lambda_function.py

import tflite_runtime.interpreter as tflite
from keras_image_helper import create_preprocessor

preprocessor = create_preprocessor('xception', target_size = (299, 299))

interpreter = tflite.Interpreter(model_path = 'clothing_model.tflite')
interpreter.allocate_tensors()

input_index = interpreter.get_input_details()[0]['index']
output_index = interpreter.get_output_details()[0]['index']

classes = ['dress',
 'hat',
 'longsleeve',
 'outwear',
 'pants',
 'shirt',
 'shoes',
 'shorts',
 'skirt',
 't-shirt']
#url = 'http://bit.ly/mlbookcamp-pants'

def predict(url):

    X = preprocessor.from_url(url)
    interpreter.set_tensor(input_index, X)
    interpreter.invoke()
    preds = interpreter.get_tensor(output_index)
    float_predictions = preds[0].tolist()

    return dict(zip(classes, float_predictions))

def lambda_handler(event, context):
    url = event['url']
    result = predict(url)
    return result