AWS Lambda handler file. Modify the code as you need

handler.py

import boto3
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import json

# change this to use other models
from SimpleModel import SimpleModel


client = boto3.client('dynamodb')
table_name = 'wordvec'

#
# get word vectors for word in sentence and build a matrix with the vectors
#
def sentence_to_matrix(sentence):
    words = sentence.split(' ')
    
    # request cannot contain duplicate keys. remove duplicates
    words_no_dup = list(set(words))
    read_batch_size = 100
    batches = [words_no_dup[i:i+read_batch_size] for i in range(0,len(words_no_dup),read_batch_size)]
    wordvec_a = []
    for batch in batches:
        request = [{'word':{'S':word}} for word in batch]
        
        response = client.batch_get_item(
            RequestItems = {
                table_name: {
                    'Keys': request
                }
            }
        )

        wordvec_a = wordvec_a + [(d['word']['S'], d['vector']['L']) for d in response['Responses'][table_name]]

    lookup = dict(wordvec_a)

    matrix = None
    for word in words:
        if word in lookup:
            vec_raw = lookup[word]
            vec = np.array([float(f['N']) for f in vec_raw])
        else:
            # random vector
            vec = np.random.rand(300)

        vec.resize(1, 1, vec.shape[0])
        if matrix is None:
            matrix = vec
        else:
            matrix = np.append(matrix, vec, axis=1)
    
    matrix.resize(1, matrix.shape[0], matrix.shape[1], matrix.shape[2])
    return matrix


def handler(event, context):
    sentence = event['input']

    input_matrix = sentence_to_matrix(sentence)

    # load and run model
    # you may need to modify this based on your model definition
    model = torch.load('SimpleModel.pt')
    model.eval()
    torchIn = torch.from_numpy(input_matrix.astype(np.float32))
    torchIn = Variable(torchIn)
    output = model(torchIn)

    # return result
    result = {
        'input': sentence,
        'output': output.data.tolist()[0]
    }
    result = json.dumps(result)
    return result