Deploying format_4_array_wrapper

app.py

@@ -11,14 +11,35 @@ import numpy as np
 import joblib
 import pandas as pd
 import os
+import traceback
 
 # Load the real model
 MODEL_PATH = os.path.join(os.path.dirname(__file__), "iris_knn_pipeline.pkl")
-model = joblib.load(MODEL_PATH)
+model = None
+load_error = None
+
+try:
+    if os.path.exists(MODEL_PATH):
+        model = joblib.load(MODEL_PATH)
+    else:
+        load_error = f"Model file not found at {MODEL_PATH}"
+except Exception as e:
+    load_error = f"Error loading model: {str(e)}\n{traceback.format_exc()}"
 
 def predict_iris(features: List[float]) -> tuple:
-    # Convert to DataFrame as expected by the pipeline
-    df = pd.DataFrame([features], columns=["sepal_length", "sepal_width", "petal_length", "petal_width"])
+    if load_error:
+        raise Exception(f"Model not loaded: {load_error}")
+    
+    # Feature names expected by the scikit-learn pipeline
+    FEATURE_NAMES = [
+        "sepal length (cm)",
+        "sepal width (cm)",
+        "petal length (cm)",
+        "petal width (cm)"
+    ]
+    
+    # Convert to DataFrame with correct column names
+    df = pd.DataFrame([features], columns=FEATURE_NAMES)
     
     pred = int(model.predict(df)[0])
     probs = model.predict_proba(df)[0].tolist()