Spaces:
Sleeping
Sleeping
| title: Tech Stack Advisor | |
| emoji: 🧠 | |
| colorFrom: indigo | |
| colorTo: pink | |
| sdk: docker | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| duplicable: 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**. | |
| --- | |
| ## 🚀 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) | |
| --- | |
| ## 📁 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 | |
| ```` | |
| --- | |
| ## 🧠 Step 1: Setup and Train Your ML Model | |
| 1. **Clone the repository** | |
| ```bash | |
| git clone https://github.com/<your-username>/tech-stack-advisor.git | |
| cd tech-stack-advisor | |
| ```` | |
| 2. **Install dependencies** | |
| (Optional: Use a virtual environment) | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. **Train the model** | |
| ```bash | |
| python train.py | |
| ``` | |
| This creates: | |
| * `model.pkl`: the trained ML model | |
| * `encoders.pkl`: label encoders for input/output features | |
| --- | |
| ## 🖥️ Step 2: Run the App Locally (Without Docker) | |
| ```bash | |
| python app.py | |
| ``` | |
| Visit the app in your browser at: | |
| ``` | |
| http://localhost:7860 | |
| ``` | |
| --- | |
| ## 🐳 Step 3: Add Docker Support | |
| Create a file named `Dockerfile` in the root of the project: | |
| ```dockerfile | |
| 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"] | |
| ``` | |
| --- | |
| ## 🔧 Step 4: Build and Run the Docker Container | |
| 1. **Build the image** | |
| ```bash | |
| docker build -t tech-stack-advisor . | |
| ``` | |
| 2. **Run the container** | |
| ```bash | |
| docker run -p 7860:7860 tech-stack-advisor | |
| ``` | |
| Visit: `http://localhost:7860` | |
| --- | |
| ## ☁️ Step 5: Publish to Docker Hub | |
| 1. **Login to Docker Hub** | |
| ```bash | |
| docker login | |
| ``` | |
| 2. **Tag the image** | |
| ```bash | |
| docker tag tech-stack-advisor <your-dockerhub-username>/tech-stack-advisor:latest | |
| ``` | |
| 3. **Push it** | |
| ```bash | |
| docker push <your-dockerhub-username>/tech-stack-advisor:latest | |
| ``` | |
| --- | |
| ## 🌐 Step 6: Deploy to Hugging Face Spaces | |
| 1. Go to [huggingface.co/spaces](https://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. | |
| --- | |
| ## 🧪 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! | |
| --- | |
| ## 🧾 License | |
| This project is licensed under the **Apache License 2.0**. | |
| See the [LICENSE](./LICENSE) file for details. | |
| --- | |
| ## 🙌 Credits | |
| Created by \[Gourav Shah](https://www.linkedin.com/in/gouravshah) as part of the **AI/ML with Docker** course at **School of DevOps**. | |
| --- | |
| > 🛠 Happy shipping, DevOps and MLOps builders! | |