Remove emojis and symbols from all files, update Dockerfile

Dockerfile

@@ -7,6 +7,7 @@ WORKDIR /app
 RUN apt-get update && apt-get install -y \
     libgl1 \
     libglib2.0-0 \
+    curl \
     && rm -rf /var/lib/apt/lists/*
 
 # Copy requirements first for better caching

GETTING_STARTED.md

@@ -1,8 +1,8 @@
-# 🚀 Getting Started with the Sampling & Quantization Demo
+#  Getting Started with the Sampling & Quantization Demo
 
 Welcome! This guide will help you get the demo up and running in just a few minutes.
 
-## 📋 Prerequisites
+## Prerequisites
 
 - **Python 3.11+** (Python 3.9+ should also work)
 - **Git** (for cloning the repository)
@@ -13,7 +13,7 @@ Optional:
 - **Make** (for convenient commands)
 - **Docker** (for containerized deployment)
 
-## 🏃‍♂️ Quick Start (30 seconds)
+## ‍️ Quick Start (30 seconds)
 
 The absolute fastest way to get started:
 
@@ -30,7 +30,7 @@ cd sampling-quantization
 
 That's it! Your browser should open to `http://localhost:8501`
 
-## 📖 Detailed Installation
+## Detailed Installation
 
 ### Step 1: Clone the Repository (if needed)
 
@@ -50,10 +50,10 @@ cd sampling-quantization
 ```
 
 This will:
-- ✅ Create a virtual environment
-- ✅ Install all dependencies
-- ✅ Verify your Python version
-- ✅ Run basic checks
+- Create a virtual environment
+- Install all dependencies
+- Verify your Python version
+- Run basic checks
 
 #### Option B: Manual Setup
 
@@ -107,7 +107,7 @@ http://localhost:8501
 
 If it doesn't open automatically, manually navigate to that URL.
 
-## ✅ Verify Installation
+## Verify Installation
 
 Run the test script to make sure everything is working:
 
@@ -121,10 +121,10 @@ python test_setup.py
 
 You should see:
 ```
-✅ All tests passed! Ready to run the demo.
+ All tests passed! Ready to run the demo.
 ```
 
-## 🎨 First Steps in the Demo
+## First Steps in the Demo
 
 Once the app is running:
 
@@ -153,7 +153,7 @@ Once the app is running:
    - Read the explanations
    - Experiment with different settings
 
-## 🔧 Troubleshooting
+## Troubleshooting
 
 ### Problem: "Python not found"
 
@@ -212,7 +212,7 @@ sudo apt-get install libgl1 libglib2.0-0
 - Close other browser tabs
 - Check your internet connection (if loading external images)
 
-## 📚 Next Steps
+## Next Steps
 
 ### For Students
 - Work through the "Educational Insights" tabs
@@ -231,7 +231,7 @@ sudo apt-get install libgl1 libglib2.0-0
 - Check out PROJECT_SUMMARY.md for technical details
 - See IMAGES.md for working with custom images
 
-## 🌐 Deploying to Hugging Face Spaces
+## Deploying to Hugging Face Spaces
 
 Want to share this with others online?
 
@@ -254,7 +254,7 @@ Follow the prompts and enter your Space name (e.g., "username/sampling-demo").
 
 Your demo will be live at: `https://huggingface.co/spaces/username/sampling-demo`
 
-## 🐳 Docker Deployment (Advanced)
+## Docker Deployment (Advanced)
 
 If you prefer Docker:
 
@@ -268,7 +268,7 @@ docker run -p 8501:8501 sampling-demo
 # Access at http://localhost:8501
 ```
 
-## 📝 Common Commands Reference
+## Common Commands Reference
 
 ```bash
 # Setup and installation
@@ -293,7 +293,7 @@ make deploy           # Alternative using make
 make clean            # Remove virtual environment
 ```
 
-## 🎓 Learning Resources
+## Learning Resources
 
 Before diving in, you might want to review:
 - Digital image representation basics
@@ -320,10 +320,10 @@ Good starting points:
 - **Discussions**: Ask questions in GitHub Discussions
 - **Email**: Contact the course instructor
 
-## ✨ Success!
+## Success!
 
-If you made it here and the app is running, congratulations! 🎉
+If you made it here and the app is running, congratulations! 
 
 You're ready to explore the fascinating world of image sampling and quantization.
 
-**Enjoy the demo!** 🎨
+**Enjoy the demo!** 

IMAGES.md

@@ -161,11 +161,11 @@ This ensures the demo works even without internet access or external dependencie
 ## Testing Your Images
 
 Before using an image in production, test:
-- ✅ Does it load quickly?
-- ✅ Are effects visible at different sampling rates?
-- ✅ Does quantization create noticeable banding?
-- ✅ Are JPEG artifacts visible at low quality?
-- ✅ Is the file size reasonable (<5MB)?
+- Does it load quickly?
+- Are effects visible at different sampling rates?
+- Does quantization create noticeable banding?
+- Are JPEG artifacts visible at low quality?
+- Is the file size reasonable (<5MB)?
 
 ## Need Help?
 

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2025 Image Analysis Course
+Copyright (c) 2025 Uni Bern Intro to Image Analysis Course
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

PROJECT_SUMMARY.md

@@ -1,4 +1,4 @@
-# 🎨 Image Sampling and Quantization Demo - Project Summary
+#  Image Sampling and Quantization Demo - Project Summary
 
 ## Overview
 
@@ -7,7 +7,7 @@ This interactive educational demo teaches fundamental concepts in digital image
 - **Quantization**: How bit depth impacts color representation
 - **Compression**: Differences between PNG (lossless) and JPEG (lossy)
 
-## ✨ Key Features
+## Key Features
 
 ### Interactive Controls
 - **Sampling Grid Size Slider** (1-16x): Shows pixelation effect and pixel count reduction
@@ -24,7 +24,7 @@ This interactive educational demo teaches fundamental concepts in digital image
 - **Visual Examples**: All concepts demonstrated with live image processing
 - **Formula Explanations**: Mathematical basis for all calculations
 
-## 🎯 Educational Objectives
+## Educational Objectives
 
 Students will learn:
 1. The relationship between sampling rate and image resolution
@@ -33,7 +33,7 @@ Students will learn:
 4. Differences between lossy and lossless compression
 5. How JPEG blocking artifacts occur
 
-## 📁 Project Structure
+## Project Structure
 
 ```
 sampling-quantization/
@@ -60,7 +60,7 @@ sampling-quantization/
     └── check_status.sh    # Check deployment status
 ```
 
-## 🚀 Quick Start
+## Quick Start
 
 ### For Users (Simplest)
 ```bash
@@ -82,7 +82,7 @@ pip install -r requirements.txt
 streamlit run app.py
 ```
 
-## 🌐 Deployment Options
+## Deployment Options
 
 ### 1. Hugging Face Spaces (Recommended)
 ```bash
@@ -104,7 +104,7 @@ docker run -p 8501:8501 sampling-demo
 streamlit run app.py --server.port 8501
 ```
 
-## 🎓 Teaching with This Demo
+## Teaching with This Demo
 
 ### Suggested Lesson Plan
 
@@ -116,14 +116,14 @@ streamlit run app.py --server.port 8501
 
 **Part 2: Quantization (15 minutes)**
 1. Reset sampling to 1x
-2. Reduce bits per pixel (8 → 4 → 2 → 1)
+2. Reduce bits per pixel (8 to 4 to 2 to 1)
 3. Discuss: Color banding, posterization
 4. Show grayscale interpretation
 
 **Part 3: Compression (20 minutes)**
 1. Apply moderate sampling/quantization
 2. Compare PNG vs JPEG at different qualities
-3. At JPEG quality < 30: Point out 8×8 blocking
+3. At JPEG quality < 30: Point out 8x8 blocking
 4. Discuss use cases for each format
 
 **Part 4: Interactive Exploration (10 minutes)**
@@ -136,16 +136,16 @@ streamlit run app.py --server.port 8501
 1. What is the minimum acceptable sampling rate for your use case?
 2. How many bits per pixel do you really need for grayscale medical images?
 3. When would you choose PNG over JPEG and vice versa?
-4. Why do JPEG artifacts appear in 8×8 blocks?
+4. Why do JPEG artifacts appear in 8x8 blocks?
 5. What's the total storage for 1000 photos at different settings?
 
-## 🔧 Technical Details
+## Technical Details
 
 ### Image Processing Pipeline
 ```
 Original Image
     ↓
-Spatial Sampling (downsample → upsample with nearest neighbor)
+Spatial Sampling (downsample to upsample with nearest neighbor)
     ↓
 Quantization (reduce bits per channel)
     ↓
@@ -156,10 +156,10 @@ Display + File Size Calculation
 
 ### File Size Calculation
 ```
-Raw Size = (Width / Sampling) × (Height / Sampling) × Channels × Bits / 8
+Raw Size = (Width / Sampling) x (Height / Sampling) x Channels x Bits / 8
 
-PNG Size ≈ Raw Size × 0.7  (typical compression ratio)
-JPEG Size ≈ Raw Size × 0.3  (typical compression ratio)
+PNG Size ≈ Raw Size x 0.7  (typical compression ratio)
+JPEG Size ≈ Raw Size x 0.3  (typical compression ratio)
 ```
 
 ### Key Algorithms
@@ -168,7 +168,7 @@ JPEG Size ≈ Raw Size × 0.3  (typical compression ratio)
 - **Quantization**: `floor(value / step) * step` where `step = 256 / 2^bits`
 - **JPEG**: OpenCV's `cv.imencode()` with quality parameter
 
-## 🎨 Customization
+## Customization
 
 ### Change Default Image
 Edit `load_sample_image()` in `app.py`:
@@ -198,21 +198,21 @@ See `CONTRIBUTING.md` for guidelines on adding:
 - Interactive exercises
 - Batch processing
 
-## 📊 Performance
+## Performance
 
 - **Load Time**: < 2 seconds on first load (with caching)
 - **Interactive Response**: Real-time (< 100ms per slider change)
 - **Memory Usage**: ~200-300 MB (depends on image size)
 - **Supported Image Sizes**: Up to 4K (auto-resized to 512px for demo)
 
-## 🐛 Known Limitations
+## Known Limitations
 
 1. Very large images (>10MB) may be slow - auto-resized to 512px
 2. JPEG artifact visibility depends on image content
 3. File size estimates are approximate (actual compression varies)
 4. Generated sample image is simple (encourage uploading real images)
 
-## 📚 Educational Resources
+## Educational Resources
 
 Concepts covered align with:
 - Digital Image Processing (Gonzalez & Woods)
@@ -220,7 +220,7 @@ Concepts covered align with:
 - Signal processing curricula
 - Compression theory courses
 
-## 🤝 Contributing
+## Contributing
 
 We welcome contributions! See `CONTRIBUTING.md` for:
 - Bug reports
@@ -229,32 +229,30 @@ We welcome contributions! See `CONTRIBUTING.md` for:
 - Documentation improvements
 - Educational content enhancements
 
-## 📄 License
+## License
 
 MIT License - Free for educational and commercial use
 
-## 🙏 Acknowledgments
+## Acknowledgments
 
 - Inspired by interactive teaching tools in computer vision
 - Built with Streamlit for rapid prototyping
 - OpenCV for image processing
 - HuggingFace for free hosting
 
-## 📞 Support
+## Support
 
 - **Issues**: GitHub Issues for bugs and features
 - **Questions**: Discussion board for educational questions
 - **Documentation**: See README.md, QUICKSTART.md, IMAGES.md
 
-## 🎉 Success Stories
+## Success Stories
 
 Perfect for:
-- ✅ Graduate image analysis courses
-- ✅ Computer vision fundamentals
-- ✅ Self-paced online learning
-- ✅ Workshop demonstrations
-- ✅ Research group tutorials
+- Graduate image analysis courses
+- Computer vision fundamentals
+- Self-paced online learning
+- Workshop demonstrations
+- Research group tutorials
 
----
-
-**Ready to start?** Run `./run_simple.sh` and explore! 🚀
+--- **Ready to start?** Run `./run_simple.sh` and explore! 

README.md

@@ -1,6 +1,6 @@
 ---
 title: Image Sampling and Quantization Demo
-emoji: 🎨
+emoji: 
 colorFrom: blue
 colorTo: purple
 sdk: streamlit
@@ -10,9 +10,9 @@ pinned: false
 license: mit
 ---
 
-# 🎨 Interactive Image Sampling and Quantization Demo
+# Interactive Image Sampling and Quantization Demo
 
-An educational demonstration showing how sampling and quantization impact image visualization and storage. Perfect for graduate-level image analysis courses!
+An educational demonstration showing how sampling and quantization impact image visualization and storage.
 
 ## What You'll Learn
 
@@ -35,11 +35,11 @@ This interactive demo teaches fundamental concepts in digital image processing:
 
 ## Features
 
-- 🎛️ **Interactive Sliders**: Real-time adjustment of sampling rate and bit depth
-- 📊 **Live File Size Estimates**: See how changes affect storage requirements
-- 🔍 **Side-by-Side Comparisons**: Original vs. processed images
-- 📸 **Compression Artifacts**: Visualize JPEG blocking effects
-- 📈 **Educational Insights**: Learn the theory behind each concept
+- **Interactive Sliders**: Real-time adjustment of sampling rate and bit depth
+- **Live File Size Estimates**: See how changes affect storage requirements
+- **Side-by-Side Comparisons**: Original vs. processed images
+- **Compression Artifacts**: Visualize JPEG blocking effects
+- **Educational Insights**: Learn the theory behind each concept
 
 ## Local Development
 
@@ -72,7 +72,7 @@ The app will be available at `http://localhost:8501`
 This app is designed to be deployed to Hugging Face Spaces:
 
 1. Create a new Space on [Hugging Face](https://huggingface.co/spaces)
-2. Choose "Streamlit" as the SDK
+2. Choose "Docker" as the SDK
 3. Push this repository to your Space
 
 Or use the deployment script:
@@ -94,7 +94,7 @@ This demo is designed for:
 
 - **Nyquist Sampling Theorem**: Understanding sampling limits
 - **Bit Depth**: Relationship between bits and color/gray levels
-- **File Size Calculation**: Width × Height × Bits per pixel ÷ 8
+- **File Size Calculation**: Width x Height x Bits per pixel / 8
 - **Lossy vs. Lossless Compression**: JPEG vs. PNG trade-offs
 - **Blocking Artifacts**: DCT-based compression effects