Build a Generative AI Micro-SaaS App with Python & Streamlit

Name: Build a Generative AI Micro-SaaS App with Python & Streamlit
Rating: 4.5 (123 reviews)

Master SaaS with Python and Streamlit: Integrate AI, Deploy Globally, and Monetize Your Skills

Created byRyan Shrott

Last updated 2/2024

English

What you'll learn

Build a micro-SaaS app with Python & Streamlit
Integrate generative AI components into a SaaS app
Subscription management with Stripe and MongoDB
App deployment to cloud with custom domain
Multimodal generative AI with photos, audio, video
Integrate Data Science components into a SaaS app
Navigate and utilize AWS and OpenAI for advanced app functionalities
Landing pages, Marketing Bootstrapping, Email Campaigns
Leverage ChatGPT in your micro-SaaS
AWS S3 integration in a Streamlit app
Open source model deployment in cloud

Course content

7 sections • 50 lectures • 4h 46m total length

Introduction1:43
Learn to build a generative AI micro-SaaS with Python and Streamlit, deploying with MongoDB and Stripe. Build an AI tenant management system that analyzes tenants for rent pay risk.
Course Overview4:00
Explore six sections of building a generative AI micro-saas app with Python and Streamlit, including Streamlit basics, a chatgpt-like clone, an AI tenant portal, OpenAI integration, and deployment.
Advanced Project Showcase: Power of Streamlit with AI for a SaaS4:39
Discover how to build a full Streamlit-based SaaS with login, property pricing for Canada, dashboards, and generative AI features, including API calls, Stripe integration, and AWS S3 data storage.
Introduction to the Course Project: Automated Tenant Management Portal8:30

Why Streamlit?5:57
Setting up your development environment6:54
Set up your development environment with Miniconda or pip, create a Python 3.x environment, install Streamlit, configure VS Code, and run your first Streamlit app to launch the micro-SaaS project.
Interactivity in Streamlit: Widgets, Layout, Session State6:36
A basic slider widget.
A checkbox in the sidebar that toggles additional content.
A select box dropdown.
A date input widget placed in the sidebar.
A simple multi-page simulation using radio buttons.
Session states
Displaying Data: Tables, Charts, and Dynamic Content5:38
Notes & Explanations:
Displaying Tables: st.table() offers a way to display static tables. This is great for showcasing smaller sets of data without needing interactive capabilities.
Displaying DataFrames: st.write(df) or st.dataframe(df) can be used to display data frames in an interactive table format. This widget has capabilities like scrolling, which makes it ideal for larger datasets.
Built-in Line Chart: Streamlit comes with a basic charting capability using functions like st.line_chart(), st.bar_chart(), and st.area_chart().
Custom Visualizations: You can also integrate visualizations from libraries like Matplotlib and Seaborn. This allows for tailored visualizations that can be more intricate or specific than Streamlit's built-in chart capabilities.
Dynamic Content: Streamlit can embed and render various dynamic content types, including:
Markdown: With st.markdown(), you can embed markdown text directly into your Streamlit apps.
Images: st.image() lets you display images from various sources.
Videos: st.video() can be used to embed videos.
Building a ChatGPT-like clone with Streamlit and OpenAI7:48
This tutorial showcased a simple yet powerful way to build a ChatGPT-like application using Streamlit and OpenAI's API. The usage of session_state ensures that our chat remains interactive and stateful across reruns, providing a smooth user experience. As you can see, in just a few lines of code, we've built a robust chat interface for users to interact with GPT models.
Integrate Open Source Models into your chatbot11:05
Explains how to leverage open source inference platforms as an alternative to OpenAI/ChatGPT. Here we use Together AI and AnyScale.

App showcase, demo and features walkthrough6:45
Learn to build a minimal streamlit-based saas app with login authentication, MongoDB or any database, and stripe payments, including a summarize tool and translation.
Local code and environment setup2:01
Clone the sass repository from github, set up VS Code, install requirements with pip, and run streamlit home.py to test the app, while the video covers stripe and mongodb.
Stripe integration3:37
Learn to set up Stripe integration in test mode for a Python and Streamlit micro-SaaS, including secret keys, environment variables, and creating a payment link with price and recurrence.
MongoDB user database setup5:35
Set up the MongoDB integration with Atlas, obtain a free 512MB cluster and connection string, and configure it in your environment variables to connect your app.
Email verification server with FastAPI Part 119:24
Build a FastAPI email verification server that stores a user token and verified flag in MongoDB, sends verification links via smtp, and exposes endpoints on Railway.
Email verification local debug with FastAPI Part 29:30
Deploying to Cloud (Railway.io or Streamlit Cloud)4:33
Use Railway.io to deploy your app to cloud
Customize your Domain Name!2:33
How to customize your domain and next steps for this course
Course Check-in! Midway Point.0:39
Celebrate completing the cookbook section and reaching halfway through the course, then invite feedback on discord and request a five-star review to help improve the course.

Introduction to the app/technology we're building3:11
We're building an automated social media post maker for realtors.
Full demo of the app we're building12:12
Full walkthrough demo of Real Estate Social Media Automation app.
Setting up the development environment2:30
Create and activate a dedicated python virtual environment with venv (name it social) for the project, optionally use conda, then install requirements and set the VS Code interpreter.
Automated Facebook Post with GPT-4 Vision6:34
TikTok Video with GPT-4-V Vision and OpenAI Voices Client9:17
Showcases TikTok video tool that uses property details, address, and images to generate GPT-4 Vision script, turn it into OpenAI Voices audio, and assemble a video with grids and overlays.
Optimized SEO ALT Text for Images3:16
AI Photo Editing with Replicate.com and instruct-pix2pix5:39
AI Photo Upscaling & Contact Us Form4:23

Intro and Preamble to Course Project2:06
Technologies involved and AWS S3 setup1:34
Explore the technologies used in this project, including Python, Streamlit, MongoDB, Stripe, and AWS S3, and set up an S3 bucket to securely store tenant documents.
AWS S3 Secret Keys Setup2:11
Learn to create AWS IAM access keys and secret access keys to enable your Streamlit app to interact with S3, including pasting keys and bucket name into your config.
More technology setup: Google Tesseract, Poppler, Conda env5:12
Explaining Utility Functions/Method Part 15:12
Set up a generative AI micro-SaaS app with Python and Streamlit, installing dependencies, configuring S3, and extracting text from PDFs and images for AI workflows.
Explaining Utility Functions/Method (Mongo) Part 24:02
Learn how utils.py houses utility methods for MongoDB and S3 storage, including saving tenant data to a tenants collection, updating records by email, and tracking units tenants have applied for.
Explaining Utility Functions/Method Part 37:34
Coding the Main Tenant Application Portal6:34
Build a Streamlit tenant portal that collects applicant data and documents, generates a combined PDF, stores data, uploads to S3, and emails the application to tenant and landlord.
Deploy to Railway Part 1 (Review)2:09
Deploy your Streamlit app to railway via a GitHub project, configuring build and start commands, and secret keys in settings for a custom domain, including tenant portal and utils.
Deploy to Railway Part 2 (Poppler and Tesseract)2:02
Learn to deploy on railway by configuring railway.toml and nixpacks.toml, set up a Python environment with requirements.txt, and install tesseract-ocr and poppler-utils on Linux.

Intro to Management Portal and AI document analysis6:52
Implement the management portal to create listings and analyze tenants using AI, including an AI analysis chat and a tenant report powered by OpenAI API and S3-stored documents.
Technology setup: Conda, AWS S3, Stripe, OpenAI, MongoDB2:47
Utility methods and S3 Data Manipulation5:20
Explore utility methods for building a generative AI micro-SaaS app with Python and Streamlit, including S3 data manipulation, pre-signed URLs, and chat with documents via embed chain.
How to create the EmbedChain Chatbot7:29
Build an embed chain powered chatbot that summarizes uploaded documents with GPT-3.5 turbo, embeds the results, and caches the bot for efficient tenant chats.
Analyzing Tenant Documents with OpenAI API11:40
"Map Reduce": AI Analysis Summary of Summaries8:02
Chatbot with Tenants using EmbedChain8:05

Introduction to Landing Pages, Painpoint Marketing and Bootstrapping7:29
Identify pain points and craft landing pages that sell the problem, not the technology. Bootstrapping tips, A/B testing, and turning visitors into leads with cost-efficient landing pages.
Landing Pages with Carrd4:49
Email Marketing with EmailOctupus2:38
Create a Landing Page in 5 seconds with Mixo.io3:50
Marketing, Measuring, Bootstrapping11:02
Examples: Reddit Bootstrapping, Email Campaigns3:50

Requirements

Basic Python knowledge: Familiarity with Python programming is necessary to follow the course content effectively.

Description

Unlock the potential of Python and Streamlit to create and monetize your own Generative AI Micro-SaaS application. This comprehensive course takes you through the journey of building a fully-functional SaaS application, integrating cutting-edge generative AI components, and deploying it globally with a custom domain. You'll learn to manage subscriptions using Stripe and MongoDB, and harness the power of cloud platforms like AWS and OpenAI ChatGPT to enhance your application's capabilities.

Whether you're a Python developer, an aspiring entrepreneur, or a tech enthusiast eager to explore the realms of AI and cloud computing, this course will equip you with the practical skills and knowledge you need to succeed. You'll not only build your own project but also understand how to market it effectively using landing pages, email campaigns, and more.

With over 3 hours of video content, interactive coding exercises, and real-world projects, you'll gain hands-on experience that's directly transferable to building your own SaaS solutions. Dive into the world of SaaS with us and transform your ideas into reality!!

Course Content Overview:

Section 1: Course Introduction and Demos
- Introduction to the course and its objectives.
- Course overview and what to expect.
- Advanced project showcase demonstrating the power of Streamlit with AI for SaaS.
- Introduction to the course project: Automated Tenant Management Portal.
Section 2: Streamlit Basics
- Exploring the reasons behind choosing Streamlit for SaaS applications.
- Setting up your development environment for success.
- Diving into interactivity in Streamlit: Widgets, Layout, Session State.
- Displaying Data: How to effectively use Tables, Charts, and Dynamic Content.
- Building a ChatGPT-like chatbot clone with Streamlit and OpenAI.
- Open-Source LLM integration into your chatbot app w AnyScale or Together AI.
Section 3: Building a Streamlit Micro-SaaS: The Cookbook
- Walking through app showcases and discussing features.
- Local code and environment setup for efficient development.
- Integrating Stripe for subscription management.
- Setting up a MongoDB user database.
- Email verification with FastAPI and local debugging techniques.
- Deploying to Cloud (choices between Railway or Streamlit Cloud).
- Customizing your Domain Name for a professional touch.
Section 4: Integrating Multimodal Generative AI in SaaS Applications
- Introduction to Multimodal Generative AI in SaaS.
- Demo of Multimodal AI Features: Photos, Audio, Video.
- Setup for Multimodal AI Development: OpenAI and Replicate.
- Automated Social Media Posts with GPT-4 Vision.
- Generating TikTok Content with GPT-4-V Vision and Audio.
- SEO Optimization with AI-Generated ALT Text.
- Advanced AI Photo Editing Techniques.
- Enhancing Image Quality through AI Upscaling.

Section 5: Course Project: AI Tenant Management System (Part 1)
- Introduction and preamble to the complex course project.
- In-depth discussion on technologies involved and AWS S3 setup.
- Detailed walkthrough on setting up AWS S3 Secret Keys and additional technology setups.
- Coding the main Tenant Application Portal.
- Deployment strategies and reviews for Railway.
Section 6: Course Project: AI Tenant Management System (Part 2)
- Introduction to Management Portal and AI document analysis.
- Comprehensive guide on technology setup including Conda, AWS S3, Stripe, OpenAI, MongoDB.
- Detailed explanation of utility methods and S3 Data Manipulation.
- Creation of the EmbedChain Chatbot and analysis of tenant documents using OpenAI API.
- Implementing "Map Reduce": AI Analysis Summary of Summaries and chatbot interactions.
Section 7: Bonus: Landing Page Creation, Pain Points, Bootstrapping, Email Marketing
- Introduction to Landing Pages, identifying Pain points, and Bootstrapping strategies.
- Practical guide to creating landing pages with Carrd and Mixo.
- Effective Email Marketing with EmailOctopus..
- Real-world marketing, measuring results, and bootstrapping examples including Reddit campaigns and email strategies.

Join us on this comprehensive journey to build and scale your very own Generative AI Micro-SaaS App with Python & Streamlit. Take the first step towards becoming a successful SaaS entrepreneur today!

Who this course is for:

Python developers who want to quickly build a SaaS app
Streamlit users who want to monetize their app
Aspiring entrepreneurs interested in SaaS and AI
Programmers seeking to learn about cloud deployment and subscription management
Tech enthusiasts eager to explore generative AI and cloud

Build a Generative AI Micro-SaaS App with Python & Streamlit

What you'll learn

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Course content

Course Introduction and Demos4 lectures • 19min

Streamlit Basics and Chatbot Builder BootCamp6 lectures • 44min

Building a Streamlit Micro-SaaS: The Cookbook9 lectures • 55min

Multimodal Gen-AI with photos, audio and video with OpenAI and Replicate.com8 lectures • 47min

Course Project: AI Tenant Management System (Part 1)10 lectures • 39min

Course Project: AI Tenant Management System (Part 2)7 lectures • 50min

Additional Content: Landing Page Creation, Pain Point Marketing, Bootstrapping6 lectures • 34min

Requirements

Description

Who this course is for: