Update app.py

app.py

@@ -37,39 +37,39 @@ def save_model_artifacts(regressor, scaler, pca):
     with open('pca.pkl', 'wb') as f:
         pickle.dump(pca, f, protocol=pickle.HIGHEST_PROTOCOL)
 
-def preprocess_data(X):
-    print('preprocess_data X',X)
-    # Load the trained scaler and PCA
-    scaler = joblib.load('scaler.pkl')
-    pca = joblib.load('pca.pkl')
+# def preprocess_data(X):
+#     print('preprocess_data X',X)
+#     # Load the trained scaler and PCA
+#     scaler = joblib.load('scaler.pkl')
+#     pca = joblib.load('pca.pkl')
 
-    # Check if the input has 8 features
-    if X.shape[1] != 8:
-        raise ValueError("Input data should have 8 features.")
+#     # Check if the input has 8 features
+#     if X.shape[1] != 8:
+#         raise ValueError("Input data should have 8 features.")
 
-    # Scale the input data using the loaded scaler
-    X_scaled = scaler.transform(X)
-    print("X_scaled",X_scaled)
-    # Apply PCA using the loaded PCA transformer
-    X_pca = pca.transform(X_scaled)
-    print("X_pca",X_pca)
+#     # Scale the input data using the loaded scaler
+#     X_scaled = scaler.transform(X)
+#     print("X_scaled",X_scaled)
+#     # Apply PCA using the loaded PCA transformer
+#     # X_pca = pca.transform(X_scaled) somehow dapat 4
+#     # print("X_pca",X_pca)
 
-    return X_pca
+#     return X_pca
 
 def predict_slump(cement, blast_furnace_slag, fly_ash, water, superplasticizer, coarse_aggregate, fine_aggregate, FLOW):
     # Prepare the input data
     X = np.array([[cement, blast_furnace_slag, fly_ash, water, superplasticizer, coarse_aggregate, fine_aggregate, FLOW]])
 
     # Preprocess the data
-    X_preprocessed = preprocess_data(X)
-    print("predict_slump X",X)
-    print("predict_slump X_preprocessed",X_preprocessed)
+    # X_preprocessed = preprocess_data(X)
+    # print("predict_slump X",X)
+    # print("predict_slump X_preprocessed",X_preprocessed)
 
     # Load the trained model
     regressor = joblib.load('slump_regressor.pkl')
 
     # Make the prediction
-    slump_prediction = regressor.predict(X_preprocessed)[0]
+    slump_prediction = regressor.predict(X)[0]
 
     return slump_prediction