title	emoji	colorFrom	colorTo	sdk	app_file	pinned	license	duplicable
Tech Stack Advisor	🧠	indigo	pink	docker	app.py	false	apache-2.0	true

🧠 Tech Stack Advisor – ML App (with Docker & Hugging Face Deployment)

Tech Stack Advisor is a hands-on machine learning project designed to teach you how to build, containerize, and deploy an ML-powered web application using Docker and Hugging Face Spaces.

🎯 This project is part of the "Artificial Intelligence and Machine Learning (AI/ML) with Docker" course from School of DevOps.

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🚀 What You'll Learn

• Build and train a simple ML model using scikit-learn
• Create a UI using Gradio
• Containerize your app using a Dockerfile
• Push your Docker image to Docker Hub
• Deploy the Dockerized app on Hugging Face Spaces (free tier)

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📁 Project Structure

tech-stack-advisor/
├── app.py             # Gradio web app
├── train.py           # Script to train and save ML model
├── requirements.txt   # Python dependencies
├── Dockerfile         # Docker build file (added during the lab)
├── model.pkl          # Trained ML model (generated after training)
├── encoders.pkl       # Encoders for categorical inputs (generated after training)
├── LICENSE            # Apache 2.0 license
└── README.md          # This guide

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🧠 Step 1: Setup and Train Your ML Model

1. Clone the repository

git clone https://github.com/<your-username>/tech-stack-advisor.git
cd tech-stack-advisor

2. Install dependencies

(Optional: Use a virtual environment)

pip install -r requirements.txt

3. Train the model

python train.py

This creates:

• model.pkl: the trained ML model
• encoders.pkl: label encoders for input/output features

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🖥️ Step 2: Run the App Locally (Without Docker)

python app.py

Visit the app in your browser at:

http://localhost:7860

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🐳 Step 3: Add Docker Support

Create a file named Dockerfile in the root of the project:

FROM python:3.11-slim

WORKDIR /app

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

COPY . .

EXPOSE 7860

CMD ["python", "app.py"]

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🔧 Step 4: Build and Run the Docker Container

1. Build the image

docker build -t tech-stack-advisor .

2. Run the container

docker run -p 7860:7860 tech-stack-advisor

Visit: http://localhost:7860

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☁️ Step 5: Publish to Docker Hub

1. Login to Docker Hub

docker login

2. Tag the image

docker tag tech-stack-advisor <your-dockerhub-username>/tech-stack-advisor:latest

3. Push it

docker push <your-dockerhub-username>/tech-stack-advisor:latest

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🌐 Step 6: Deploy to Hugging Face Spaces

1. Go to huggingface.co/spaces

2. Click Create New Space

3. Select:

   • SDK: Docker
   • Repository: Link to your GitHub repo with the Dockerfile

4. Hugging Face will auto-build and deploy your container.

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🧪 Test Your Skills

• Can you swap the model in train.py for a LogisticRegression model?
• Can you add logging to show which inputs were passed?
• Try changing the Gradio layout or theme!

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🧾 License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

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🙌 Credits

Created by [Gourav Shah](https://www.linkedin.com/in/gouravshah) as part of the AI/ML with Docker course at School of DevOps.

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🛠 Happy shipping, DevOps and MLOps builders!