Author Archives: Evelina Rosser

CAN YOU EXPLAIN HOW TO ADD USER AUTHENTICATION TO THE FLASK APPLICATION

To add user authentication to a Flask application, you will need to manage user registration, login/logout functionality, store user account information, and protect routes that require a logged in user.

The first step is to decide how you will store user account information like usernames, passwords, etc. A common approach is to use a SQL database like SQLite. You can create a database model to represent a user account using Flask-SQLAlchemy. The model should include at minimum a username, password hash, and any other profile fields you want to store.

For example:

python
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from flask_sqlalchemy import SQLAlchemy

db = SQLAlchemy()

class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(80), unique=True, nullable=False)
password = db.Column(db.String(255), nullable=False)

Next, you need to set up user registration views and forms. Create a RegistrationForm that validates the username and password fields. Then add a route and view to handle POST requests to /register:

python
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from flask_wtf import FlaskForm
from wtforms import StringField, PasswordField
from wtforms.validators import DataRequired, Length, EqualTo

class RegistrationForm(FlaskForm):
username = StringField(‘Username’, validators=[DataRequired(), Length(min=2, max=20)])
password = PasswordField(‘Password’, validators=[DataRequired()])
confirm_password = PasswordField(‘Confirm Password’, validators=[DataRequired(), EqualTo(‘password’)])

# route to handle form submission
@app.route(‘/register’, methods=[‘GET’, ‘POST’])
def register():

form = RegistrationForm()

if form.validate_on_submit():
user = User(username=form.username.data, password=generate_password_hash(form.password.data))
db.session.add(user)
db.session.commit()
return redirect(url_for(‘login’))

return render_template(‘register.html’, form=form)

To authenticate users on login, you need to securely hash and store passwords in the database. Use the Python password hashing library passlib.hash() to hash passwords on registration, and compare hashed passwords on login using passlib.check_password_hash().

For the login route, you can create a LoginForm similar to the registration form, then query the database to check if a user with that username exists and the passwords match:

python
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from passlib.hash import pbkdf2_sha256

#…

class LoginForm(FlaskForm):
username = StringField(‘Username’, validators=[DataRequired()])
password = PasswordField(‘Password’, validators=[DataRequired()])

@app.route(‘/login’, methods=[‘GET’, ‘POST’])
def login():

form = LoginForm()

if form.validate_on_submit():

user = User.query.filter_by(username=form.username.data).first()

if user and pbkdf2_sha256.verify(form.password.data, user.password):
login_user(user)
return redirect(url_for(‘home’))

return render_template(‘login.html’, form=form)

To keep users logged in across sessions, you can use the Flask-Login extension. It provides a login_user() function to log users in, and current_user to access the logged in user object in views.

Import Flask-Login, set up the LoginManager, and define a UserMixin class to represent the user object:

python
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from flask_login import LoginManager, UserMixin, login_user, login_required, current_user

login_manager = LoginManager()
login_manager.init_app(app)

@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))

class User(UserMixin, db.Model):
#…

Now decorate any route that requires a logged in user with @login_required. The user will be redirected to login if not authenticated.

You also need to add a logout route:

python
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@app.route(‘/logout’)
@login_required
def logout():
logout_user()
return redirect(url_for(‘login’))

To recap, user authentication in Flask involves:

User models and database setup
Registration and login forms + routes
Password hashing
Flask-Login for session management
login_required decorator
Routes for logout

Following these steps allows you to manage users, restrict access to authenticated users only, and easily add additional authentication methods like OAuth in the future. With Flask and these extensions, user authentication can be implemented securely and scalably in any Flask application.

WHAT ARE SOME POTENTIAL CHALLENGES THAT STUDENTS MAY FACE WHEN CONDUCTING A CAPSTONE PROJECT

Students undertaking capstone projects for the first time may face a variety of challenges as they take on this large culminating project before graduating. Successful completion of a capstone project requires strong time management, research, writing, and presentation skills. It is a substantial undertaking that really tests students’ abilities before entering the workforce or continuing on to further study.

One of the biggest challenges students may face is effectively managing their time. Capstone projects require extensive research, data collection, analysis, and writing over the course of several months. Students have to balance the demands of the capstone with other responsibilities like coursework, extracurricular activities, employment, and their personal lives. Poor time management is a common pitfall that can cause stress and lead to delays in completion. Students need to set interim deadlines, prioritize tasks, and schedule work in block to stay on track.

Related to time management is the challenge of conducting in-depth and thorough research. Capstone projects demand that students explore their topics from many different angles to demonstrate a comprehensive understanding and analysis. Students have to identify relevant scholarly sources like peer-reviewed articles and reports, but also integrate professional publications, case studies, interviews and surveys to develop a robust literature review and framework. The research process takes time and persistence to uncover all necessary information and data. Students may struggle navigating library databases and sorting through more materials than expected.

Analysis of research findings can also prove difficult. Capstone projects require sophisticated analysis that applies theories and models. Students have to make sense of complex data, identify patterns and relationships, and draw logical conclusions. Strong quantitative, qualitative or mixed methodology skills are necessary. Some students find the scope of analysis intimidating or are confused about how deeply to interpret their results. Statistical analysis software and qualitative data management take time to learn.

Developing the structure and Organization of a lengthy capstone paper or report poses additional challenges. Students must create a clear introduction, thesis, body, and conclusion that flow cohesively. The section types and paper length will differ depending on the academic field and topic. Using proper citation formats, developing headings and subheadings, adhering to formatting guidelines and creating appendixes all take practice. The capstone writing process is an iterative one of drafting, revising, editing and proofreading that some students struggle with.

Choosing an appropriate and engaging presentation format for the capstone findings and getting comfortable publicly speaking are also hurdles. Multimedia, poster presentations and live demonstrations require technical skills that students may lack. Even an oral presentation may induce significant nerves for those uncomfortable with public speaking. Rehearsing, practicing responses to questions and communicating research passionately takes effort to prepare for what is typically the final stage of the capstone experience.

Finding a faculty advisor or project supervisor who is available, provides guidance and delivers constructive feedback presents an ongoing area of difficulty. Students want to find an engaged mentor invested in their success, but some end up frustrated by unresponsive or unhelpful advisors. Asking questions, setting regular meetings and clarifying expectations upfront helps promote a smooth advising relationship. Advisor changes or delays still occur outside a student’s control.

While immensely rewarding, the capstone project milestone demands that students push beyond their comfort zones. With diligent planning, time management, research rigor, analytical abilities, writing skills, technical proficiency, public presentation experience and advisor support, students can work to overcome these challenges. The capstone epitomizes demonstrating one’s depth of knowledge in a field of study upon the cusp of graduation or the next step in their education or career. Students who seek assistance and persist through setbacks gain transferable competencies well serving them in future endeavors.

WHAT ARE SOME POTENTIAL IMPLICATIONS OF THE FINDINGS ON ANTIBIOTIC RESISTANCE IN SOIL BACTERIA

The discovery of antibiotic resistance genes in soil bacteria is extremely significant as it indicates that antibiotic resistance exists naturally in the environment and has the potential to spread from environmental bacteria to human pathogens. Soil bacteria have been found to contain genes that provide resistance to virtually every class of antibiotic used in human and veterinary medicine today. These include genes for resistance to beta-lactams (penicillin, cephalosporins), quinolones, macrolides, trimethoprim, sulfonamides and even last resort antibiotics like vancomycin.

The presence of these genes in soil microbes that have no direct contact with clinical antibiotic use suggests that antibiotic resistance has evolved naturally in the environment long before the antibiotic era. It is believed that antibiotics have been naturally produced by some soil bacteria and fungi for millions of years as a defense against competition, and other microbes have developed resistance as a result. The natural reservoir of antibiotic resistance genes in the environment means that antibiotic resistance is an ancient and enduring phenomenon, and is therefore a challenge that is unlikely to be easily overcome.

A major public health implication is that resistance genes from soil and other environmental bacteria can spread to human pathogens. Gene transfer between different bacteria species occurs frequently in the environment through horizontal gene transfer mechanisms like conjugation, transduction and transformation. Pathogenic bacteria can acquire resistance determinants from non-pathogenic environmental bacteria through these processes. For example, soil bacteria have been found to be the source of resistance genes for newer antibiotics like vancomycin that have spread to disease-causing organisms like MRSA. Such spread of environmental resistance genes poses a serious threat as it can render our current antibiotics ineffective.

Another concern is that human activities are providing increased selective pressures that can further enhance the spread of resistance from environmental bacteria. The overuse and misuse of antibiotics in clinical medicine and massive antibiotic usage in agriculture selects for resistant bacteria and drives the proliferation of resistance genes in both pathogens and environmental bacteria alike. Agricultural use of antibiotics also leads to their entry into soil and water through manure application. This exposes more environmental bacteria directly to antibiotics and further enriches the pool of resistance determinants. activities such as the proliferation of CAFOs (concentrated animal feeding operations), the spread of antibiotic-resistant pathogens through agricultural runoff into waterways and floods, and the overall increase in global connectivity through travel and trade are accelerating the mixing of bacteria from different sources. These anthropogenic factors can potentially enhance the transfer of antibiotic resistance between environmental and pathogenic bacteria worldwide on a massive scale. Climate change may also influence the spread as changing temperature and rainfall patterns may affect the distribution of bacteria in the environment.

The long-term implications are alarming. If resistance proliferation and dissemination from environmental reservoirs continue unchecked, we may soon enter a post-antibiotic era where many life-saving modern medicines become ineffective against common infections. This can have devastating consequences for public health and the economy. It is already estimated that by 2050, antibiotic resistance could potentially cause 10 million annual deaths globally if no action is taken – more than cancer. We may also lose our ability to perform vital medical procedures that rely on antibiotic prophylaxis like organ transplants, cancer chemotherapy and surgery for high-risk infections if resistance spreads further.

The discovery of antibiotic resistance genes in native environmental microbes highlights the natural origins and immense reservoir of resistance that exists independently of human antibiotic usage. It is clear that anthropogenic activities are accentuating the spread of these resistance traits from environmental bacteria to human pathogens on a unprecedented global scale. Urgent coordinated action is needed to strengthen surveillance of antimicrobial resistance in different ecosystems as well as prudent antibiotic usage policies in medicine and agriculture to curb the rise and dissemination of resistant bacteria before our antibiotic armory becomes dangerously depleted.

HOW DO AR GLASSES AND HEADSETS COMPARE TO SMARTPHONES IN TERMS OF IMMERSIVE AR EXPERIENCES

When it comes to delivering truly immersive augmented reality (AR) experiences, AR glasses and headsets have distinct advantages over smartphones. While smartphones were the first major platform to bring AR to the consumer market and enable basic overlay of digital content on the real world, they have inherent limitations that prevent them from achieving the same levels of immersion as head-mounted displays (HMDs).

One of the most significant differences is the field of view (FOV) which refers to the extent of the real world that can be seen through the device. Smartphone FOVs are constrained by their small screen sizes, typically ranging from 5-6 inches diagonal. Even holding a phone at arms length only provides a FOV of 30-40 degrees. In contrast, HMDs are designed to fill more of the user’s natural FOV in order to fuse digital and physical scenes seamlessly. Current AR glasses like the Vuzix Blade have FOVs over 40 degrees, while advanced research prototypes are approaching human FOV levels of 180-220 degrees horizontal and 120-135 degrees vertical. A wider FOV is critical for convincing depth cues and peripheral awareness of blended environments.

Related to FOV is the optical resolution and pixel density needed to overlay graphics convincingly on the real world. Again smartphones are limited by their screens which top out around 450-500 pixels per inch (PPI), compared to next generation AR displays targeting 1000+ PPI. Higher resolutions are required to avoid the “screen-door effect” where individual pixels are visible, breaking the illusion. They also enable finer details and text in AR overlays. While smartphones can handle basic overlays, more complex 3D graphics and holograms will appear blurry or pixelated on phone displays.

Eye tracking is another differentiating feature that enhances immersion. Integrated eye tracking allows HMDs to track a user’s focus and line of sight, enabling new interactions like gaze-based controls and foveated rendering. Foveated rendering optimizes graphical fidelity based on where the user is looking for performance gains. For phones, crude eye tracking is possible through front cameras but precision is limited.

Input is also more natural and intuitive with HMDs. Most support 6 degrees of freedom (6DoF) head tracking which precisely tracks and renders virtual content anchored in 3D space. Users can intuitively look around objects from different angles. Phones are limited to 3DoF and gyros – they can’t perceive true 6DoF head movements. Touchscreens also don’t support gestures like pointing that are natural in AR. Motion controllers further expand interactivity for some HMDs.

Perhaps the biggest difference lies in the form factor itself. Being untethered to a phone frees hands for other tasks while seeing AR. They also provide a more private experience that can be used discreetly in public. In contrast, holding phones up is awkward, tiring on arms, and draws more attention from others. This limits long-term use cases for AR on phones to passive, short-form experiences. The hands-free and discreet nature of HMDs unlocks many productivity, educational, and social/collaborative AR applications.

On the technical side, HMDs provide far better thermal management due to their design. Phones can overheat quickly rendering graphics-intensive AR for extended periods due to thermal constraints of thin, tightly packed devices. For a truly immersive experience, consistent performance is required. Phones are great for short demos but aren’t suitable for applications requiring persistent compute resources from the device.

Connectivity is also more reliable with HMDs which will support high throughput WiFi 6 and 5G connections. Phones still depend on mobile data plans that vary by region and provider. Offline and low-latency AR is challenging on phones but better supported by HMD hardware. Battery life is much longer too, enabling all-day AR use cases versus a few hours maximum on phones.

While smartphones created mainstream awareness of AR, their inherent form factor limitations prevent truly immersive experiences on par with HMDs. Only head-mounted displays can provide the large field of view, high resolution optics, integrated input like gaze and gesture, 6DoF tracking, thermal performance, offline capability and all-day battery required for advanced AR applications. As optical and computational technologies progress, AR glasses and headsets will continue leaving smartphones behind in the pursuit of seamlessly blending digital imagery with the real world.

DO YOU HAVE ANY SUGGESTIONS FOR DATA ANALYTICS PROJECT IDEAS USING PYTHON

Sentiment analysis of movie reviews: You could collect a dataset of movie reviews with sentiment ratings (positive, negative) and build a text classification model in Python using NLP techniques to predict the sentiment of new reviews. The goal would be to accurately classify reviews as positive or negative sentiment. Some popular datasets for this are the IMDB dataset or Stanford’s Large Movie Review Dataset.

Predicting housing prices: You could obtain a dataset of housing sales with features like location, number of bedrooms/bathrooms, square footage, age of home etc. and build a regression model in Python like LinearRegression or RandomForestRegressor to predict future housing prices based on property details. Popular datasets for this include King County home sales data or Boston housing data.

Movie recommendation system: Collect a movie rating dataset where users have rated movies. Build collaborative filtering models in Python like Matrix Factorization to predict movie ratings for users and recommend unseen movies. Popular datasets include the MovieLens dataset. You could create a web app for users to log in and see personalized movie recommendations.

Stock market prediction: Obtain stock price data for companies over time along with other financial data. Engineer features and build classification or regression models in Python to predict stock price movements or trends. For example, predict if the stock price will be up or down on the next day. Popular datasets include Yahoo Finance stock data.

Credit card fraud detection: Obtain a credit card transaction dataset with labels indicating fraudulent or legitimate transactions. Engineer relevant features from the raw data and build classification models in Python to detect potentially fraudulent transactions. The goal is to accurately detect fraud while minimizing false positives. Popular datasets are the Kaggle credit card fraud detection datasets.

Customer churn prediction: Get customer data from a telecom or other subscription-based company including customer details, services used, payment history etc. Engineer relevant features and build classification models in Python to predict the likelihood of a customer churning i.e. cancelling their service. The goal is to target high-risk customers for retention programs.

Employee attrition prediction: Obtain employee records data from an HR department including demographics, job details, salary, performance ratings etc. Build classification models to predict the probability of an employee leaving the company. Insights can help focus retention efforts for at-risk employees.

E-commerce product recommendations: Collect e-commerce customer purchase histories and product metadata. Build recommendation models to suggest additional products customers might be interested in based on their purchase history and similar customers’ purchases. Popular datasets include Amazon product co-purchases data.

Travel destination recommendation: Get a dataset with customer travel histories, destination details, reviews etc. Engineer features around interests, demographics, past destinations visited to build recommendation models to suggest new destinations tailored for each customer.

Image classification: Obtain a dataset of labeled images for a classification task like recognizing common objects, animals etc. Build convolutional neural network models in Python using frameworks like Keras/TensorFlow to build very accurate image classifiers. Popular datasets include CIFAR-10, CIFAR-100 for objects, MS COCO for objects in context.

Natural language processing tasks like sentiment analysis, topic modeling, named entity recognition etc. can also be applied to various text corpora like news articles, social media posts, product reviews and more to gain useful insights.

These are some ideas that could be implemented as data analytics projects using Python and freely available public datasets. The goal is to apply machine learning techniques with an understandable business problem or use case in mind. With projects like these, students can gain hands-on experience in the entire workflow from data collection/wrangling to model building, evaluation and potentially basic deployment.