Dockerized Machine Learning API

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Project Structure

├── docker-compose.yml   # Docker Compose configuration for the services
├── init.sql             # SQL script to initialize the MySQL database
├── mlapi/               # The Flask API for handling requests
│   ├── Dockerfile       # Dockerfile for building the Flask API container
│   ├── requirements.txt # Python dependencies
│   ├── app.py           # Flask application
│   └── templates/       # HTML templates for rendering the UI
├── temperaturedata.csv   # Historical temperature data for training the model

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Setup Instructions

1. Clone the Repository

   git clone https://github.com/affafghani98/Docker-ML-Deployment.git
   

2. Navigate to the Project Directory

   cd Docker-Machine-Learning-API

3. Build and Start Services with Docker Compose

   docker-compose up --build

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API Endpoints

• POST /predict: Receives historical temperature data and returns a prediction based on the trained machine learning model.

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Requirements

• Docker
• Docker Compose
• Python 3.x (inside the container)

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License

This project is licensed under the MIT License.