Integration tests (IBM#7)

* added integration tests and switched to max-base image

* corrected model name in .travis.yml

.dockerignore

@@ -1,2 +1,5 @@
 README.md
 Dockerfile
+.git/
+tests/
+/.pytest_cache/

.gitignore

@@ -100,3 +100,4 @@ ENV/
 
 # mypy
 .mypy_cache/
+/.pytest_cache/

.travis.yml

@@ -0,0 +1,12 @@
+language: python
+python:
+  - 3.6
+services:
+  - docker
+install:
+  - docker build -t max-breast-cancer-mitosis-detector .
+  - docker run -it -d -p 5000:5000 max-breast-cancer-mitosis-detector
+before_script:
+  - pip install pytest pycurl
+script:
+  - pytest tests/test.py

Dockerfile

@@ -1,21 +1,18 @@
-FROM continuumio/miniconda3
+FROM codait/max-base
 
 RUN apt-get update && apt-get install -y libopenslide0 gcc && rm -rf /var/lib/apt/lists/*
 
 # Python package versions
 ARG numpy_version=1.14.1
 ARG tensorflow_version=1.9.0
 
-RUN pip install --upgrade pip && \
-    pip install numpy==${numpy_version} && \
+RUN pip install numpy==${numpy_version} && \
     pip install Pillow && \
     pip install h5py && \
-    pip install flask-restplus && \
     pip install openslide-python && \
     pip install tensorflow==${tensorflow_version}
 
-RUN mkdir /workspace && \
-    cd /workspace && \
+RUN cd /workspace && \
     git clone https://github.com/codait/deep-histopath && \
     cp -R deep-histopath/. .
 

tests/test.py

@@ -0,0 +1,45 @@
+import pytest
+import pycurl
+import io
+import json
+
+
+def test_response():
+
+    # Test True
+
+    c = pycurl.Curl()
+    b = io.BytesIO()
+    c.setopt(pycurl.URL, 'http://localhost:5000/model/predict')
+    c.setopt(pycurl.HTTPHEADER, ['Accept:application/json', 'Content-Type: multipart/form-data'])
+    c.setopt(pycurl.HTTPPOST, [('image', (pycurl.FORM_FILE, "assets/true.png"))])
+    c.setopt(pycurl.WRITEFUNCTION, b.write)
+    c.perform()
+    assert c.getinfo(pycurl.RESPONSE_CODE) == 200
+    c.close()
+    response = b.getvalue()
+    response = json.loads(response)
+
+    assert response['status'] == 'ok'
+    assert response['predictions'][0]['probability'] > 0.5
+
+    # Test False
+
+    c = pycurl.Curl()
+    b = io.BytesIO()
+    c.setopt(pycurl.URL, 'http://localhost:5000/model/predict')
+    c.setopt(pycurl.HTTPHEADER, ['Accept:application/json', 'Content-Type: multipart/form-data'])
+    c.setopt(pycurl.HTTPPOST, [('image', (pycurl.FORM_FILE, "assets/false.png"))])
+    c.setopt(pycurl.WRITEFUNCTION, b.write)
+    c.perform()
+    assert c.getinfo(pycurl.RESPONSE_CODE) == 200
+    c.close()
+    response = b.getvalue()
+    response = json.loads(response)
+
+    assert response['status'] == 'ok'
+    assert response['predictions'][0]['probability'] < 0.5
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])