Falsk Part

Flask is most used framework for making API for machine learning and deep learning applications.

To create API we need two parts

1. Wrap the model: code that deal with model and return response.
2. Building the app: This is where we communicate with the client and build an actual API with Flask.

To deploy api to be accessed using end users we use Heroku that need :-

• Procfile :- configuration file.
• main:- code of api, define routes and its functions.
• requirements.txt:- contains list of packages needed in code.
• model.pkl :- serialized pre-trained model.

Flask code :-

from flask import Flask , request , jsonify
from sklearn.externals import joblib
import numpy as np 


app = Flask(__name__)

# main path (root )
@app.route("/")
def hello():
    return "Hello Every One To Qurany App"


@app.route('/uploadfile',methods=['GET','POST'])
def uploadfile():
    // do logic here, load model, predict values. 
    return "Result" 


if __name__ == '__main__':
    app.run(debug = True , port= 5874)

it consists from

1. import statments.
2. creating instance from flask app.
3. define end points using @app.route(path).

end point with more details

• First define end point (route) using @app.route(path)

• add to route definition ttype of requests such as GET, POST, ..etc.

• create function and return response.

• receive data from client:

  • Form-url encoded :-

  request.form.get("file") 

  • raw json:- request.get_josn["file"]
  • files :- file = request.files['file']

• return response as json using jsonfy by using

  return jsonify( result = str(file.filename))