updated README for building and deploying the project along with hugging face metadata

README.md

@@ -1,2 +1,188 @@
-# tech-stack-advisor
-Tech Stack Advisor Sample ML App 
+---
+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 \[Your Name] as part of the **AI/ML with Docker** course at **School of DevOps**.
+
+---
+
+> 🛠 Happy shipping, DevOps builders!
+