Full Stack AI Program
"Building AI-powered Web Apps – Flask, FastAPI, Streamlit with AI Models & Building an AI-Powered Chatbot."

📌 Course Overview

This course is designed to teach students how to develop AI-powered web applications using Flask, FastAPI, and Streamlit. By the end of the course, students will be able to integrate AI models into web applications, deploy them, and build an AI-powered chatbot.

🗂 Course Modules & Breakdown

1️⃣ Introduction to Full Stack AI Development

• Overview of Full Stack AI and its applications

• Understanding AI-powered web apps

• Choosing the right framework: Flask, FastAPI, or Streamlit?

• Course project overview: AI-powered chatbot & web apps

2️⃣ Python Foundations for AI Web Development

• Quick Python refresher (functions, OOP, virtual environments)

• Introduction to API calls and HTTP methods

• Working with Python web frameworks

3️⃣ Flask for AI-Powered Web Apps

• Setting up Flask & basic routing

• Creating API endpoints for AI models

• Handling form data & user input

• Integrating AI models into Flask applications

• Deploying a Flask app (Heroku, AWS, or Render)

4️⃣ FastAPI for High-Performance AI Applications

• Why FastAPI? Advantages over Flask

• Setting up FastAPI & creating endpoints

• Using Pydantic for data validation

• Async programming for AI-based applications

• Deploying FastAPI applications with Docker

5️⃣ Streamlit for AI Model Visualization

• Introduction to Streamlit for AI apps

• Building an interactive AI-powered dashboard

• Deploying AI models in Streamlit

• Hosting Streamlit apps on Streamlit Cloud

6️⃣ AI Model Integration in Web Apps

• Selecting an AI model (NLP, Computer Vision, Tabular Data)

• Loading pre-trained models (TensorFlow, PyTorch, Hugging Face)

• Custom AI model training & deployment

• Serving AI models via APIs (Flask & FastAPI)

7️⃣ Database & Cloud Storage for AI Apps

• Introduction to SQL (PostgreSQL, SQLite) & NoSQL (MongoDB, Firebase)

• Connecting web apps to databases

• Storing AI model outputs & user interactions

• Cloud storage for AI models (AWS S3, Google Drive)

8️⃣ Frontend Essentials for AI Web Apps

• Basics of HTML, CSS, JavaScript

• Using React or Vue.js for UI enhancements

• Integrating frontend with Flask, FastAPI, and Streamlit

• RESTful & GraphQL API calls from the frontend

9️⃣ Building an AI-Powered Chatbot

• Introduction to NLP & Chatbot architectures

• Using OpenAI’s GPT, Dialogflow, or Rasa for chatbot models

• Building a chatbot API with FastAPI/Flask

• Deploying the chatbot to Telegram, WhatsApp, or a Web App

🔟 Deployment & Scaling AI Web Apps

• Deploying on AWS, GCP, Azure

• Containerization with Docker

• Serverless deployment options

• CI/CD pipelines for AI applications

• Performance optimization & security best practices

👨‍🏫 Teaching Strategy

• Hands-on First Approach: Code-along exercises from Day 1

• Project-Based Learning: Build real-world AI-powered apps in each module

• Live Coding & Debugging: Encourage interactive sessions with live problem-solving

• Assignments & Capstone Projects: Practical implementations of AI models in web apps

• Collaboration & GitHub: Students work on GitHub repositories & contribute to projects

🚀 Course Outcome

By the end of this course, students will: ✅ Build and deploy AI-powered web applications using Flask, FastAPI, and Streamlit
✅ Integrate AI models (NLP, Computer Vision, etc.) into web apps
✅ Build, deploy, and scale an AI-powered chatbot
✅ Deploy AI applications on cloud platforms
✅ Have a portfolio of AI-powered web projects