api change 4

main.py

@@ -1,41 +1,35 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
-from joblib import load
 import numpy as np
+import joblib
 
 app = FastAPI(
-    title="Iris Species Prediction API",
-    description="An API to predict Iris species with class probabilities.",
-    version="1.1.0"
+    title="Iris KNN Prediction API",
+    description="API for predicting Iris species using KNN model",
+    version="1.0.0"
 )
 
-# Load model at startup
 try:
-    model = load("iris_knn.pkl")
-    target_names = ["setosa", "versicolor", "virginica"]
-except FileNotFoundError:
+    model, target_names = joblib.load("iris_knn.pkl")
+except:
     model = None
     target_names = []
 
-
 class IrisData(BaseModel):
     sepal_length: float
     sepal_width: float
     petal_length: float
     petal_width: float
 
-
 @app.get("/")
-def read_root():
-    return {"message": "Welcome! Use POST /predict to classify Iris data."}
-
+def root():
+    return {"message": "Iris KNN API Running! Visit /docs to test the API."}
 
 @app.post("/predict")
 def predict_iris(data: IrisData):
     if model is None:
-        return {"error": "Model not found on server."}
+        return {"error": "Model not found on server"}
 
-    # Convert input
     arr = np.array([[
         data.sepal_length,
         data.sepal_width,
@@ -43,15 +37,8 @@ def predict_iris(data: IrisData):
         data.petal_width
     ]])
 
-    # Predictions
-    pred_idx = int(model.predict(arr)[0])
-    probas = model.predict_proba(arr)[0]
-
+    pred = model.predict(arr)[0]
     return {
         "input": data.dict(),
-        "predicted_class_index": pred_idx,
-        "predicted_class_name": target_names[pred_idx],
-        "class_probabilities": {
-            target_names[i]: float(probas[i]) for i in range(len(target_names))
-        }
+        "predicted_class": str(target_names[pred])
     }