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image-super-resolution

Inference Endpoints

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image-super-resolution / local_test.py

Justin Dulay

update for batches

8b26614 10 months ago

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	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']