https://github.com/saadsalmanakram/streamlit-masterclass
https://github.com/saadsalmanakram/streamlit-masterclass
streamlit streamlit-application streamlit-component streamlit-dashboard streamlit-webapp
Last synced: 8 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/saadsalmanakram/streamlit-masterclass
- Owner: saadsalmanakram
- License: apache-2.0
- Created: 2024-09-07T12:58:13.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-28T05:14:51.000Z (9 months ago)
- Last Synced: 2025-01-28T06:22:47.931Z (9 months ago)
- Topics: streamlit, streamlit-application, streamlit-component, streamlit-dashboard, streamlit-webapp
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
---
# 🚀 Streamlit Masterclass - Build & Deploy Data Apps Effortlessly
## 📝 Introduction
**Streamlit** is an open-source Python library that makes it easy to build and deploy interactive **data applications** with minimal effort. This repository serves as a **comprehensive guide** to mastering Streamlit, covering everything from **basic UI components** to **advanced data visualization, machine learning integration, and cloud deployment**.
📌 **Learn to create data-driven apps with Python**
📌 **Explore interactive widgets, dashboards, and APIs**
📌 **Integrate ML models and real-time data visualization**
📌 **Deploy Streamlit apps on cloud platforms (Streamlit Sharing, Hugging Face Spaces, AWS, etc.)**
---
## 🚀 Features
- 🏗 **Step-by-step tutorials** for beginners and advanced users
- 🖥 **Interactive dashboards** and UI components
- 📊 **Real-time data visualization** using Matplotlib, Plotly, Altair
- 🤖 **Machine learning model integration** with Scikit-learn & TensorFlow
- 🌍 **Deploy Streamlit apps** on various cloud platforms
- 📝 **Jupyter notebooks** with hands-on examples
---
## 📌 Prerequisites
Make sure you have:
- **Python 3.x** → [Download Here](https://www.python.org/downloads/)
- **Streamlit** installed (`pip install streamlit`)
- Basic knowledge of Python & Data Science (Pandas, NumPy, Matplotlib)
---
## 📂 Repository Structure
```
Streamlit-Masterclass/
│── examples/ # Streamlit app examples
│── notebooks/ # Jupyter notebooks for hands-on learning
│── components/ # Custom Streamlit UI components
│── ml_integration/ # ML model deployment with Streamlit
│── deployment/ # Guides for Streamlit deployment
│── README.md # Documentation
└── requirements.txt # Python dependencies
```
---
## 🏆 Getting Started
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/saadsalmanakram/Streamlit-Masterclass.git
cd Streamlit-Masterclass
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run a Sample Streamlit App
```bash
streamlit run examples/basic_app.py
```
---
## 🔍 Topics Covered
### 🎨 **Streamlit Basics**
- Creating a basic **Streamlit app**
- Using widgets like sliders, buttons, and checkboxes
- Displaying text, images, and tables
### 📊 **Data Visualization with Streamlit**
- Using **Matplotlib, Plotly, Seaborn, Altair**
- Interactive dashboards with real-time updates
- Displaying **charts, maps, and progress bars**
### 🤖 **Integrating Machine Learning Models**
- Loading and deploying **Scikit-learn & TensorFlow models**
- Creating a **Streamlit-powered ML Web App**
- **Real-time predictions** with user input
### 🚀 **Deploying Streamlit Apps**
- Deploying on **Streamlit Cloud**
- Hosting on **Hugging Face Spaces & AWS**
- CI/CD for automated Streamlit deployments
---
## 🚀 Example Code
### 🎨 **Basic Streamlit App**
```python
import streamlit as st
st.title("Hello, Streamlit! 🚀")
st.write("This is a simple web app powered by Streamlit.")
name = st.text_input("Enter your name:")
if name:
st.write(f"Hello, {name}!")
```
### 📊 **Data Visualization Example**
```python
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({
"Category": ["A", "B", "C", "D"],
"Values": [10, 20, 30, 40]
})
st.bar_chart(df.set_index("Category"))
```
### 🤖 **Machine Learning Model in Streamlit**
```python
import streamlit as st
import pickle
model = pickle.load(open("ml_model.pkl", "rb"))
st.title("ML Model Deployment with Streamlit")
input_value = st.number_input("Enter a value:")
if st.button("Predict"):
prediction = model.predict([[input_value]])
st.write(f"Prediction: {prediction}")
```
---
## 🏆 Real-World Applications
📌 **Data dashboards & reports**
📌 **AI/ML model deployment**
📌 **Real-time data visualization**
📌 **Stock market & financial analytics**
📌 **NLP-based text analysis apps**
---
## 🏆 Contributing
Contributions are welcome! 🚀
🔹 **Fork** the repository
🔹 Create a new branch (`git checkout -b feature-name`)
🔹 Commit changes (`git commit -m "Added new Streamlit widget"`)
🔹 Push to your branch (`git push origin feature-name`)
🔹 Open a pull request
---
## 📜 License
This project is licensed under the **MIT License** – feel free to use, modify, and share the code.
---
## 📬 Contact
📧 **Email:** saadsalmanakram1@gmail.com
🌐 **GitHub:** [SaadSalmanAkram](https://github.com/saadsalmanakram)
💼 **LinkedIn:** [Saad Salman Akram](https://www.linkedin.com/in/saadsalmanakram/)
---
⚡ **Master Streamlit & Build Interactive Data Apps!** ⚡
---