Merge pull request #13 from UPstartDeveloper/testing

Testing

Author: UPstartDeveloper

.gitignore

@@ -11,7 +11,10 @@ __pycache__/
 .vscode
 
 # Keras Models
-*model/*
+model/
+
+# Images Downloaded for Testing
+test-assets/
 
 # Distribution / packaging
 .Python

.travis.yml

@@ -0,0 +1,9 @@
+language: python
+python:
+  - "3.6"
+# command to install dependencies
+install:
+  - pip install -r requirements.txt
+# command to run tests
+script:
+  - pytest

README.md

@@ -11,9 +11,6 @@ We built this API to show how the technology can fight this and other crises, an
 ### Use the API
 To classify your own images, you can use the live API: use the link [here](https://fire-detection-api.herokuapp.com/docs) to read the documentation and send requests.
 
-### Run the Tests
-TBD
-
 ### Running Locally
 
 #### Using Docker
@@ -35,6 +32,12 @@ And then run the app using `uvicorn` in the Command Line:
 (env) $ uvicorn app.main:app --reload  
 ```
 
+### Run the Tests
+To run the tests, you will first need to set up a Python virtual environment to run this project locally (see above). Then you can run the automated tests from the root directory, using the command line:
+```
+(env) $ pytest
+```
+
 ## 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.
 

app/__init__.py

@@ -15,7 +15,7 @@
 predictor = ImagePredictor.init_from_config_path(predictor_config_path)
 
 
-@app.post("/classify-image/", tags=["Detect Fire in an Image"])
-def create_upload_file(file: UploadFile = File(...)):
+@app.post("/detect-fire/", tags=["Detect Fire in an Image"])
+def make_prediction(file: UploadFile = File(...)):
     """Predicts the possibility that a RBG image contains fire."""
     return predictor.predict_from_file(file.file)

app/main.py

@@ -0,0 +1,28 @@
+import unittest
+
+from fastapi.testclient import TestClient
+import tensorflow as tf
+
+from app.main import app
+
+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.'''
+        # A: get image
+        image_path = tf.keras.utils.get_file(
+            origin=self.TEST_COLOR_IMG_URL, 
+            cache_dir=".", cache_subdir="./test-assets"
+        )
+        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
+            )
+            assert response.status_code == 307

app/main_test.py

@@ -4,4 +4,5 @@ fastapi==0.49.0
 opencv-python-headless==4.5.3.56
 PyYAML==5.3
 uvicorn
+pytest==6.2.5
 python-multipart