC_num):
+        response = self.cloudwatch.get_metric_data(
+            MetricDataQueries=[
+                {
+                    'Id': 'testConcurrency',
+                    'MetricStat': {
+                        'Metric': {
+                            'Namespace': 'AWS/Lambda',
+                            'MetricName': 'ConcurrentExecutions'
+                        },
+                        'Period': 600,
+                        'Stat': 'Maximum'
+                    },
+                    'ReturnData': True,
+                },
+            ],
+            StartTime=datetime.now().utcnow() - timedelta(minutes=15),
+            EndTime=datetime.now().utcnow(),
+            ScanBy='TimestampAscending'
+        )
+        print(response['MetricDataResults'][0]['Values'])
+        values = response['MetricDataResults'][0]['Values']
+        if not values:
+            self.test_result[TC_num] = "FAIL"
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+            return
+        if max(values) < 5:
+            self.test_result[TC_num] = "FAIL"
+        else:
+            self.test_result[TC_num] = "PASS"
+        print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+
+    def check_bucket_exist(self, bucket):
+        if not bucket:
+            print(f"Bucket name is empty!")
+            return False
+        try:
+            self.s3.head_bucket(Bucket=bucket)
+            print(f"Bucket {bucket} Exists!")
+            return True
+        except botocore.exceptions.ClientError as e:
+            # If a client error is thrown, then check that it was a 404 error.
+            # If it was a 404 error, then the bucket does not exist.
+            error_code = int(e.response['Error']['Code'])
+            if error_code == 403:
+                print("Private Bucket. Forbidden Access!")
+                return True
+            elif error_code == 404:
+                print(f"Bucket {bucket} does Not Exist!")
+                return False
+    def empty_s3_bucket(self, bucket_name):
+        bucket = self.s3_resources.Bucket(bucket_name)
+        bucket.objects.all().delete()
+        print(f"{bucket_name} S3 Bucket is now EMPTY !!")
+
+    def count_bucket_objects(self, bucket_name):
+        bucket = self.s3_resources.Bucket(bucket_name)
+        count = 0
+        for index in bucket.objects.all():
+            count += 1
+        #print(f"{bucket_name} S3 Bucket has {count} objects !!")
+        return count
+
+    def validate_s3_buckets_initial(self, TC_num):
+        print(" - Run this BEFORE the workload generator client starts. Press Ctrl^C to exit.")
+        print(" - WARN: If there are objects in the S3 buckets; they will be deleted")
+        print(" ---------------------------------------------------------")
+
+        in_isExist = self.check_bucket_exist(self.in_bucket_name)
+        out_isExist = self.check_bucket_exist(self.out_bucket_name)
+
+        if in_isExist:
+            ip_obj_count = self.count_bucket_objects(self.in_bucket_name)
+            print(f"S3 Input Bucket:{self.in_bucket_name} has {ip_obj_count} object(s)")
+        if out_isExist:
+            op_obj_count = self.count_bucket_objects(self.out_bucket_name)
+            print(f"S3 Output Bucket:{self.out_bucket_name} has {op_obj_count} object(s)")
+
+        if in_isExist and out_isExist and ip_obj_count==0 and op_obj_count==0:
+            self.test_result[TC_num] = "PASS"
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+        else:
+            self.test_result[TC_num] = "FAIL"
+            print(f"Test status of {TC_num} : {self.test_result[TC_num]}")
+
+    def display_menu(self):
+        print("\n")
+        print("=============================================================================")
+        print("======== Welcome to CSE546 Cloud Computing AWS Console ======================")
+        print("=============================================================================")
+        print(f"IAM ACESS KEY ID: {self.access_key}")
+        print(f"IAM SECRET ACCESS KEY: {self.secret_key}")
+        print("=============================================================================")
+        print("1 - Validate 1 Lambda function")
+        print("2 - Validate S3 Buckets names and initial states")
+        print("3 - Validate S3 output bucket subfolders")
+        print("4 - Validate S3 output objects")
+        print("5 - Check lambda average duration")
+        print("6 - Check lambda concurrency")
+        print("0 - Exit")
+        print("Enter a choice:")
+        choice = input()
+        return choice
+
+    def main(self):
+        while(1):
+            choice = self.display_menu()
+            if int(choice) == 1:
+                self.validate_lambda_exists('Test_1')
+            elif int(choice) == 2:
+                self.validate_s3_buckets_initial('Test_2')
+            elif int(choice) == 3:
+                self.validate_s3_subfolders('Test_3')
+            elif int(choice) == 4:
+                self.validate_s3_output_objects('Test_4')
+            elif int(choice) == 5:
+                self.check_lambda_duration('Test_5')
+            elif int(choice) == 6:
+                self.check_lambda_concurrency('Test_6')
+            elif int(choice) == 0:
+                break
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Grading Script')
+    parser.add_argument('--access_key', type=str, help='ACCCESS KEY ID of the grading IAM user')
+    parser.add_argument('--secret_key', type=str, help='SECRET KEY of the grading IAM user')
+    parser.add_argument('--input_bucket', type=str, help='Name of the S3 Input Bucket')
+    parser.add_argument('--output_bucket', type=str, help='Name of the S3 Output Bucket')
+    parser.add_argument('--lambda_name', type=str, help="Name of the Lambda function")
+
+
+    args = parser.parse_args()
+
+    access_key = args.access_key
+    secret_key   = args.secret_key
+    input_bucket    = args.input_bucket
+    output_bucket   = args.output_bucket
+    lambda_name = args.lambda_name
+    region = 'us-east-1'
+
+    aws_obj = aws_grader(access_key, secret_key, input_bucket, output_bucket, lambda_name,region)
+    aws_obj.main()
diff --git a/Tooling/Project_2/src/Dockerfile b/Tooling/Project_2/src/Dockerfile
new file mode 100644
index 0000000..ec7c2e7
--- /dev/null
+++ b/Tooling/Project_2/src/Dockerfile
@@ -0,0 +1,59 @@
+#__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:${RUNTIME_VERSION} AS python-alpine
+
+#RUN apt-get update \
+#    && apt-get install -y cmake ca-certificates libgl1-mesa-glx
+RUN python${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
+#RUN apk --no-cache add ffmpeg
+
+# Copy handler function
+COPY requirements.txt ${FUNCTION_DIR}
+#COPY ffmpeg ${FUNCTION_DIR}
+#COPY ffmpeg /usr/bin
+
+RUN python${RUNTIME_VERSION} -m pip install -r requirements.txt --target ${FUNCTION_DIR}
+COPY entry.sh /
+
+# Copy function code
+COPY handler.py ${FUNCTION_DIR}
+RUN chmod 777 /entry.sh
+
+# Set the CMD to your handler (could also be done as a parameter override outside of the Dockerfile)
+ENTRYPOINT [ "/entry.sh" ]
+CMD [ "handler.handler" ]
diff --git a/Tooling/Project_2/src/entry.sh b/Tooling/Project_2/src/entry.sh
new file mode 100644
index 0000000..a608361
--- /dev/null
+++ b/Tooling/Project_2/src/entry.sh
@@ -0,0 +1,6 @@
+#!/bin/sh
+if [ -z "${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
diff --git a/Tooling/Project_2/src/face-extraction-code.py b/Tooling/Project_2/src/face-extraction-code.py
new file mode 100644
index 0000000..8bcdecd
--- /dev/null
+++ b/Tooling/Project_2/src/face-extraction-code.py
@@ -0,0 +1,37 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import os
+import imutils
+import cv2
+import json
+from PIL import Image
+from facenet_pytorch import MTCNN
+from shutil import rmtree
+
+mtcnn = MTCNN(keep_all=True, device='cpu')
+               
+def face_extraction_function(folder_name):
+    if not os.path.exists(folder_name):
+        print(f"Folder {folder_name} does not exist!")
+        return None
+
+    pics = sorted(os.listdir(folder_name))
+    for pic in pics:
+        path = os.path.join(folder_name, pic)
+        frame = cv2.imread(path, cv2.IMREAD_COLOR)
+        boxes, _ = mtcnn.detect(frame)
+
+        if boxes is None:
+            rmtree(folder_name)
+            return
+
+        for box in boxes:
+            cv2.rectangle(frame,
+                          (int(box[0]), int(box[1])),
+                          (int(box[2]), int(box[3])),
+                          (0, 255, 0),
+                          2)
+            cv2.imwrite(path,frame)
+    return folder_name
+
diff --git a/Tooling/Project_2/src/face-recognition-code.py b/Tooling/Project_2/src/face-recognition-code.py
new file mode 100644
index 0000000..9594dc1
--- /dev/null
+++ b/Tooling/Project_2/src/face-recognition-code.py
@@ -0,0 +1,44 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import os
+import imutils
+import cv2
+import json
+from PIL import Image, ImageDraw, ImageFont
+from facenet_pytorch import MTCNN, InceptionResnetV1
+from shutil import rmtree
+import numpy as np
+import torch
+
+
+mtcnn = MTCNN(image_size=240, margin=0, min_face_size=20) # initializing mtcnn for face detection
+resnet = InceptionResnetV1(pretrained='vggface2').eval() # initializing resnet for face img to embeding conversion
+
+def face_recognition_function(folder_name):
+    if not os.path.exists(folder_name):
+        print(f"Folder {folder_name} does not exist!")
+        return None
+    name = ""
+    pics = sorted(os.listdir(folder_name))
+    return_names = dict()
+    for pic in pics:
+        path = os.path.join(folder_name, pic)
+        img = cv2.imread(path, cv2.IMREAD_COLOR)
+        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+        face,prob = mtcnn(img, return_prob=True, save_path=None)
+        saved_data = torch.load('/tmp/data.pt')  # loading data.pt file
+        if face != None:
+            emb = resnet(face.unsqueeze(0)).detach()  # detech is to make required gradient false
+            embedding_list = saved_data[0]  # getting embedding data
+            name_list = saved_data[1]  # getting list of names
+            dist_list = []  # list of matched distances, minimum distance is used to identify the person
+            for idx, emb_db in enumerate(embedding_list):
+                dist = torch.dist(emb, emb_db).item()
+                dist_list.append(dist)
+            idx_min = dist_list.index(min(dist_list))
+            pic_extension = pic.split(".")[0]
+            return_names[pic_extension] = name_list[idx_min]
+    return return_names
+
+
diff --git a/Tooling/Project_2/src/face_recognition_additional_files/tmp/data.pt b/Tooling/Project_2/src/face_recognition_additional_files/tmp/data.pt
new file mode 100644
index 0000000..184abd7
Binary files /dev/null and b/Tooling/Project_2/src/face_recognition_additional_files/tmp/data.pt differ
diff --git a/Tooling/Project_2/src/handler.py b/Tooling/Project_2/src/handler.py
new file mode 100644
index 0000000..d3b3625
--- /dev/null
+++ b/Tooling/Project_2/src/handler.py
@@ -0,0 +1,8 @@
+#__copyright__   = "Copyright 2024, VISA Lab"
+#__license__     = "MIT"
+
+from boto3 import client as boto3_client
+
+
+def handler(event, context):	
+	print("Hello")
diff --git a/Tooling/Project_2/src/motion-detection-function.py b/Tooling/Project_2/src/motion-detection-function.py
new file mode 100644
index 0000000..88f3510
--- /dev/null
+++ b/Tooling/Project_2/src/motion-detection-function.py
@@ -0,0 +1,53 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import os
+import imutils
+import cv2
+def detect(lgray, frame, min_area):
+    frame = imutils.resize(frame, width=320)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    gray = cv2.GaussianBlur(gray, (21, 21), 0)
+
+    frameDelta = cv2.absdiff(lgray, gray)
+    thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
+
+    thresh = cv2.dilate(thresh, None, iterations=2)
+    contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
+                cv2.CHAIN_APPROX_SIMPLE)
+    contours = imutils.grab_contours(contours)
+    for c in contours:
+        if cv2.contourArea(c) > min_area:
+            return True, gray
+    return False, gray
+
+def motion_detection_function(folder_name):
+    if not os.path.exists(folder_name):
+        print(f"Folder {folder_name} does not exist!")
+        return None
+
+    # Motion detection specific parameters
+    min_area = 10  # if 10% of the frame was changed, motion happens
+    last_gray = None
+
+    pics = sorted(os.listdir(folder_name))
+    for pic in pics:
+        path = os.path.join(folder_name, pic)
+        frame = cv2.imread(path, cv2.IMREAD_COLOR)
+        if frame is None:
+            print("failed to open picture %s" % path)
+            return None
+
+        if last_gray is None:
+            frame = imutils.resize(frame, width=320)
+            last_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            last_gray = cv2.GaussianBlur(last_gray, (21, 21), 0)
+            continue
+
+        detected, gray = detect(last_gray, frame, min_area)
+        if detected:
+            break
+        else:
+            last_gray = gray
+            os.remove(path)
+    return folder_name
diff --git a/Tooling/Project_2/src/requirements.txt b/Tooling/Project_2/src/requirements.txt
new file mode 100644
index 0000000..36f1741
--- /dev/null
+++ b/Tooling/Project_2/src/requirements.txt
@@ -0,0 +1,4 @@
+boto3
+ffmpeg
+ffmpeg-python
+
diff --git a/Tooling/Project_2/src/video-splitting-cmdline.py b/Tooling/Project_2/src/video-splitting-cmdline.py
new file mode 100644
index 0000000..e11ef3a
--- /dev/null
+++ b/Tooling/Project_2/src/video-splitting-cmdline.py
@@ -0,0 +1,30 @@
+#__copyright__   = "Copyright 2024, VISA Lab"
+#__license__     = "MIT"
+
+
+import os
+import ffmpeg
+import subprocess
+import math
+
+
+def video_splitting_cmdline(video_filename):
+    filename = os.path.basename(video_filename)
+    outdir = os.path.splitext(filename)[0]
+    outdir = os.path.join("/tmp",outdir)
+    output_dir = outdir
+    if not os.path.exists(outdir):
+        os.makedirs(outdir)
+
+    split_cmd = '/usr/bin/ffmpeg -ss 0 -r 1 -i ' +video_filename+ ' -vf fps=1/10 -start_number 0 -vframes 10 ' + outdir + "/" + 'output-%02d.jpg -y'
+    try:
+        subprocess.check_call(split_cmd, shell=True)
+    except subprocess.CalledProcessError as e:
+        print(e.returncode)
+        print(e.output)
+
+    fps_cmd = 'ffmpeg -i ' + video_filename + ' 2>&1 | sed -n "s/.*, \\(.*\\) fp.*/\\1/p"'
+    fps = subprocess.check_output(fps_cmd, shell=True).decode("utf-8").rstrip("\n")
+    fps = math.ceil(float(fps))
+    return outdir
+
diff --git a/Tooling/Project_2/workload_generator/README.md b/Tooling/Project_2/workload_generator/README.md
new file mode 100644
index 0000000..f9c03dc
--- /dev/null
+++ b/Tooling/Project_2/workload_generator/README.md
@@ -0,0 +1,50 @@
+# CSE546 workload_generator for Project 2
+
+This repository contains code examples for you to use our workload generator.
+
+Usage:
+```
+usage: workload_generator.py [-h] [--access_key AWS_ACCESS_KEY_ID] [--secret_key AWS_SECRET_KEY] [--input_bucket S3_INPUT_BUCKET_NAME] [--testcase_folder TESTCASE_FOLDER]
+
+Upload videos to input S3
+
+options:
+  -h, --help            show this help message and exit
+  --access_key ACCESS_KEY
+                        ACCCESS KEY of the grading IAM user
+  --secret_key SECRET_KEY
+                        SECRET KEY of the grading IAM user
+  --input_bucket INPUT_BUCKET
+                        Name of the input bucket, e.g. 1234567890-input
+  --output_bucket OUTPUT_BUCKET
+                        Name of the output bucket, e.g. 1234567890-stage-1
+  --testcase_folder TESTCASE_FOLDER
+                        the path of the folder where videos are saved, e.g. test_cases/test_case_1/
+```
+
+Examples:
+
+The following command sends videos from test_case_1 folder to 1234567890-input bucket.
+```
+python3 workload_generator.py \
+ --access_key <ACCESS_KEY> \
+ --secret_key <SECRET_KEY> \
+ --input_bucket 1234567890-input \
+ --testcase_folder test_cases/test_case_1/ \
+```
+
+Sample output:
+```
+Nothing to clear in input bucket
+Uploading to input bucket..  name: test_5.mp4
+Uploading to input bucket..  name: test_2.mp4
+Uploading to input bucket..  name: test_8.mp4
+Uploading to input bucket..  name: test_7.mp4
+Uploading to input bucket..  name: test_6.mp4
+Uploading to input bucket..  name: test_4.mp4
+Uploading to input bucket..  name: test_1.mp4
+Uploading to input bucket..  name: test_0.mp4
+Time to run =  11.037546873092651 (seconds)
+
+```
+
diff --git a/Tooling/Project_2/workload_generator/workload_generator.py b/Tooling/Project_2/workload_generator/workload_generator.py
new file mode 100644
index 0000000..d55cd2a
--- /dev/null
+++ b/Tooling/Project_2/workload_generator/workload_generator.py
@@ -0,0 +1,73 @@
+#__copyright__   = "Copyright 2024, VISA Lab"
+#__license__     = "MIT"
+
+from boto3 import client as boto3_client
+import os
+import argparse
+import time
+
+input_bucket = "546proj2"
+output_bucket = "546proj2output"
+test_cases = "test_cases/"
+
+start_time = time.time()
+parser = argparse.ArgumentParser(description='Upload videos to input S3')
+# parser.add_argument('--num_request', type=int, help='one video per request')
+parser.add_argument('--access_key', type=str, help='ACCCESS KEY of the grading IAM user')
+parser.add_argument('--secret_key', type=str, help='SECRET KEY of the grading IAM user')
+parser.add_argument('--input_bucket', type=str, help='Name of the input bucket, e.g. 1234567890-input')
+parser.add_argument('--output_bucket', type=str, help='Name of the output bucket, e.g. 1234567890-stage-1')
+parser.add_argument('--testcase_folder', type=str, help='the path of the folder where videos are saved, e.g. test_cases/test_case_1/')
+
+args = parser.parse_args()
+
+access_key = args.access_key
+secret_key = args.secret_key
+input_bucket = args.input_bucket
+output_bucket = args.output_bucket
+test_cases = args.testcase_folder
+region = 'us-east-1'
+
+s3 = boto3_client('s3', aws_access_key_id = access_key,
+                    aws_secret_access_key = secret_key, region_name=region)
+def clear_input_bucket(input_bucket):
+	global s3
+	list_obj = s3.list_objects_v2(Bucket=input_bucket)
+	try:
+		for item in list_obj["Contents"]:
+			key = item["Key"]
+			s3.delete_object(Bucket=input_bucket, Key=key)
+	except:
+		print("Nothing to clear in input bucket")
+	
+def clear_output_bucket(output_bucket):
+	global s3
+	list_obj = s3.list_objects_v2(Bucket=output_bucket)
+	try:
+		for item in list_obj["Contents"]:
+			key = item["Key"]
+			s3.delete_object(Bucket=output_bucket, Key=key)
+	except:
+		print("Nothing to clear in output bucket")
+
+def upload_to_input_bucket_s3(input_bucket, path, name):
+	global s3
+	s3.upload_file(path + name, input_bucket, name)
+	
+def upload_files(input_bucket, test_dir):
+	for filename in os.listdir(test_dir):
+		if filename.endswith(".mp4") or filename.endswith(".MP4"):
+			print("Uploading to input bucket..  name: " + str(filename)) 
+			upload_to_input_bucket_s3(input_bucket, test_dir, filename)
+
+	
+
+clear_input_bucket(input_bucket)
+clear_input_bucket(output_bucket)
+
+upload_files(input_bucket,test_cases)
+
+end_time = time.time()
+print("Time to run = ", end_time-start_time, "(seconds)")
+print(f"Timestamps: start {start_time}, end {end_time}")
+