# 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()