riteshatri/upload-app repository overview

⁠📷 Upload-App (Streamlit Photo Upload & Gallery)

A lightweight Streamlit-based web application, packaged as a Docker image, designed to demonstrate Persistent Volume (PV) and Persistent Volume Claim (PVC) concepts in Docker/Kubernetes.

This app allows users to:

• Upload multiple images via a browser
• Persist uploaded images on disk
• View uploaded images in a clean gallery layout

The repository image is available on Docker Hub and is intentionally kept small, simple, and practical for DevOps & Kubernetes learning use‑cases.

⁠🔍 What Does This Image Do?

This Docker image runs a Streamlit web server that:

1. Accepts image uploads (png, jpg, jpeg)
2. Stores images inside a local /photos directory
3. Displays all stored images in a responsive grid

💡 Core learning objective:

⁠🧠 Why This App Is Useful (Real Meaning)

In real-world Kubernetes:

• Containers are ephemeral
• Pod restarts = data loss ❌ (unless volumes are used)

This app makes that concept visually clear:

• Upload images → stored in /photos
• Restart container / pod
• Images remain available if volume is mounted

Perfect for:

• Docker volumes practice
• Kubernetes PV / PVC demos
• Interviews & concept explanation

⁠🗂 Repository Structure (Explained Deeply)

upload-app/
├── manifests/        # (Optional) Kubernetes YAMLs (PV, PVC, Deployment)
├── photos/           # Runtime directory where images are stored
├── Dockerfile        # Container build instructions
├── main.py           # Streamlit application code
├── requirements.txt  # Python dependencies
├── pyproject.toml    # Project metadata (modern Python standard)
├── uv.lock           # Dependency lock file
├── README.md         # Documentation
└── .python-version   # Python version pin (3.11)

⁠🔹 photos/

This is the MOST IMPORTANT directory.

• All uploaded images are stored here
• This is the directory you mount as:
  • Docker volume
  • Kubernetes PVC

⁠🐳 Docker Image Details

⁠Base Image

python:3.11-slim

• Lightweight
• Secure
• Production-friendly

⁠Exposed Port

8501

(Default Streamlit port)

⁠🧱 Dockerfile Explained (Line by Line)

FROM python:3.11-slim

✔ Minimal Python image

WORKDIR /app

✔ All app files live here

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

✔ Dependency installation

COPY . .

✔ Copy application code

EXPOSE 8501

✔ Streamlit UI port

CMD ["streamlit", "run", "main.py", "--server.port=8501", "--server.address=0.0.0.0"]

✔ Starts Streamlit server

⁠▶️ How To Run This Image (Docker)

⁠1️⃣ Pull Image

docker pull riteshatri/upload-app:latest

⁠2️⃣ Run Without Volume (Data will be lost)

docker run -d \
  -p 8501:8501 \
  --name upload-app \
  riteshatri/upload-app:latest

❌ Restart container → images gone

⁠💾 Run With Docker Volume (Recommended)

docker volume create photos-data

docker run -d \
  -p 8501:8501 \
  -v photos-data:/app/photos \
  --name upload-app \
  riteshatri/upload-app:latest

✅ Restart container → images stay

⁠☸️ Kubernetes Use Case (PV / PVC)

⁠Concept Mapping

⁠Deployment Example (Conceptual)

volumeMounts:
- name: photos-volume
  mountPath: /app/photos

Now:

• Pod delete ❌ → data safe
• Pod recreate ✅ → images still visible

⁠🖼 Application Features (UI Level)

⁠Upload Section

• Multiple file upload
• Supported formats: png, jpg, jpeg

⁠Storage Logic

PHOTOS_DIR = Path("photos")
PHOTOS_DIR.mkdir(exist_ok=True)

✔ Directory auto-created

⁠Gallery View

• 4-column responsive grid
• Auto-detects stored images

⁠📦 Dependencies

From requirements.txt:

streamlit
pillow

⁠🔐 Security Notes

• No authentication (demo app)
• Not intended for public production exposure
• Best used inside:
  • Internal cluster
  • Learning environment

⁠🚀 Ideal For

✅ Docker learners
✅ Kubernetes beginners
✅ PV/PVC explanation
✅ Interview demos
✅ DevOps labs

⁠🧪 Common Test Scenarios

1. Upload images
2. Restart container
3. Observe data loss (without volume)
4. Attach volume
5. Restart again
6. Observe persistence 🎯

⁠📌 Summary (One-Line)

⁠👨‍💻 Maintainer

Ritesh Sharma
DevOps | Docker | Kubernetes | Azure

⭐ If you are learning containers & Kubernetes — this image is made exactly for you.