added all

inference.py

@@ -1,20 +1,13 @@
+# inference.py
+
 import joblib
 import numpy as np
-from sklearn.preprocessing import StandardScaler
-
-# Load the model and scaler
-model = joblib.load("classification_model.joblib")
-scaler = joblib.load("scaler.pkl")
 
-def predict(features):
-    # Scale the features
-    scaled_features = scaler.transform(np.array(features).reshape(1, -1))
-    prediction = model.predict(scaled_features)
-    return prediction[0]
+# Load your model (no scaler)
+model = joblib.load('classification_model.joblib')
 
-# Sample usage
-if __name__ == "__main__":
-    # Sample feature data (replace with real data when calling)
-    sample_data = [0.5, 1.2, -0.3, 2.0]
-    result = predict(sample_data)
-    print(f"Prediction: {result}")
+def predict(input_features):
+    # Make sure the input is a 2D array (batch_size, num_features)
+    input_array = np.array(input_features).reshape(1, -1)
+    prediction = model.predict(input_array)
+    return prediction.tolist()  # Return the prediction as a list for easy JSON handling