Update app.py
Browse files
app.py
CHANGED
@@ -10,3 +10,45 @@ from sklearn.preprocessing import LabelEncoder
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import streamlit as st
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st.title("Rouge Component Model")
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import streamlit as st
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st.title("Rouge Component Model")
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#Reading Dataset
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df = pd.read_csv('identify_rogue_50K_ALL.csv')
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print("Dataset Size:",df.shape)
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# Dropping the SRU serial number
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df.drop(['SRU serial number','Date of Manufacture','Last Maintenance Date','date of last failure'], axis = 1, inplace=True)
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def label_encoder(df):
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le = LabelEncoder()
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cat = df.select_dtypes(include='O').keys()
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categ = list(cat)
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df[categ] = df[categ].apply(le.fit_transform)
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return df
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def preprocess_dataset(X):
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x = X.values #returns a numpy array
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min_max_scaler = preprocessing.MinMaxScaler()
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x_scaled = min_max_scaler.fit_transform(x)
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X_df = pd.DataFrame(x_scaled)
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return X_df
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def prediction(df):
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X = df.loc[:,df.columns!= "Rogue LRU/SRU (Target)"]
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y = df["Rogue LRU/SRU (Target)"]
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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print(X_train.shape)
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print(X_test.shape)
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X_test_encoded = label_encoder(X_test)
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X_test_df = preprocess_dataset(X_test_encoded)
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x_model = loaded_model = tf.keras.models.load_model(r'/content/drive/MyDrive/Colab Notebooks/HAL/saved_model/my_model')
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y_pred = x_model.predict(X_test_df)
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predicition = []
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for i in list(y_pred):
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if i[0]<=0.8:
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predicition.append(0)
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else:
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predicition.append(1)
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X_test['Actual_time_to_repair'] = y_test
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X_test['Predicted_time_to_repair'] = predicition
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# X_test.to_csv(r'/content/drive/MyDrive/Colab Notebooks/HAL/rogue_test_data.csv')
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print(X_test.head())
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prediction(df)
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