Update README.md

Author: olawale0254

README.md

@@ -1 +1,51 @@
-# deep_recommender
+# Movie Recommendation System
+
+## Overview
+
+This project implements a scalable Movie Recommendation System that leverages a multi-stage pipeline with ETL, model training, and an API for serving recommendations. The entire system is containerized using Docker and includes load testing using Locust.
+
+## Features
+
+- **ETL Pipeline**: Extracts and transforms movie data, then saves it to an S3-compatible storage (MinIO).
+- **Model Training**: Loads data from S3, trains the recommendation model, and saves the trained model back to S3.
+- **API Service**: A FastAPI-based service that loads the model from S3 and serves movie recommendations.
+- **Load Testing**: Uses Locust to test the API's performance under load.
+
+## Project Structure
+
+- `Dockerfile`: Configuration for building the application container.
+- `docker-compose.yml`: Defines services for ETL, model training, API, and MinIO.
+- `locustfile.py`: Script for load testing the API.
+- `src/`: Source code for ETL, training, and API.
+- `data/`: Initial data files used by the ETL process.
+
+## Installation and Setup
+
+1. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/olawale0254/Movie_recommendation.git
+   cd Movie_recommendation
+   ```
+
+2. ** Environment Variables: Create a `.env` file in the root directory with your MinIO credentials:
+   ```bash
+   S3_ACCESS_KEY=<your-access-key>
+   S3_SECRET_KEY=<your-secret-key>
+   ```
+3. ** Build and Run the Application:
+   ```bash
+   docker-compose up --build
+   ```
+4. Access the Services:
+   - API: `http://localhost:8000`
+   - MinIO Console: `http://localhost:9001`
+## Pipeline Overview
+- ETL Service: Loads and processes movie data, then stores it in MinIO (S3-compatible storage).
+- Trainer Service: Retrieves the processed data from MinIO, trains the model, and saves the model back to MinIO.
+- API Service: Loads the trained model from MinIO and provides movie recommendations via RESTful endpoints.
+
+## Load Testing
+Use Locust to test the API's performance:
+```bash
+locust -f locustfile.py --host=http://localhost:8000
+```