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FR-AI / ANALYSISWhy it is worth attention
It provides a clear, step-by-step learning roadmap for Streamlit fundamentals, covering 13 distinct features with ready-to-run example scripts, and explicitly outlines future integration plans with FastAPI, ML, Docker, and AWS.
Who it is for
- Python beginners who want to build interactive web apps
- Data scientists and analysts exploring Streamlit for dashboards
- Students learning Python web development
- Developers preparing to integrate Streamlit with FastAPI and ML
Use cases
- Learning Streamlit UI components and widgets through practical examples
- Building simple interactive web applications for data visualization
- Prototyping dashboards with charts, progress bars, and status messages
- Setting up a foundation for future FastAPI and machine learning projects
Strengths
- Covers 13 core Streamlit topics with dedicated, runnable Python scripts
- Includes a clear learning roadmap and future project ideas (e.g., BMI Calculator, Weather UI)
- Provides media handling examples (image, audio, video) out of the box
- Repository is open source, well-structured, and encourages contributions
Considerations
- Only covers introductory Streamlit features; no advanced topics like caching, session state, or component customization
- No real-world application or deployment examples included yet
- Repository relies on placeholder media files (image.jpg, audio.mp3, video.mp4) that need to be supplied by the user
README quick start
Streamlit-Learning-Projects
A beginner-friendly Streamlit learning repository covering UI components, widgets, layouts, media handling, charts, and interactive web application development using Python.
🚀 Streamlit Learning Journey
📖 About This Repository
This repository documents my Streamlit learning journey.
The goal is to master Streamlit fundamentals while building interactive Python web applications that will later be integrated with FastAPI, Machine Learning models, Docker, and AWS.
Every file in this repository represents a specific Streamlit concept that I have learned through hands-on practice.
🎯 Learning Roadmap
Python
│
▼
Streamlit ✅
│
▼
FastAPI
│
▼
Machine Learning Integration
│
▼
Docker
│
▼
AWS Deployment
📂 Repository Structure
📦 Streamlit-Basics
├── 📄 title_markdown_header.py
├── 📄 Input_Widgets.py
├── 📄 Sidebar_and_Containers.py
├── 📄 Display_Graphs.py
├── 📄 Prog_and_Status.py
├── 📄 Image_Audio_Video.py
├── 🖼 image.jpg
├── 🎵 audio.mp3
├── 🎥 video.mp4
├── 📄 requirements.txt
└── 📘 README.md
🚀 Topics Covered
| Feature | Status |
|---|---|
| Titles & Headers | ✅ |
| Markdown | ✅ |
| Text Display | ✅ |
| Input Widgets | ✅ |
| Sidebar | ✅ |
| Containers | ✅ |
| Columns | ✅ |
| Images | ✅ |
| Audio | ✅ |
| Video | ✅ |
| Charts | ✅ |
| Progress Bars | ✅ |
| Status Messages | ✅ |
🛠 Technologies Used
💡 Future Mini Projects
- 🧮 BMI Calculator
- 🌦 Weather Prediction UI
- 📋 Todo App
- 💰 Expense Tracker
- 📝 Notes App
- 📄 Resume Builder
- 📊 Dashboard Projects
▶️ Run Locally
Clone the repository
git clone https://github.com/yourusername/streamlit-basics.git
Move inside the project
cd streamlit-basics
Install dependencies
pip install -r requirements.txt
Run Streamlit
streamlit run Input_Widgets.py
🌟 Why This Repository?
✔ Learn Streamlit Fundamentals
✔ Practice Interactive UI Development
✔ Build a Foundation for ML Applications
✔ Prepare for FastAPI Integration
✔ Improve Python Development Skills
📈 GitHub Stats
🤝 Connect With Me
⭐ Thank you for visiting!
Description
A beginner-friendly Streamlit learning repository covering UI components, widgets, layouts, media handling, charts, and interactive web application development using Python.
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