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How to use test_response_content_type method in localstack

Best Python code snippet using localstack_python

inference.py

Source:inference.py

1import imp, sys2import numpy as np3import os4import pickle5import torch6import io7import boto38import json9from torchvision import transforms10from PIL import Image11from six import BytesIO12str_labels = ["FP", "GOOD", "VG", "VG-EX", "EX", "EX-MT", "NM", "NM-MT", "MINT", "GEM-MT"]13    14def train(hyperparameters, input_data_config, channel_input_dirs, output_data_dir,15          num_gpus, num_cpus, hosts, current_host, **kwargs):16    pass17def test(ctx, net, val_data):18    pass19def model_fn(model_dir):20    """Creates pytorch model specified via model_dir.21    22    Args:23        model_dir (str): path to model root dir. 24    """25    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")26    model = torch.load(open(os.path.join(model_dir, 'model/model.pth'), "rb")).to(device)27    print('[INFO] model loaded:')28    print(model.eval())29    return model 30def predict_fn(input_object, model):31    """ Uses Image library to load image and transfrom into float numbers for model prediction.32    33    Args:34        input_object (Image): image loaded using PIL.Image35        model (Object): unpickled model provided using model_fn36    """37    transformer = transforms.Compose([38                                  transforms.Resize((255, 255)),39                                  transforms.ToTensor(),40                                  transforms.Normalize(mean=[0.485, 0.456, 0.406],41                                                       std=[0.229, 0.224, 0.225]),42                                  ])43    print("[INFO] Running model inference on image: {}".format(input_object))44    X = transformer(input_object)45    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')46    model.to(device)47    model.eval()48    with torch.no_grad():49        logits = model(X[None, ...].to(device))50        print("[INFO]: The prediction logits are: {}".format(logits))51        pred = logits.argmax(dim=1)52        return str_labels[pred]53def input_fn(request_body, request_content_type):54    """ Loads input from request content and returns image specified in request_body as PIL.Image.55    56    Args:57        request_body (bytes): serialized dictionary with two required key: "bucket" and "key" which specify an image to predict.58        request_content_type (str): type of content59        60    """61    print('[INFO] request_body: {}'.format(request_body))62    data = json.loads(request_body.decode())63    s3 =  boto3.resource('s3')64    bucket = s3.Bucket(data['bucket'])65    object = bucket.Object(data['key'])66    response = object.get()67    file_stream = response['Body']68    image = Image.open(file_stream)69 70    #print('[INFO] Got Image of shape {} with scale {}'.format(image.shape, image.max()))71    return image72def output_fn(prediction, content_type):73    """Outputs result of prediction.74    75    Args:76        prediction (str): Label predicted by predict_fn77        content_type (str): type of content78    """79    print('[INFO] prediction: {}'.format(prediction))80    return {'statusCode': 200, 'body': '{}'.format(prediction) }81if __name__ == "__main__":82    """83    Test script - please run this at the root directory of model/, i.e., ./model/..84    """85    86    test_model_dir = "./"87    test_request_body = json.dumps({'bucket': 'ccbd-mint-cv-model', 'key': 'test_data.jpeg'}).encode('utf-8')88    test_request_content_type = "test_type" 89    test_response_content_type = "test_type"90    91    # Deserialize the Invoke request body into an object we can perform prediction on92    input_object = input_fn(test_request_body, test_request_content_type)93    test_model = model_fn(test_model_dir)94    95    # Perform prediction on the deserialized object, with the loaded model96    prediction = predict_fn(input_object, test_model)97    # Serialize the prediction result into the desired response content type98    output = output_fn(prediction, test_response_content_type)99    ...

test_GeniosSite.py

Source:test_GeniosSite.py

...14    if actual_response:15        assert actual_response == BASE_URL16    else:17        assert False18def test_response_content_type():19    headers = site.get_response_headers()20    content_type = headers["Content-Type"]21    assert content_type == "text/html; charset=UTF-8"22def test_site_connection_type():23    headers = site.get_response_headers()24    connection_type = headers["Connection"]25    assert connection_type == "Upgrade, Keep-Alive"26def test_site_link_lists():27    actual_links = site.get_site_link_lists()28    expected_links = csv_manipulation(SITE_LIST).read_csv_lines()29    if len(actual_links) == len(expected_links):30        check_link_list = zip(sorted(actual_links), sorted(expected_links))31        for actual, expected in check_link_list:32            assert actual == expected33    else:34        assert False35def test_all_cubes_types():36    cube_name = csv_manipulation(CUBE_TYPES).read_csv_specific_column(0)37    cube_link = csv_manipulation(CUBE_TYPES).read_csv_specific_column(1)38    expected_cube_types = dict(zip(cube_name, cube_link))39    actual_cube_types = site.get_all_cubes_types()40    if len(actual_cube_types) == len(expected_cube_types):41        for key in actual_cube_types.keys():42            assert actual_cube_types[key] == expected_cube_types[key]43    else:44        assert False45if __name__ == '__main__':46    # test_response_content_type()47    # test_site_connection_type()48    # test_site_link_lists()...

renderers_test.py

Source:renderers_test.py

1# -*- coding: utf-8 -*-2from h import renderers3def test_response_content_type(pyramid_request):4    renderer = renderers.CSV({})5    renderer({}, {'request': pyramid_request})6    assert pyramid_request.response.content_type == 'text/csv'7def test_render_simple_csv(pyramid_request):8    renderer = renderers.CSV({})9    sys = {'request': pyramid_request}10    value = {'header': ['One', 'Two'],11             'rows': [[1, 2], [3, 4]]}12    assert renderer(value, sys) == "One,Two\r\n1,2\r\n3,4\r\n"13def test_render_unicode_csv(pyramid_request):14    renderer = renderers.CSV({})15    sys = {'request': pyramid_request}16    value = {'header': [u'Ó', u'Ä'],17             'rows': [[u'Ã±', u'ã'], [u'ïº', u'Óª']]}...

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