animator · Vivekkaspa · Jun 2, 2024 · Jun 2, 2024 · Jun 2, 2024 · Jun 2, 2024
diff --git a/contrib/machine-learning/flask-guide.md b/contrib/machine-learning/flask-guide.md
@@ -0,0 +1,167 @@
+# Overview of Flask & Deploying an ML model using Flask
+
+Developed by Armin Ronacher of Pocoo, Flask is an open-source micro web framework written in Python. It provides a lightweight and modular design that makes it highly adaptable for a variety of applications.
+
+## Key Features of Flask
+
+- Micro & Modular Framework
+- Provides Routing Mechnanism to map URLs to Python functions
+- Uses Jinja2 as its template engine, allowing to build dynamic web pages
+- Provides a request object for each HTTP request, allowing to handle GET, POST requests,etc.
+- Supports secure cookie-based session management
+- Protects against common web attacks like Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF)
+
+## Environment Setup & Installation
+
+Let's begin with setting up the Flask environment and installing it. We need to make sure the current python3 version is higher than 2.7 for doing so.
+
+#### Virtual environment installation
+```python
+pip install virtualenv
+```
+
+#### Create New Folder for virtual environment
+```python
+mkdir my_new_fold  
+cd  my_new_fold  
+virtualenv venv
+```
+
+#### Activate the environment
+```python
+venv\scripts\activate
+```
+
+#### Install the Flask
+```python
+ pip install flask  
+```
+
+## Basic Application using Flask & Routes
+
+Create and save the following file as flask_basic.py.
+```python
+from flask import Flask
+app = Flask(__name__)
+
+@app.route('/')
+def my_basic_appli():
+   return 'This is my basic flask application!’
+
+if __name__ == '__main__':
+   app.run(debug=True)
+```
+
+This application has a single route (/) that returns “This is my basic flask application!”.
+
+## Request & Response Objects - Using GET & POST methods
+
+Flask provides request and response objects to handle HTTP requests and responses. Here’s an example of handling GET and POST requests:
+
+```python
+from flask import Flask, request
+app = Flask(__name__)
+
+@app.route('/data', methods=['GET', 'POST'])
+def data():
+    if request.method == 'POST':
+        return "You've posted some data: " + str(request.json)
+    else:
+        return "Hello, GET request!"
+```
+
+## Dynamic Route Creation, Error Handling & HTTP Requests
+
+We can create dynamic routes in Flask like this:
+
+```python
+@app.route('/user/<username>')
+def show_user_profile(username):
+    return 'User %s' % username
+```
+
+For error handling, we can use error handlers:
+```python
+@app.errorhandler(404)
+def page_not_found(error):
+    return 'This page does not exist', 404
+```
+
+## Decorators & CRUD Operations
+
+Flask uses decorators for route handling. We can also use Flask-SQLAlchemy for CRUD operations:
+
+```python
+from flask import Flask
+from flask_sqlalchemy import SQLAlchemy
+
+app = Flask(__name__)
+app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/test.db'
+db = SQLAlchemy(app)
+
+class User(db.Model):
+    id = db.Column(db.Integer, primary_key=True)
+    username = db.Column(db.String(80), unique=True, nullable=False)
+    email = db.Column(db.String(120), unique=True, nullable=False)
+
+    def __repr__(self):
+        return '<User %r>' % self.username
+```
+
+
+## Creating ML Application & Deploying using Flask
+
+We can use Flask to serve a machine learning model. Consider an example of a Sentiment analysis model that predicts whether a given text is positive or negative in the context of analyzing customer reviews.
+
+- We’ll use Flask to serve the model, and we’ll use the **joblib** library to load the model because it’s more efficient for large datasets.
+- We’ll assume that the model has been trained and saved to a file named **‘Amazon_Customer_Reviews_Analysis_model.joblib’**, and the vectorizer (used for converting text data into numerical data that can be used by the model) has been saved to a file named **‘vectorizer.joblib’**.
+
+#### Installation of joblib library (if not yet installed)
+
+```python
+pip install  joblib
+```
+
+#### Quickstart with Code
+
+```python
+# Import necessary libraries
+from flask import Flask, request, jsonify
+import joblib
+
+app = Flask(__name__)
+
+# Load the model and vectorizer
+model = joblib.load('Amazon_Customer_Reviews_Analysis_model.joblib')
+vectorizer = joblib.load('vectorizer.joblib')
+
+@app.route('/api', methods=['POST'])
+def predict():
+    # Get data from POST request
+    data = request.get_json(force=True)
+
+    # Convert data into numerical data
+    data_vectorized = vectorizer.transform([data['text']])
+
+    # Make prediction using model loaded from disk as per the data
+    prediction = model.predict(data_vectorized)
+
+    # Take the first value of prediction
+    output = prediction[0]
+
+    # Convert the prediction into a JSON response
+    response = jsonify({'prediction': output})
+
+    return response
+
+if __name__ == '__main__':
+    app.run(port=5000, debug=True)
+```
+
+We can then run your Flask app with the following command:
+```python
+python app.py
+```
+Our Flask app will be accessible at http://localhost:5000/api.
+
+
diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md
@@ -16,3 +16,4 @@
 - [Types_of_Cost_Functions](cost-functions.md)
 - [Clustering](clustering.md)
 - [Grid Search](grid-search.md)
+- [Overview of Flask framework & Deploying ML model using Flask](flask-guide.md)