findDogForApi.py

# USAGE
# Start the server:
#   python run_keras_server.py
# Submit a request via cURL:
#   curl -X POST -F image=@dog.jpg 'http://localhost:5000/predict'
# Submita a request via Python:
# python simple_request.py

# import the necessary packages
from keras.applications import ResNet50
from keras.preprocessing.image import img_to_array
from keras.applications import imagenet_utils
from PIL import Image
import numpy as np
import flask
import io

# initialize our Flask application and the Keras model
app = flask.Flask(__name__)
model = None

def load_model():
  # load the pre-trained Keras model (here we are using a model
  # pre-trained on ImageNet and provided by Keras, but you can
  # substitute in your own networks just as easily)
  global model
  model = ResNet50(weights="imagenet")

def prepare_image(image, target):
  # if the image mode is not RGB, convert it
  if image.mode != "RGB":
    image = image.convert("RGB")

  # resize the input image and preprocess it
  image = image.resize(target)
  image = img_to_array(image)
  image = np.expand_dims(image, axis=0)
  image = imagenet_utils.preprocess_input(image)

  # return the processed image
  return image

@app.route("/predict", methods=["POST"])
def predict():
  # initialize the data dictionary that will be returned from the
  # view
  data = {"success": False}

  # ensure an image was properly uploaded to our endpoint
  if flask.request.method == "POST":
    if flask.request.files.get("image"):
      # read the image in PIL format
      image = flask.request.files["image"].read()
      image = Image.open(io.BytesIO(image))

      # preprocess the image and prepare it for classification
      image = prepare_image(image, target=(224, 224))

      # classify the input image and then initialize the list
      # of predictions to return to the client
      preds = model.predict(image)
      results = imagenet_utils.decode_predictions(preds)
      data["predictions"] = []

      # loop over the results and add them to the list of
      # returned predictions
      for (imagenetID, label, prob) in results[0]:
        r = {"label": label, "probability": float(prob)}
        data["predictions"].append(r)

      # indicate that the request was a success
      data["success"] = True

  # return the data dictionary as a JSON response
  return flask.jsonify(data)

# if this is the main thread of execution first load the model and
# then start the server
if __name__ == "__main__":
  print(("* Loading Keras model and Flask starting server..."
    "please wait until server has fully started"))
  load_model()
  app.run(debug = False, threaded = False)