Update app.py

app.py

@@ -107,23 +107,23 @@ def preprocess_dataset(X):
   return X_df
 
 def prediction(df):
-  X = df.loc[:,df.columns!= "Rogue LRU/SRU (Target)"]
-  y = df["Rogue LRU/SRU (Target)"]
-  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-  print(X_train.shape)
-  print(X_test.shape)
-  X_test_encoded = label_encoder(X_test)
+  #X = df.loc[:,df.columns!= "Rogue LRU/SRU (Target)"]
+  #y = df["Rogue LRU/SRU (Target)"]
+  #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+  #print(X_train.shape)
+  #print(X_test.shape)
+  X_test_encoded = label_encoder(df)
   X_test_df = preprocess_dataset(X_test_encoded)
   x_model = loaded_model = tf.keras.models.load_model('my_model')
   y_pred = x_model.predict(X_test_df)
-  predicition = []
-  for i in list(y_pred):
-    if i[0]<=0.8:
-      predicition.append(0)
+  #predicition = []
+  #for i in list(y_pred):
+  if y_pred ==0:
+      st.write('Rouge Component is Good')
     else:
-      predicition.append(1)
-  X_test['Actual_time_to_repair'] = y_test
-  X_test['Predicted_time_to_repair'] = predicition
+      st.write('Rouge Component is not good')
+  #X_test['Actual_time_to_repair'] = y_test
+  #X_test['Predicted_time_to_repair'] = predicition
   # X_test.to_csv(r'/content/drive/MyDrive/Colab Notebooks/HAL/rogue_test_data.csv')
-  print(X_test.head())
-prediction(df)
+  #print(X_test.head())
+prediction(user_data)