API for interior quality classification
How to start with our app
This is an example of how to list things you need to use the software and how to install them.
- Python >= 3.6
$ sudo apt-get install software-properties-common $ sudo add-apt-repository ppa:deadsnakes/ppa $ sudo apt-get update $ sudo apt-get install python3.6 $ sudo apt install python3-pip
- Git
$ sudo apt install git-all
- Clone the repo
$ git clone https://github.com/ra-led/python-remont-api.git
$ cd python-remont-api- Install Python packages
$ pip3 install -r requirements.txt
- Download pre-trained weights Create model dir and start Python
$ mkdir models $ python3
In Python console run
import gdown url = 'https://drive.google.com/uc?id=1pewnHlXGvPEd0h6yzueIpKq8hlnmCU-E' output = 'models/resnet18_baseline_cpu.torch' gdown.download(url, output, quiet=False) quit()
Start server with Flask default WSGI
$ python3 run.py
Server will start on port 5005, to change port set it in conf.yml
Alternativly, you can use Gunicorn WSGI
Launch http://{your_host_adres}:5005/
- Interior class quality prediction
Params
file - image file for classification
Response JSON
{"predicted": "Bez otdelki" | "Luks" | "Standart" | "Trebuet kosmetiki" }
Example
curl -X POST -F 'file=@path/to/pictures/picture.jpg' http://127.0.0.1:5005/predict_class