Test

.github/workflows/cicd.yml

@@ -1,10 +1,10 @@
 name: ci-cd
 
-on: pull_request, push
+on: [pull_request, push]
 
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-latest 
     steps:
       - name: Checkout the repository
         uses: actions/checkout@v2
@@ -18,9 +18,9 @@ jobs:
       - name: Run pytest
         run: |
           pytest
-
+  
   upload_zip:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-latest 
     needs: build
     if: ${{ github.event_name == 'push' }}
     steps:

README.md

@@ -0,0 +1,24 @@
+# ML-Ops demo using a FastAPI application
+
+This repository contains code which demonstrates ML-Ops using a `FastAPI` application which predicts the flower class using the IRIS dataset (https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)
+
+
+## Setup Project
+- Create fork from fork button
+- Clone the fork using `git clone https://github.com/purnendukar/mlops-iris`
+- Install dependency using `pip3 install -r requirements.txt`
+
+## Running Project
+- Run application using `python3 main.py`
+- Run test using `pytest`
+
+## CI/CD
+- `build` (test) for all the pull requests
+- `build` (test) and `upload_zip` for all pushes
+
+## Assignment Tasks
+
+1. Change this README to add your name here: Purnendu Kar. Add and commit changes to a new branch and create a pull request ONLY TO YOUR OWN FORK to see the CI/CD build happening. If the build succeeds, merge the pull request with master and see the CI/CD `upload_zip` take place.
+2. Add 2 more unit tests of your choice to `test_app.py` and make sure they are passing.
+3. Add one more classifier to startup and use only the one with better accuracy.
+4. Add the attribute `timestamp` to the response and return the current time with it. 

test_app.py

@@ -18,4 +18,30 @@ def test_pred_virginica():
     with TestClient(app) as client:
         response = client.post('/predict_flower', json=payload)
         assert response.status_code == 200
-        assert response.json() == {'flower_class': "Iris Virginica"}
+        assert response.json() == {'flower_class': "Iris Virginica"}
+
+
+def test_pred_versicolor():
+    payload = {
+      "sepal_length": 5.8,
+      "sepal_width": 2.7,
+      "petal_length": 3.9,
+      "petal_width": 1.2
+    }
+    with TestClient(app) as client:
+        response = client.post('/predict_flower', json=payload)
+        assert response.status_code == 200
+        assert response.json() == {'flower_class': "Iris Versicolour"}
+
+
+def test_pred_setosa():
+    payload = {
+      "sepal_length": 4.5,
+      "sepal_width": 2.3,
+      "petal_length": 1.3,
+      "petal_width": 0.3
+    }
+    with TestClient(app) as client:
+        response = client.post('/predict_flower', json=payload)
+        assert response.status_code == 200
+        assert response.json() == {'flower_class': "Iris Setosa"}