The TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup. Click the Run in Google Colab button.

Colab link - Open colab

How to Easily Deploy Machine Learning Models Using Flask

Why Flask?

1. Easy to use.

2. Built in development server and debugger.

3. Integrated unit testing support.

4. RESTful request dispatching.

5. Extensively documented.

Importing Needed packages


import matplotlib.pyplot as plt
import pandas as pd
import pylab as pl
import numpy as np
%matplotlib inline

!pip install scikit-learn==0.23.2
import sklearn
print(sklearn.__version__)

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler

from sklearn import linear_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit

import warnings
warnings.filterwarnings('ignore')
# setting up default plotting parameters
%matplotlib inline

plt.rcParams['figure.figsize'] = [20.0, 7.0]
plt.rcParams.update({'font.size': 22,})

sns.set_palette('viridis')
sns.set_style('white')
sns.set_context('talk', font_scale=0.8)
np.random.seed(27)


!wget -O FuelConsumption.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv

df = pd.read_csv("FuelConsumption.csv")


from xgboost import XGBRegressor


train_x = df.drop(['MODELYEAR', 	'MAKE', 	'MODEL','VEHICLECLASS','TRANSMISSION','FUELTYPE', 'CO2EMISSIONS'], axis=1)
train_y = df['CO2EMISSIONS']

train_x = np.array(train_x)
train_y = np.array(train_y)

obj1 = XGBRegressor()

# split data into train and test sets

seed = 7
test_size = 0.2
X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size=test_size, random_state=seed)

Grid search is an exhaustive search over specified parameter values.


booster = ['gbtree']
eta = [0.1]
gamma = [0]
max_depth = [4]
min_child_weight = [1]
subsample = [0.5]
reg_lambda = [0.5]
scale_pos_weight = [2]



param_grid = dict(scale_pos_weight=scale_pos_weight,booster=booster,eta=eta,gamma=gamma,max_depth=max_depth,min_child_weight=min_child_weight,subsample=subsample,reg_lambda=reg_lambda)

grid = GridSearchCV(estimator=obj1, param_grid=param_grid, scoring='neg_root_mean_squared_error', verbose=1, n_jobs=-1)

grid_result = grid.fit(X_train, y_train)

STORE YOUR MODEL


import pickle
pickle.dump(grid, open('model.pkl','wb'))

model = pickle.load(open('model.pkl','rb'))

TEST MODEL


print(model.predict([[1,1,1,1,1,1]]))

Build requirements


pip freeze > requirements.txt

CREATE A Procfile : Create a file with name Procfile and paste content below line in it into it


web: gunicorn app:app

ORGANIZE FILES 1. Create folder templates and add your .html files in it. We create a webpage for calling the api. This is available at https://github.com/pranavn91/demoflask/blob/main/templates/index.html

2. Paste Procfile and model.pkl in outside templates

INSTALL FLASK AND NGROK


!pip install flask-ngrok
from flask_ngrok import run_with_ngrok
from flask import Flask

Create file - app.py


import numpy as np
from flask import Flask, request, jsonify, render_template
import pickle

app = Flask(__name__)
run_with_ngrok(app)   #starts ngrok when the app is run
model = pickle.load(open('model.pkl', 'rb'))


@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict',methods=['POST','GET'])
def predict():

    int_features = [int(x) for x in request.form.values()]    

    final_features = [np.array(int_features)]
    prediction = model.predict(final_features)

    output = round(prediction[0], 2)

    return render_template('index.html', prediction_text='CO2EMISSIONS should be $ {}'.format(output))



if __name__ == "__main__":
    app.run()

TensorFlow tutorials - How to Easily Deploy Machine Learning Models Using Flask