File size: 1,843 Bytes
8b26614 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
from handler import EndpointHandler
import json
# init handler
my_handler = EndpointHandler(path=".")
import base64
# prepare sample payload
# non_holiday_payload = {"inputs": "I am quite excited how this will turn out", "date": "2022-08-08"}
# holiday_payload = {"inputs": "Today is a though day", "date": "2022-07-04"}
# with open('sample_input.json', 'r') as file:
# data = json.load(file)
import io
from PIL import Image
import requests
response = requests.get('https://mystore-12345-product-images.s3-us-east-2.amazonaws.com/0c817b58-2774-4f02-95b8-3ae379aa2e98.jpeg')
image = Image.open(io.BytesIO(response.content)).convert('RGB')
response2 = requests.get('https://mystore-12345-product-images.s3-us-east-2.amazonaws.com/3b9c698b-b7ae-4c5d-a978-2179ccc08d12.jpeg')
image2 = Image.open(io.BytesIO(response2.content)).convert('RGB')
response3 = requests.get('https://mystore-12345-product-images.s3-us-east-2.amazonaws.com/72801dfa-5d6a-442e-91cd-80bdb394a323.jpeg')
image3 = Image.open(io.BytesIO(response3.content)).convert('RGB')
pil_images = [image.copy() for i in range(10)]
pil_images[1] = image2.copy()
pil_images[2] = image3.copy()
base64_strings = []
for output_image in pil_images:
buffered = io.BytesIO()
output_image = output_image.convert('RGB')
output_image.save(buffered, format="png")
img_str = base64.b64encode(buffered.getvalue())
base64_strings.append(img_str)
inputs = {
'image': base64_strings
}
# test the handler
import time
start = time.time()
prediction=my_handler(inputs)
# holiday_payload=my_handler(holiday_payload)
print("inference time itself is", time.time() - start)
# show results
# print("prediction", prediction)
print("type of prediction", type(prediction))
data_json = prediction
print("type of prediction", data_json.keys())
img_str = data_json['image'] |