ruff modifications

.ruff.toml

@@ -0,0 +1,18 @@
+line-length = 100
+target-version = "py310"
+lint.select = [
+    # pycodestyle
+    "E",
+    "W",
+    # Pyflakes
+    "F",
+    # pyupgrade
+    "UP",
+    # flake8-bugbear
+    "B",
+    # flake8-simplify
+    "SIM",
+    # isort
+    "I",
+]
+extend-include = ["*.ipynb"]

app/app/__init__.py

@@ -6,9 +6,9 @@
 __copyright__ = "Copyright 2018-2024"
 
 
+from config import config
 from flask import Flask, render_template
 
-from config import config
 from .model import init_model
 
 

app/app/api.py

@@ -7,10 +7,11 @@
 
 
 import io
+
 from flask import Blueprint, jsonify, request
 from skimage.io import imread
-from .model import current_app, np, preprocess_image
 
+from .model import current_app, np, preprocess_image
 
 api = Blueprint("api", __name__)
 
@@ -21,31 +22,30 @@ def predict():
     result = {"success": False}
 
     # ensure an image was properly uploaded to our endpoint
-    if request.method == "POST":
-        if request.files.get("file"):
-            # read image as grayscale
-            image_req = request.files["file"].read()
-            request.files["file"].close()
-            image = imread(io.BytesIO(image_req), as_gray=True)
+    if request.method == "POST" and request.files.get("file"):
+        # read image as grayscale
+        image_req = request.files["file"].read()
+        request.files["file"].close()
+        image = imread(io.BytesIO(image_req), as_gray=True)
 
-            # preprocess the image for model
-            preprocessed_image = preprocess_image(image)
+        # preprocess the image for model
+        preprocessed_image = preprocess_image(image)
 
-            # classify the input image generating a list of predictions
-            model = current_app.config["model"]
-            preds = model.predict(preprocessed_image)
+        # classify the input image generating a list of predictions
+        model = current_app.config["model"]
+        preds = model.predict(preprocessed_image)
 
-            # add generated predictions to result
-            result["predictions"] = []
+        # add generated predictions to result
+        result["predictions"] = []
 
-            for i in range(0, 10):
-                pred = {"label": str(i), "probability": str(preds[0][i])}
-                result["predictions"].append(pred)
+        for i in range(0, 10):
+            pred = {"label": str(i), "probability": str(preds[0][i])}
+            result["predictions"].append(pred)
 
-            result["most_probable_label"] = str(np.argmax(preds[0]))
+        result["most_probable_label"] = str(np.argmax(preds[0]))
 
-            # indicate that the request was a success
-            result["success"] = True
+        # indicate that the request was a success
+        result["success"] = True
 
     # return result dictionary as JSON response to client
     return jsonify(result)

app/app/model.py

@@ -6,11 +6,10 @@
 __copyright__ = "Copyright 2018-2024"
 
 
-from tensorflow.keras.models import load_model
-from skimage import transform, util
 import numpy as np
-
 from flask import current_app
+from skimage import transform, util
+from tensorflow.keras.models import load_model
 
 
 def init_model():

app/tests/conftest.py

@@ -6,9 +6,10 @@
 __copyright__ = "Copyright 2018-2024"
 
 
-from app import create_app
 import pytest
 
+from app import create_app
+
 
 @pytest.fixture
 def app():

app/tests/test_app.py

@@ -22,29 +22,29 @@ def test_api(client):
     IMAGE_PATH = "../app/static/4.jpg"
 
     # create payload with image for request
-    image = open(IMAGE_PATH, "rb")
-    payload = {"file": image}
-    response = client.post(SERVER_URL, data=payload)
-
-    # check response
-    assert response.status_code == 200
-
-    # JSON format
-    try:
-        json_response = json.loads(response.data.decode("utf8"))
-    except ValueError as e:
-        print(e)
-        assert False
-
-    # successful
-    if json_response["success"]:
-        # most probable label
-        print(json_response["most_probable_label"])
-        # predictions
-        for dic in json_response["predictions"]:
-            print(f"label {dic['label']} probability: {dic['probability']}")
-
-        assert json_response["most_probable_label"] == "4"
-    # failed
-    else:
-        assert False
+    with open(IMAGE_PATH, "rb") as image:
+        payload = {"file": image}
+        response = client.post(SERVER_URL, data=payload)
+
+        # check response
+        assert response.status_code == 200
+
+        # JSON format
+        try:
+            json_response = json.loads(response.data.decode("utf8"))
+        except ValueError as e:
+            print(e)
+            exit(1)
+
+        # successful
+        if json_response["success"]:
+            # most probable label
+            print(json_response["most_probable_label"])
+            # predictions
+            for dic in json_response["predictions"]:
+                print(f"label {dic['label']} probability: {dic['probability']}")
+
+            assert json_response["most_probable_label"] == "4"
+        # failed
+        else:
+            raise AssertionError()