OI.AI.PyDev.TakeHome

Video Compression Service

This project is a Python-based service for handling video compression using FastAPI. It monitors a folder for new video files, compresses the videos to a maximum of 1/5 of their original size, and logs metadata of the processed videos into an SQLite database.

Folder Structure

OI.AI.PyDev.TakeHome/
│
├── config/
│   ├── Dockerfile                # Dockerfile for building the image
│   ├── .dockerignore             # Dockerignore to exclude unnecessary files
│   ├── Pipfile                   # Pipfile for dependency management
│   ├── Pipfile.lock              # Lockfile to ensure consistent dependencies
│
├── data/
│   ├── uploaded_videos/          # Folder where videos will be uploaded
│   ├── compressed_videos/        # Folder where compressed videos will be saved
│   ├── video_metadata.db         # SQLite database (runtime)
│
├── src/
│   ├── __init__.py
│   ├── batch.py                  # FastAPI application
│   ├── main.py                   # Entry point for running the app
│   ├── models/                   # Holds database-related models
│   │   ├── __init__.py
│   │   ├── db_manager.py         # Manages SQLite database operations
│   ├── services/                 # Contains service classes
│   │   ├── __init__.py
│   │   ├── video_compressor.py   # Contains the logic for compressing videos
│
├── tests/
│   ├── __init__.py
│   ├── test_service.py           # Test cases
│
├── README.md                     # Project documentation

Key Folders and Files:

•	config/: Contains configuration files like Dockerfile, Pipfile, and .dockerignore.
•	data/: This folder holds the runtime data such as uploaded videos, compressed videos, and the SQLite database file (video_metadata.db).
•	src/: Contains the FastAPI application and core logic, including:
•	batch.py: The main FastAPI app where API routes are defined.
•	main.py: The entry point for running the FastAPI app.
•	models/db_manager.py: Handles the database operations for logging video metadata.
•	services/video_compressor.py: Contains the video compression logic using FFmpeg.
•	tests/: Holds the test cases for the project.

Running the Project Without Docker

Prerequisites

•	Python 3.11
•	FFmpeg: You need FFmpeg installed on your system to handle video compression.
•	Pipenv: Pipenv is used for managing Python dependencies.

Steps:

1. Clone the repository: bash git clone <repository-url> cd OI.AI.PyDev.TakeHome

2. Install the dependencies: bash pipenv install Then install project dependencies using: bash pipenv install

3. Run the FastAPI application: bash pipenv run python src/main.py

4. Access the FastAPI application at http://127.0.0.1:8000/docs to access the FastAPI Swagger UI and interact with the API.

Running the Project with Docker

Prerequisites

    •	Docker: Make sure Docker is installed on your system.

Steps:

1. Clone the repository:

   git clone <repository-url>
   cd OI.AI.PyDev.TakeHome

2. Build the Docker image:

   docker build -t fastapi-video-app .

3. Run the Docker container:

   docker run -p 8000:8000 fastapi-video-app

4. http://127.0.0.1:8000/docs to access the FastAPI Swagger UI.

API Endpoints

1. Upload a Video

- URL: POST /upload/
- Description: Upload a video file, which will be compressed and stored.
- Request:

curl -X 'POST' \
  'http://127.0.0.1:8000/upload/' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@/path/to/your/video.mp4'

• Response:

{
  "message": "Video uploaded and compressed successfully.",
  "original_size": 5000000,
  "compressed_size": 1000000,
  "compression_ratio": 0.2,
  "compressed_file": "compressed_videos/compressed_video.mp4"
}

2. Get All Video Metadata

• URL: GET /videos/
• Description: Retrieve the metadata for all processed videos from the SQLite database.
• Request:

 curl -X 'GET' 'http://127.0.0.1:8000/videos/'

• Response:

[
  {
    "id": 1,
    "original_file": "uploaded_videos/video.mp4",
    "compressed_file": "compressed_videos/compressed_video.mp4",
    "original_size": 5000000,
    "compressed_size": 1000000,
    "compression_ratio": 0.2
  }
]

Notes

• The compressed videos are stored in data/compressed_videos/ and the original uploaded videos are stored in data/uploaded_videos/.
• The metadata for each video, including the original size and compressed size, is stored in an SQLite database located at data/video_metadata.db.