Merge pull request #14 from UPstartDeveloper/readme-polish

Readme polish

Author: UPstartDeveloper

Dockerfile

@@ -1,5 +1,6 @@
 FROM python:3.6-slim
 COPY app/ /app/
+COPY classifier/ /classifier/
 WORKDIR /
 RUN apt update
 RUN apt install -y git

README.md

@@ -1,4 +1,7 @@
 # DeepFire
+![This repo has a MIT license](https://img.shields.io/github/license/UPstartDeveloper/Fire-Detection-API)
+![Automated tests passing on Travis (.org)](https://img.shields.io/travis/UPstartDeveloper/Fire-Detection-API)
+![Docker Image Size (latest)](https://img.shields.io/docker/image-size/zainrazatheupstart/fire-classifier-api/latest?style=flat-square)
 ![Project cover image](https://i.postimg.cc/d1tr9S2H/Screen-Shot-2021-09-02-at-12-37-00-PM.png)
 An API for detecting fires in images using deep learning.
 
@@ -37,9 +40,10 @@ To run the tests, you will first need to set up a Python virtual environment to
 ```
 (env) $ pytest
 ```
-
+If there are no failures, then you should be good to go!
+You can inspect the code for the tests in `app/main_test.py` if you wish.
 ## The Data and the Model
-The image dataset and model used for the production API will be documented on the [Releases](https://github.com/UPstartDeveloper/Fire-Detection-API/releases) page of this repository.
+The image dataset and neural network model used for the production API will be documented on the [Releases](https://github.com/UPstartDeveloper/Fire-Detection-API/releases) page of this repository.
 
 ## Making Your Own Deep Learning API
 

app/main_test.py

@@ -7,22 +7,20 @@
 
 client = TestClient(app)
 
+
 class ClassifierAPITest(unittest.TestCase):
 
     TEST_COLOR_IMG_URL = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"
 
     def test_make_prediction_normal(self):
-        '''Client submits a RGB image and gets a valid response.'''
+        """Client submits a RGB image and gets a valid response."""
         # A: get image
         image_path = tf.keras.utils.get_file(
-            origin=self.TEST_COLOR_IMG_URL, 
-            cache_dir=".", cache_subdir="./test-assets"
+            origin=self.TEST_COLOR_IMG_URL, cache_dir=".", cache_subdir="./test-assets"
         )
-        with open(image_path, 'rb') as f:
+        with open(image_path, "rb") as f:
             # B: format a mock request
             files = {"file": (f.name, f, "multipart/form-data")}
             # C: make a request, and verify a valid prediction is returned
-            response = client.post(
-                '/detect-fire', files=files
-            )
+            response = client.post("/detect-fire", files=files)
             assert response.status_code == 307

classifier/util/__init__.py

@@ -26,12 +26,12 @@ def __init__(self, config: Dict[str, str]):
 
     @classmethod
     def reconstruct_model(cls, config):
-        '''Make a new instance, and load in the model straightaway.'''
+        """Make a new instance, and load in the model straightaway."""
         model_utility = cls(config)
         # detect save format
-        save_format = 'composite'
+        save_format = "composite"
         if config["model_file_paths"] and len(config["model_file_paths"]) == 1:
-            save_format = 'h5'
+            save_format = "h5"
         # load the model
         return model_utility.load_model(save_format)