[add] elaborate comments

.github/workflows/cicd.yml

@@ -6,7 +6,8 @@ on: [pull_request, push]
 jobs:
   # first job to test the application using pytest
   build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-latest # choose the OS for running the action
+    # define the individual sequential steps to be run
     steps:
       - name: Checkout the repository
         uses: actions/checkout@v2
@@ -23,11 +24,13 @@ jobs:
   
   # second job to zip the codebase and upload it as an artifact when build succeeds 
   upload_zip:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-latest # choose the OS for running the action
     needs: build
 
     # only run this action for pushes
     if: ${{ github.event_name == 'push' }}
+
+    # define the individual sequential steps to be run
     steps:
       - name: Checkout the repository
         uses: actions/checkout@v2

main.py

@@ -7,7 +7,8 @@
 # defining the main app
 app = FastAPI(title="Iris Predictor", docs_url="/")
 
-# calling the load_model during startup
+# calling the load_model during startup.
+# this will train the model and keep it loaded for prediction.
 app.add_event_handler("startup", load_model)
 
 # class which is expected in the payload
@@ -32,21 +33,29 @@ class FeedbackIn(BaseModel):
 
 # Route definitions
 @app.get("/ping")
+# Healthcheck route to ensure that the API is up and running
 def ping():
     return {"ping": "pong"}
 
 
 @app.post("/predict_flower", response_model=QueryOut, status_code=200)
+# Route to do the prediction using the ML model defined.
+# Payload: QueryIn containing the parameters
+# Response: QueryOut containing the flower_class predicted (200)
 def predict_flower(query_data: QueryIn):
     output = {"flower_class": predict(query_data)}
     return output
 
 @app.post("/feedback_loop", status_code=200)
+# Route to further train the model based on user input in form of feedback loop
+# Payload: FeedbackIn containing the parameters and correct flower class
+# Response: Dict with detail confirming success (200)
 def feedback_loop(data: List[FeedbackIn]):
     retrain(data)
     return {"detail": "Feedback loop successful"}
 
 
 # Main function to start the app when main.py is called
 if __name__ == "__main__":
+    # Uvicorn is used to run the server and listen for incoming API requests on 0.0.0.0:8888
     uvicorn.run("main:app", host="0.0.0.0", port=8888, reload=True)

ml_utils.py

@@ -3,18 +3,23 @@
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import accuracy_score
 
+# define a Gaussain NB classifier
 clf = GaussianNB()
 
+# define the class encodings and reverse encodings
 classes = {0: "Iris Setosa", 1: "Iris Versicolour", 2: "Iris Virginica"}
 r_classes = {y: x for x, y in classes.items()}
 
 # function to train and load the model during startup
 def load_model():
+    # load the dataset from the official sklearn datasets
     X, y = datasets.load_iris(return_X_y=True)
 
+    # do the test-train split and train the model
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
     clf.fit(X_train, y_train)
 
+    # calculate the print the accuracy score
     acc = accuracy_score(y_test, clf.predict(X_test))
     print(f"Model trained with accuracy: {round(acc, 3)}")
 
@@ -26,7 +31,11 @@ def predict(query_data):
     print(f"Model prediction: {classes[prediction]}")
     return classes[prediction]
 
+# function to retrain the model as part of the feedback loop
 def retrain(data):
+    # pull out the relevant X and y from the FeedbackIn object
     X = [list(d.dict().values())[:-1] for d in data]
     y = [r_classes[d.flower_class] for d in data]
+
+    # fit the classifier again based on the new data obtained
     clf.fit(X, y)

test_app.py

@@ -5,12 +5,14 @@
 def test_ping():
     with TestClient(app) as client:
         response = client.get("/ping")
+        # asserting the correct response is received
         assert response.status_code == 200
         assert response.json() == {"ping": "pong"}
 
 
 # test to check if Iris Virginica is classified correctly
 def test_pred_virginica():
+    # defining a sample payload for the testcase
     payload = {
         "sepal_length": 3,
         "sepal_width": 5,
@@ -19,5 +21,6 @@ def test_pred_virginica():
     }
     with TestClient(app) as client:
         response = client.post("/predict_flower", json=payload)
+        # asserting the correct response is received
         assert response.status_code == 200
         assert response.json() == {"flower_class": "Iris Virginica"}