Update app.py

app.py

@@ -28,11 +28,8 @@ forecast_ngb_features = forecast_ngb_artifact["feature_names"]
 kmeans_artifact = joblib.load("kmeans_model.joblib")
 kmeans_model = kmeans_artifact["model"]
 cluster_scaler = kmeans_artifact["scaler"]
-cluster_features_names = [
-    f for f in kmeans_artifact["feature_names"]
-    if f != "cluster"
-]
-
+# Only use the features that were actually used during training
+cluster_features_names = ["close", "volume", "trend", "fg_index"]
 
 
 ################### FEATURE GATHERING
@@ -121,7 +118,6 @@ def assign_cluster(df):
     df["cluster"] = kmeans_model.predict(scaled)
     return df
 
-
 ################### PREDICTION FUNCTION
 def predict_volatility(date):
     # Fetch raw data