Python for AI: Create AI Apps with Flask & OpenAI
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- Live or Self-paced
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Book live on Noble DesktopSummary
Harness the power of AI for web development. Whether you're looking to enhance your existing Python development skills or break into the world of AI-powered apps, this hands-on class is designed to equip you with the knowledge and tools you need to succeed, and have you confidently build dynamic web applications using the Flask framework.
You'll start by learning the basics of Flask, including how to set up your project, create routes, and render HTML templates. From there, you'll dive into web development essentials such as designing and styling your app with HTML and CSS, building forms for user input, and handling requests to make your app more interactive and user-friendly. But we don’t stop at the basics—this course takes your skills to the next level by integrating the OpenAI API into your Flask app. You'll learn how to make API calls for tasks like sentiment analysis, and display results dynamically in your web app. The class will also cover essential topics like error handling, debugging, and deploying your app. By the end of the course, you'll be able to create a smooth and successful Python development experience with AI integration.
Prerequisites
This course requires a basic to intermediate understanding of Python programming. Students without prior experience should first take our Python for Data Science Bootcamp or Python Programming Bootcamp.
Curriculum
What you'll learn
- Set up Flask projects and create routes for web applications
- Handle GET and POST requests, and render HTML templates in Flask
- Configure and make OpenAI API requests for sentiment analysis tasks
- Design and style HTML forms, and apply CSS to your web apps
- Implement error handling and debug common issues in Flask applications
- Integrate AI models like GPT-4 into web applications using APIs
Course syllabus
Introduction to Flask
- Overview of Flask and its use cases
- Setting up a Flask project
- Creating and running your first Flask app
Building the Web Application
- Designing the HTML structure and creating templates
- Styling the app with CSS
- Creating Flask routes for handling requests and rendering templates
Integrating the OpenAI API
- Setting up the OpenAI API and acquiring an API key
- Making API calls to perform sentiment analysis
- Parsing and displaying the API's results in your web app
Handling User Input
- Building forms in HTML to capture user input
- Processing form submissions in Flask
- Displaying dynamic content based on user input
Error Handling & Debugging
- Implementing error handling to catch and manage potential issues
- Debugging common errors and understanding Flask's error messages
Deploying & Testing
- Running the Flask app locally and testing its functionality
- Reviewing the complete workflow of submitting a comment and receiving sentiment analysis results
What's included
- Free course retake within one year to refresh the material and gain practice.
- Class recordings
Stated by the provider. Confirm what your tuition covers before enrolling.
Live classes
Choose dates and book your instructor-led class on nobledesktop.com.
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
-
–
In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
-
–
In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
-
–
In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
-
–
In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
Confirm current dates and availability on Noble Desktop before booking.
Self-paced course
Learn through recorded lessons on your own schedule.
This course is available for 30 days. You can choose when to start your access period. Once you activate, you will have 30 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
- Tuition
- $1,895
- Course length
- 30 hours
- Schedule
- On your schedule
Harness the power of AI for web development. Whether you're looking to enhance your existing Python development skills or break into the world of AI-powered apps, this hands-on class is designed to equip you with the knowledge and tools you need to succeed, and have you confidently building dynamic web applications using the Flask framework.
You'll start by learning the basics of Flask, including how to set up your project, create routes, and render HTML templates. From there, you'll dive into web development essentials such as designing and styling your app with HTML and CSS, building forms for user input, and handling requests to make your app interactive and user-friendly. But we don’t stop at the basics — this course takes your skills to the next level by integrating the OpenAI API into your Flask application. You'll learn how to make API calls for tasks like sentiment analysis and display results dynamically in your web app. The class will also cover essential topics like error handling, debugging, and deploying your app. By the end of the course, you'll be able to create a smooth and successful Python development experience with AI integration.
Self-paced prerequisites
This course requires a basic to intermediate understanding of Python programming. Students without prior experience should first take our Python for Data Science Bootcamp or Python Programming Bootcamp.
Self-paced curriculum
What you'll learn self-paced
- Set up Flask projects and create routes for web applications
- Handle GET and POST requests, and render HTML templates in Flask
- Configure and make OpenAI API requests for sentiment analysis tasks
- Design and style HTML forms, and apply CSS to web apps
- Implement error handling and debug common issues in Flask applications
- Integrate AI models like GPT-4 into web applications using APIs
Self-paced syllabus
Introduction to Flask
- Overview of Flask and its use cases
- Setting up a Flask project
- Creating and running your first Flask app
Building the Web Application
- Designing the HTML structure and creating templates
- Styling the app with CSS
- Creating Flask routes for handling requests and rendering templates
Integrating the OpenAI API
- Setting up the OpenAI API and acquiring an API key
- Making API calls to perform sentiment analysis
- Parsing and displaying the API's results in your web app
Handling User Input
- Building forms in HTML to capture user input
- Processing form submissions in Flask
- Displaying dynamic content based on user input
Error Handling & Debugging
- Implementing error handling to catch and manage potential issues
- Debugging common errors and understanding Flask's error messages
Deploying & Testing
- Running the Flask app locally and testing its functionality
- Reviewing the complete workflow of submitting a comment and receiving sentiment analysis results
What's included with self-paced
- Self-paced video lessons
Self-paced lessons
Watch free previews and explore the lessons included with enrollment at Noble Desktop.
Python for AI Course Online (Self-Paced)
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Getting Started
Lesson 1: Using VSCode & Flask
10:00
Build web applications using Python’s Flask framework and the OpenAI API, starting from core concepts and assuming foundational Python programming knowledge, while setting up VS Code and an OpenAI account.
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Enrollment required
Set Up VS Code for Flask
Lesson 1: Using VSCode & Flask
37:40
Create a new VS Code project, set up a virtual environment, install Flask, and run a basic Hello World app in the browser.
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Add an 'About' Route
Lesson 1: Using VSCode & Flask
11:41
Add a new Flask route, update the homepage to link to it, and test changes by restarting the development server.
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Flask for Rendering
Lesson 2: Rendering HTML with Flask
6:21
Render full HTML pages with Flask by using render_template, creating an index.html file inside a templates folder, and updating routes to return HTML instead of plain text.
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Render an About Page
Lesson 2: Rendering HTML with Flask
7:47
Build an About page by defining a new route, rendering an HTML template, and linking to the route path rather than the file name.
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Enrollment required
Connecting to OpenAI
Lesson 3: Connecting with OpenAI
36:38
Connect a Flask app to the OpenAI API by installing the OpenAI module, configuring an API key, sending a request to a GPT model, parsing the JSON response, and displaying the AI output in the browser.
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Enrollment required
Prompting AI to Respond in JSON
Lesson 3: Connecting with OpenAI
27:00
Prompt the AI to return structured JSON responses, parse them into Python dictionaries, and extract specific data fields for use in the app.
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Jinja Templating Engine for HTML
Lesson 4: Jinja and Prompt Engineering
15:00
Use Jinja templating to dynamically display AI-generated content inside HTML pages rendered by Flask.
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Prompt Engineering With JSON and Jinja Part 1
Lesson 4: Jinja and Prompt Engineering
42:21
Identify and resolve JSON prompt errors caused by curly quotes or invalid characters in Python code.
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Prompt Engineering With JSON and Jinja Part 2
Lesson 4: Jinja and Prompt Engineering
22:14
Fix malformed JSON by correcting quotation marks, adding missing commas, and validating syntax before parsing.
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Having an Ongoing Chat with AI
Lesson 5: Building the Chat
15:00
Implement a terminal-based AI chat function that maintains conversation context using a list, processes user input in a loop, and appends AI responses sequentially.
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Coding with HTML and CSS Part 1
Lesson 6: Coding HTML & CSS
13:37
Design a browser-based chat interface with HTML and CSS by structuring content with semantic elements, organizing assets in static folders, styling user and AI messages, and adding a hero image.
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Coding with HTML and CSS Part 2
Lesson 6: Coding HTML & CSS
21:59
Add interactive input fields and buttons to the chat interface and prevent page reloads through JavaScript event handling.
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Javascript for Send Button
Lesson 7: Using Javascript
15:49
Implement JavaScript logic to capture user input, create chat message elements, and append them dynamically to the chat window.
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Enrollment required
Javascript fetch() for Server Requests
Lesson 7: Using Javascript
17:33
Introduce JavaScript fetch by retrieving data from a public API and displaying results on the page in response to user interaction.
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JavaScript fetch() Request for Flask
Lesson 7: Using Javascript
17:10
Fetch JSON data from custom Flask routes, process responses in JavaScript, and update page content dynamically.
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Fetch & Python Flask Chat Round Trip
Lesson 8: Improving the Chat
15:00
Send chat messages from the browser to a Flask server using fetch, process them server-side, and return simulated AI responses to the UI.
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Completing the AI Chat Assistant Part 1
Lesson 8: Improving the Chat
18:39
Integrate full chat workflows by sending conversation history as JSON to Flask, forwarding it to the OpenAI API, storing responses, and updating the chat interface in real time.
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Completing the AI Chat Assistant Part 2
Lesson 8: Improving the Chat
26:13
Debug JSON handling issues in an AI chat system and improve response quality by incorporating structured FAQ context.
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AI Prompt Engineering with Product FAQ
Lesson 8: Improving the Chat
27:17
Load and manage FAQ content within a Flask application to enhance an AI assistant’s ability to answer product- or service-specific questions accurately.
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Making a File Chooser in HTML & JS
Lesson 9: Starting the Meal Analyzer
12:34
Create an image upload feature by configuring a virtual environment, building HTML and JavaScript upload forms, and displaying uploaded images using Flask.
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Sending Image Data to Flask and Returning Temp URL
Lesson 9: Starting the Meal Analyzer
34:34
Send uploaded image data to Flask, generate a temporary file path, and render the image back in the browser.
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Sending Image Data to Flask and Returning Temp URL Part 2
Lesson 9: Starting the Meal Analyzer
24:58
Encode uploaded images in Base64, send them to the OpenAI API, and process JSON responses for display in the application.
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Sending Image Data to Flask and Returning Temp URL Part 3
Lesson 9: Starting the Meal Analyzer
26:35
Apply prompt engineering techniques to request structured AI analysis of images, such as extracting meal details from uploaded photos.
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Securing API Key as Environment Variable
Lesson 10: Finalizing the Meal Analyzer
16:18
Secure OpenAI API credentials by storing them as environment variables in a .env file and loading them with python-dotenv to prevent accidental exposure in version control.
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