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Deploy Machine Learning Model using Python Flask | Machine Learning Project Tutorial

Updated: May 30, 2023

Discover the art of deploying machine learning models with Python Flask! This comprehensive tutorial takes you through the process of building, packaging, and deploying a machine learning project. Learn to create a RESTful API, handle model predictions, and provide real-time insights. Enhance your skills in model deployment and bring your machine learning projects to life. #DeployMLModel #PythonFlask #MachineLearning

deploy machine learning model using python flask
Deploy Machine Learning model using Python Flask

In this project tutorial we will implement Python Flask on a trained model using the Iris Dataset Project which we already trained and processed.

If you haven't seen the Iris Dataset Project you may view the article and tutorial video here

You can watch the video based tutorial with step by step explanation down below

First we must save the trained model in order to implement Python Flask

# save the model
import pickle
filename = 'savedmodel.sav'
pickle.dump(model, open(filename, 'wb'))
  • Pickle is used to save the model

  • Pickle is a module for data type and file manipulation

iris flower dataset
Iris flower dataset
  • Display of the dataset

Next we load the model

load_model = pickle.load(open(filename,'rb'))

Now we can predict data from the model

load_model.predict([[6.0, 2.2, 4.0, 1.0]])

array(['Iris-versicolor'], dtype=object)

  • Originally the model was label encoded, where the array value would be from zero to two as processed in the label encoder section

  • Training the data without label encoder will return the actual label name

After applying all the necessary configuration we proceed to the backend to implement Flask

from flask import Flask, render_template, request
import pickle
  • Necessary import to use Flask for this project

  • Pickle is also imported to load the model

First we initialize Flask and load the model for processing

app = Flask(__name__)
# load the model
model = pickle.load(open('savedmodel.sav', 'rb'))

Next we display the home page and create a function

def home():
    result = ''
    return render_template('index.html', **locals())
  • Defined function will return the index of the current home page that we want to load

Later we define another function to predict the data

@app.route('/predict', methods=['POST', 'GET'])
def predict():
    sepal_length = float(request.form['sepal_length'])
    sepal_width = float(request.form['sepal_width'])
    petal_length = float(request.form['petal_length'])
    petal_width = float(request.form['petal_width'])
    result = model.predict([[sepal_length, sepal_width, petal_length, petal_width]])[0]
    return render_template('index.html', **locals())
  • This function will request the user data from the webpage and predict the label from the loaded model and return to the webpage

if __name__ == '__main__':
  • Main function of the program, if any error occurs it will appear in the terminal.

Now we must define "index.html" to get user input and display the results



<form action='/predict' method="POST">

Sepal Length: <input type='text' name='sepal_length'><br>

Sepal Width: <input type='text' name='sepal_width'><br>

Petal Length: <input type='text' name='petal_length'><br>

Petal Width: <input type='text' name='petal_width'><br>

Class: {{result}} <br>

<input type='submit' value='Predict'>




  • Place the index.html file in templates folder to avoid any issues

  • This is the HTML body structure for this project to input data in the forms and predict the result from the model

  • You may structure your page(s) however you want, this is a simple view in a single page

  • You must organize the display of the results with same order as how they are ordered in the model to avoid input errors

flask deployed model demo
  • Display of the web page that we defined

flask deployed model demo
Deployed Model Demo
  • You input the corresponding values in the box to predict a sample

flask deployed model demo
  • After clicking the predict box it displays the output class

  • You may design a separate web page to view the output

Final Thoughts

  • You may create multiple HTML pages or JavaScript files to display the results

  • You can implement Flask on cloud platforms to deploy your trained model

In this project tutorial, we have explored how to deploy a machine learning model using Python Flask framework to display the results from a trained model.

Get the project notebook from here Thanks for reading the article!!! Check out more project videos from the YouTube channel Hackers Realm


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