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Exposing ML/DL Models as REST APIs
  • Difficulty Level : Medium
  • Last Updated : 17 Jan, 2019

In this article, we will learn how to expose ML/DL model as flask APIs.

Frameworks we’ll be using:
Keras is a Deep Learning library, built on top of backends such as Tensorflow, Theano or CNTK. It provides abstraction and allows rapid development of ML/DL models.

Flask is a micro web framework in Python, which is used to quickly spin up servers to serve pages. Refer Introduction to Flask.

Since the focus of the article is to serve a ML/DL model using an API, we wouldn’t get deeper into the making of the Convolutional model. We will be using the ResNet50 Convolutional Neural Network.

Install Tensorflow and Keras



StepLinux/MacWindows
Update pippip install -U pippython -m pip install -U pip
Install virtualenvsudo pip install virtualenvpip install virtualenv
Create a new folder ml-apimkdir ml-api && cd ml-apimkdir ml-api && cd ml-apiCreate a virtual environment(Good practice) and activate itvirtualenv –system-site-packages -p python3 ./venvvirtualenv –system-site-packages -p python3 ./venvActivate the virtual environmentsource ./venv/bin/activate

.\venv\Scripts\activateInstall TensorFlowpip install –upgrade tensorflowpip install –upgrade tensorflowInstall Keraspip install keraspip install kerasInstall other dependenciespip install flask gevent requests pillowpip install flask gevent requests pillow

Examples:

Creating the REST API :




# keras_server.py
  
# Python program to expose a ML model as flask REST API
  
# import the necessary modules
from keras.applications import ResNet50 # pre-built CNN Model
from keras.preprocessing.image import img_to_array 
from keras.applications import imagenet_utils
import tensorflow as tf
from PIL import Image
import numpy as np
import flask
import io
  
# Create Flask application and initialize Keras model
app = flask.Flask(__name__)
model = None
  
# Function to Load the model 
def load_model():
      
    # global variables, to be used in another function
    global model     
    model = ResNet50(weights ="imagenet")
    global graph 
    graph = tf.get_default_graph()
  
# Every ML/DL model has a specific format
# of taking input. Before we can predict on
# the input image, we first need to preprocess it.
def prepare_image(image, target):
    if image.mode != "RGB":
        image = image.convert("RGB")
      
    # Resize the image to the target dimensions
    image = image.resize(target) 
      
    # PIL Image to Numpy array
    image = img_to_array(image) 
      
    # Expand the shape of an array,
    # as required by the Model
    image = np.expand_dims(image, axis = 0
      
    # preprocess_input function is meant to
    # adequate your image to the format the model requires
    image = imagenet_utils.preprocess_input(image) 
  
    # return the processed image
    return image
  
# Now, we can predict the results.
@app.route("/predict", methods =["POST"])
def predict():
    data = {} # dictionary to store result
    data["success"] = False
  
    # Check if image was properly sent to our endpoint
    if flask.request.method == "POST":
        if flask.request.files.get("image"):
            image = flask.request.files["image"].read()
            image = Image.open(io.BytesIO(image))
  
            # Resize it to 224x224 pixels 
            # (required input dimensions for ResNet)
            image = prepare_image(image, target =(224, 224))
  
        # Predict ! global preds, results
            with graph.as_default():
                preds = model.predict(image)
                results = imagenet_utils.decode_predictions(preds)
                data["predictions"] = []
  
          
            for (ID, label, probability) in results[0]:
                r = {"label": label, "probability": float(probability)}
                data["predictions"].append(r)
  
            data["success"] = True
  
    # return JSON response
    return flask.jsonify(data)
  
  
  
if __name__ == "__main__":
    print(("* Loading Keras model and Flask starting server..."
        "please wait until server has fully started"))
    load_model()
    app.run()

Run the flask server

python keras_server.py 

 
Note #1: For the first time you run it, it takes time to download the weights of the model. In subsequent runs, this won’t be the case.
Note #2: Save images in the same directory (ml-api) – dog.jpg, cat.jpg, lion.jpg .

Make requests to the server:

Method #1 : use cURL (Download cURL for Windows from here.)

$ curl -X POST -F image=@dog.jpg "http://localhost:5000/predict"

 
Method #2 : (Suitable if making a Full-stack web app with proper UI)
Create a simple HTML form




<!-- index.html -->
<form action = "http://localhost:5000/predict" method = "POST" 
         enctype = "multipart/form-data">
         <input type = "file" name = "image" />
         <input type = "submit"/>
</form>

 
Method #3 : Create a simple python script to make HTTP requests to flask server.




# simple_request.py
import requests
import sys
  
  
# provide image name as command line argument
IMAGE_PATH = sys.argv[1
  
image = open(IMAGE_PATH, "rb").read()
payload = {"image": image}
  
# make request to the API
request = requests.post(URL, files = payload).json()
  
if request["success"]:
    # Print formatted Result
    print("% s % 15s % s"%("Rank", "Label", "Probability"))
    for (i, result) in enumerate(request["predictions"]):
        print("% d.    % 17s %.4f"%(i + 1, result["label"],
            result["probability"]))
  
    else:
        print("Request failed")

Run it as :

python simple_request.py dog.jpg

Output:

To deactivate after you are done(both Windows and Linux) :

> deactivate

So the workflow, in general for any model is

  • Build the model and save it.
  • Create a Flask app.
  • Load the Model
  • Define endpoints of API for each type of requests.
  • Preprocess input according to the Model’s architecture and feed it to the model
  • Process the input and send back the output to client.

machine-learning-img




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