diff --git a/main.py b/main.py
index 7351584f..99cf35de 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,8 @@
 import uvicorn
 from fastapi import FastAPI
 from pydantic import BaseModel
-from ml_utils import load_model, predict
+from ml_utils import load_model, predict, retrain
+from typing import List
 
 # defining the main app
 app = FastAPI(title="Iris Predictor", docs_url="/")
@@ -21,6 +22,13 @@ class QueryIn(BaseModel):
 class QueryOut(BaseModel):
     flower_class: str
 
+# class which is expected in the payload while re-training
+class FeedbackIn(BaseModel):
+    sepal_length: float
+    sepal_width: float
+    petal_length: float
+    petal_width: float
+    flower_class: str
 
 # Route definitions
 @app.get("/ping")
@@ -33,6 +41,11 @@ def predict_flower(query_data: QueryIn):
     output = {"flower_class": predict(query_data)}
     return output
 
+@app.post("/feedback_loop", status_code=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__":
diff --git a/ml_utils.py b/ml_utils.py
index 75a358b8..24703d04 100644
--- a/ml_utils.py
+++ b/ml_utils.py
@@ -6,6 +6,7 @@
 clf = GaussianNB()
 
 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():
@@ -24,3 +25,8 @@ def predict(query_data):
     prediction = clf.predict([x])[0]
     print(f"Model prediction: {classes[prediction]}")
     return classes[prediction]
+
+def retrain(data):
+    X = [list(d.dict().values())[:-1] for d in data]
+    y = [r_classes[d.flower_class] for d in data]
+    clf.fit(X, y)