- OS: Ubuntu 18.04/20.04

- Miniconda environment with necessary python package

- Install Docker and Node.js

- Install open source packages, npm and serverless.

- setup AWS account for 1 year free subscription by providing credit card details.

### Importing the Libraries
import torch
from torchvision import models

### Instancing a pre-trained model will download it's weights to a cache memory
model = models.mobilenet_v2(pretrained = True)

### Deactivating some layers in the model, so that model output it's inferences as expected.
model.eval()

### Trace the model with some dummy input
traced_model = torch.jit.trace(model,torch.randn(1,3,224,224))

### Saving the model
traced_model.save('mobilenet_v2.pt')

### Setting up serverless(sls) with the AWS user credentials
sudo sls config credentials --provider aws --key <AWS-key> --secret <AWS-secret-key>

### Create a service (Lambda function) in current directory with service name as new folder.
#serverless create --template <name-of-the-template> --path <name-of-the-service>
serverless create --template aws-python3 --path mobilenet-v2

sudo serverless plugin install -n serverless-python-requirements

sudo chown -R $USER:$USER .

{
  "name": "mobilenet-v2",
  "description": "",
  "version": "0.1.0",
  "dependencies": {},
  "scipts":{"deploy":"serverless deploy"},
  "devDependencies": {
    "serverless-python-requirements": "^5.1.0"
  }
}

"""Code to download pre-trained pytorch model.
Download the pre-trained model and convert into trace model
"""
try:
    import unzip_requirements
except ImportError:
    pass
from requests_toolbelt.multipart import decoder
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image

from torchvision.models import resnet50
from torch import nn

import boto3
import os
import tarfile  
import io
import base64
import json
print("Import End")

# define environmental variables if there are not existing
S3_BUCKET = os.environ['S3_BUCKET'] if 'S3_BUCKET' in os.environ else 'name-of-the-bucket'
MODEL_PATH = os.environ['MODEL_PATH'] if 'MODEL_PATH' in os.environ else 'mobilenet_v2.pt'
json_data = os.environ['json_data'] if 'json_data' in os.environ else 'imagenet1000_clsidx_to_labels.json'

print("Downloading the model:")

s3 = boto3.resource('s3')

try:
    if os.path.isfile(MODEL_PATH) != True:
        #get object from s3
        obj = s3.Bucket(S3_BUCKET).Object(MODEL_PATH)
        # read it in memory
        print("creating Bytestream")
        bytestream = io.BytesIO(obj.get()['Body'].read())
        print("Loading Model")
        model = torch.jit.load(bytestream)
        print("Model loaded")

except Exception as e:
    print(repr(e))
    raise(e)

def transform_image(image_bytes):

    """Transform the image for pre-trained model.
    Transform the image which includes resizing, centercrop and normalize.
    Args:
        image_bytes: Input image in bytes
    Returns:
        Tensor
    Raises:
        Except: An error occurred accessing the bytes.
    """
    try:
        transformations = transforms.Compose([
            transforms.Resize(255), # Resnet needed 224  image
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
        image = Image.open(io.BytesIO(image_bytes))
        return transformations(image).unsqueeze(0) #converted image into batch size of 1
    except Exception as e:
        print(repr(e))
        raise(e)

def imagenet1000_classidx_to_label(class_idx):

    """Convert class index to class labels, Imagenet.
    Converting predicted class index to class labels, Imagenet.
    Args:
        class_idx: str, predicted class index.
    Returns:
        A dict mapping keys to the corresponding table row data
        fetched. Each row is represented as a tuple of strings.
        Returned keys are always bytes.  If a key from the keys argument is
        missing from the dictionary, then that row was not found in the
        table (and require_all_keys must have been False).
    Raises:
        Exception: An error occurred accessing the json file.
    """
    try:
        #content_object = s3.Object(S3_BUCKET,json_data)
        print('Entered into Class prediction level')
        content_object = s3.Bucket(S3_BUCKET).Object(json_data)
        file_content = content_object.get()['Body'].read().decode('utf-8')
        json_content = json.loads(file_content)
        return json_content[class_idx]
    except Exception as e:
        print("Error",repr(e))
        return "Class Not Found"

def get_prediction(image_bytes):

    """Prediction from pre-trained model.
    Inferecing using pre-trained model
    Args:
        image_bytes: Transformed image_bytes
    Returns:
        int, predicted class index from imagenet
    """
    tensor = transform_image(image_bytes=image_bytes)
    return model(tensor).argmax().item()

def classify_image(event, context):

    """Classify image using api.
    Function is called from this template python: handler.py
    Args:
        event: Dictionary containing API inputs 
        context: Dictionary
    Returns:
        dictionary: API response
    Raises:
        Exception: Returning API repsonse 500
    """
    try:
        print("Entering into event")
        content_type_header = event['headers']['content-type']
        #print(event['body']) #printing the actual image in hex format
        body = base64.b64decode(event["body"]) #decoding the base64 image
        print("Body loaded")

        picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
        prediction = get_prediction(image_bytes=picture.content)
        print(prediction)

        prediction_label = imagenet1000_classidx_to_label(str(prediction))

        filename = picture.headers[b'Content-Disposition'].decode().split(';')[1].split('=')[1]
        if len(filename) < 4:
            filename = picture.headers[b'Content-Disposition'].decode().split(';')[2].split('=')[1]

        return { #returning the result for the event
            "statusCode": 200,
            "headers": {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
                "Access-Control-Allow-Credentials": True
                },
            "body": json.dumps({'file': filename.replace('"', ''),'predicted': f"{prediction}, {prediction_label}"})
    }
    except Exception as e:
        print(repr(e))
        return {
            "statusCode": 500,
            "headers": {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
                "Access-Control-Allow-Credentials": True
            },
            "body": json.dumps({"error": repr(e)})
        }

service: mobilenet-v2

provider:
  name: aws
  runtime: python3.8
  stage: dev
  region: ap-south-1
  timeout: 60
  environment:
    S3_BUCKET: s3-bucket-name
    MODEL_PATH: mobilenet_v2.pt
  iam:
    role:
      statements:
      - Effect: "Allow"
        Action:
          - s3:getObject
        Resource: arn:aws:s3:::s3-bucket-name/*

custom:
  pythonRequirements:
    dockerizePip: true
    zip: true
    slim: true
    strip: false
    noDeploy:
      - docutils
      - jmespath
      - pip
      - python-dateutil
      - setuptools
      - six
      - tensorboard
    useStaticCache: true
    useDownloadCache: true
    cacheLocation: "./cache"

package:
  patterns:
    - '!package.json'
    - '!package-log.json'
    - '!node_modules/**'
    - '!cache/**'
    - '!test/**'
    - '!__pycache__/**'
    - '!.pytest_cache/**'
    - '!model/**'

functions:
  classify_image:
    handler: handler.classify_image
    memorySize: 3008
    timeout: 60
    events:
      - http:
          path: mobilenetV2-classify
          method: post
          cors: true

plugins:
  - serverless-python-requirements

serverless deploy

    HTTP Method: POST

    URL: {generated API link after deployment}

    Content-Type: multipart/form-data

    Select image file for clasification: dog.jpg