Initial commit

.github/workflows/cicd.yml

@@ -23,7 +23,7 @@ jobs:
           pytest
 
   codecoverage:
-    runs-on: ubuntu-latest # choose the OS for running the action
+    runs-on: windows-latest # choose the OS for running the action
     strategy:
       matrix: 
         python: ['3.7', '3.8', '3.9']

README.md

@@ -1,12 +1,12 @@
 # Dev-Sec-Ops Demo/Assignment
 
-[![codecov](https://codecov.io/gh/PGCSEDS-IIITH/devsecops-iris/branch/master/graph/badge.svg?token=EILEH8L7R5)](https://codecov.io/gh/PGCSEDS-IIITH/devsecops-iris)
+[![codecov][![codecov](https://codecov.io/gh/swathi14-5/devsecops-iris/branch/master/graph/badge.svg?token=BI069NHK2A)](https://codecov.io/gh/swathi14-5/devsecops-iris)
 
 This repository contains code which demonstrates Dev-Sec-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)
 
 ## Running Instructions
 - Create a fork of the repo using the `fork` button.
-- Clone your fork using `git clone https://www.github.com/<your-username>/mlops-iris.git`
+- Clone your fork using `git clone https://www.github.com/swathi14-5/mlops-iris.git`
 - Install dependencies using `pip3 install -r requirements.txt`
 - Run application using `python3 main.py`
 - Run tests using `pytest`
@@ -25,4 +25,4 @@ This repository contains code which demonstrates Dev-Sec-Ops using a `FastAPI` a
 - Make a visualisation for sarif report using any tool. upload screenshot, image, html or pdf. Add it to the reports/ folder. 
 
 ## Submission
-- Link to github repository as a comment on the submission (olympus portal). [uploading .txt is optional]
+- Link to github repository as a comment on the submission (olympus portal). [uploading .txt is optional]

main.py

@@ -1,7 +1,7 @@
 import uvicorn
 from fastapi import FastAPI
 from pydantic import BaseModel
-from ml_utils import load_model, predict, retrain
+from ml_utils import load_model, predict #retrain
 from typing import List
 
 # defining the main app
@@ -49,13 +49,13 @@ def predict_flower(query_data: QueryIn):
     return output
 
 
-@app.post("/feedback_loop", status_code=200)
+#@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"}
+#def feedback_loop(data: List[FeedbackIn]):
+    #retrain(data)
+    #return {"detail": "Feedback loop successful"}
 
 
 # Main function to start the app when main.py is called

ml_utils.py

@@ -33,10 +33,12 @@ def predict(query_data):
 
 
 # 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)
+"""

requirements.txt

@@ -4,3 +4,4 @@ pytest==6.2.4
 requests==2.25.1
 pandas==1.2.4
 scikit-learn==0.24.1
+pytest-cov