Merge pull request #1246 from serengil/feat-task-0206-cosmetic-changes-for-docker

Feat task 0206 cosmetic changes for docker

README.md

@@ -315,11 +315,11 @@ cd scripts
 
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-api.jpg" width="90%" height="90%"></p>
 
-Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Default service endpoints will be `http://localhost:5000/verify` for face recognition, `http://localhost:5000/analyze` for facial attribute analysis, and `http://localhost:5000/represent` for vector representation. You can pass input images as exact image paths on your environment, base64 encoded strings or images on web. [Here](https://github.com/serengil/deepface/tree/master/deepface/api/postman), you can find a postman project to find out how these methods should be called.
+Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Default service endpoints will be `http://localhost:5005/verify` for face recognition, `http://localhost:5005/analyze` for facial attribute analysis, and `http://localhost:5005/represent` for vector representation. You can pass input images as exact image paths on your environment, base64 encoded strings or images on web. [Here](https://github.com/serengil/deepface/tree/master/deepface/api/postman), you can find a postman project to find out how these methods should be called.
 
 **Dockerized Service** - [`Demo`](https://youtu.be/9Tk9lRQareA)
 
-You can deploy the deepface api on a kubernetes cluster with docker. The following [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh) will serve deepface on `localhost:5000`. You need to re-configure the [Dockerfile](https://github.com/serengil/deepface/blob/master/Dockerfile) if you want to change the port. Then, even if you do not have a development environment, you will be able to consume deepface services such as verify and analyze. You can also access the inside of the docker image to run deepface related commands. Please follow the instructions in the [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh).
+You can deploy the deepface api on a kubernetes cluster with docker. The following [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh) will serve deepface on `localhost:5005`. You may need to re-configure the [Dockerfile](https://github.com/serengil/deepface/blob/master/Dockerfile) if you want to apply some customization. Then, even if you do not have a development environment, you will be able to consume deepface services such as verify and analyze. You can also access the inside of the docker image to run deepface related commands. Please follow the instructions in the [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh).
 
 ```shell
 cd scripts

deepface/api/src/app.py

@@ -1,8 +1,14 @@
 # 3rd parth dependencies
 from flask import Flask
+from deepface import DeepFace
 from deepface.api.src.modules.core.routes import blueprint
+from deepface.commons import logger as log
+
+logger = log.get_singletonish_logger()
+
 
 def create_app():
     app = Flask(__name__)
     app.register_blueprint(blueprint)
+    logger.info(f"Welcome to DeepFace API v{DeepFace.__version__}!")
     return app

deepface/api/src/modules/core/routes.py

@@ -1,4 +1,5 @@
 from flask import Blueprint, request
+from deepface import DeepFace
 from deepface.api.src.modules.core import service
 from deepface.commons import logger as log
 
@@ -9,7 +10,7 @@ blueprint = Blueprint("routes", __name__)
 
 @blueprint.route("/")
 def home():
-    return "<h1>Welcome to DeepFace API!</h1>"
+    return f"<h1>Welcome to DeepFace API v{DeepFace.__version__}!</h1>"
 
 
 @blueprint.route("/represent", methods=["POST"])

scripts/dockerize.sh

@@ -17,7 +17,8 @@ docker build -t deepface .
 # docker cp ~/.deepface/weights/. <CONTAINER_ID>:/root/.deepface/weights/
 
 # run image
-docker run --net="host" deepface
+# docker run --net="host" deepface
+docker run -p 5005:5000 deepface
 
 # to access the inside of docker image when it is in running status
 # docker exec -it <CONTAINER_ID> /bin/sh

scripts/service.sh

@@ -5,4 +5,4 @@ cd ../deepface/api/src
 # python api.py
 
 # run the service with gunicorn - for prod purposes
-gunicorn --workers=1 --timeout=3600 --bind=0.0.0.0:5000 "app:create_app()"
+gunicorn --workers=1 --timeout=3600 --bind=0.0.0.0:5005 "app:create_app()"