Update app.py

app.py

@@ -10,3 +10,45 @@ from sklearn.preprocessing import LabelEncoder
 import streamlit as st 
 
 st.title("Rouge Component Model")
+#Reading Dataset
+df = pd.read_csv('identify_rogue_50K_ALL.csv')
+print("Dataset Size:",df.shape)
+
+# Dropping the SRU serial number
+df.drop(['SRU serial number','Date of Manufacture','Last Maintenance Date','date of last failure'], axis = 1, inplace=True)
+
+def label_encoder(df):
+  le = LabelEncoder()
+  cat = df.select_dtypes(include='O').keys()
+  categ = list(cat)
+  df[categ] = df[categ].apply(le.fit_transform)
+  return df
+
+def preprocess_dataset(X):  
+  x = X.values #returns a numpy array
+  min_max_scaler = preprocessing.MinMaxScaler()
+  x_scaled = min_max_scaler.fit_transform(x)
+  X_df = pd.DataFrame(x_scaled)
+  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_test_df = preprocess_dataset(X_test_encoded)
+  x_model = loaded_model = tf.keras.models.load_model(r'/content/drive/MyDrive/Colab Notebooks/HAL/saved_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)
+    else:
+      predicition.append(1)
+  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)