Delete app.py

app.py

@@ -1,99 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder
-from sklearn.compose import ColumnTransformer
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Input, Dense
-from sklearn.metrics import classification_report, confusion_matrix
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# Load data
-@st.cache_data
-def load_data():
-    return pd.read_csv("Global_Cybersecurity_Threats_2015-2024.csv")
-
-df = load_data()
-st.title("Cybersecurity Attack Type Classifier")
-
-st.write("### Data Preview", df.head())
-
-# Define features
-target = 'Attack Type'
-
-cat_features = [
-    'Country',
-    'Target Industry',
-    'Attack Source',
-    'Security Vulnerability Type',
-    'Defense Mechanism Used'
-]
-
-num_features = [
-    'Year',
-    'Financial Loss (in Million $)',
-    'Number of Affected Users',
-    'Incident Resolution Time (in Hours)'
-]
-
-# Preprocessing
-X = df.drop(columns=[target])
-y = df[target]
-
-preprocessor = ColumnTransformer(
-    transformers=[
-        ('num', StandardScaler(), num_features),
-        ('cat', OneHotEncoder(handle_unknown='ignore'), cat_features)
-    ]
-)
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=27)
-X_train = preprocessor.fit_transform(X_train)
-X_test = preprocessor.transform(X_test)
-
-le = LabelEncoder()
-y_train = le.fit_transform(y_train)
-y_test = le.transform(y_test)
-
-# Model settings
-st.sidebar.header("Model Parameters")
-epochs = st.sidebar.slider("Epochs", 5, 100, 30)
-batch_size = st.sidebar.selectbox("Batch Size", [8, 16, 32, 64], index=1)
-
-# Build model
-model = Sequential()
-model.add(Input(shape=(X_train.shape[1],)))
-model.add(Dense(16, activation='relu'))
-model.add(Dense(32, activation='relu'))
-model.add(Dense(len(np.unique(y)), activation='softmax'))
-model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
-
-if st.button("Train Model"):
-    history = model.fit(X_train, y_train, validation_split=0.2, epochs=epochs, batch_size=batch_size, verbose=0)
-    st.success("Model training complete.")
-
-    # Plot loss
-    st.subheader("Training History")
-    fig, ax = plt.subplots()
-    ax.plot(history.history['loss'], label='Training Loss')
-    ax.plot(history.history['val_loss'], label='Validation Loss')
-    ax.legend()
-    st.pyplot(fig)
-
-    # Evaluate
-    st.subheader("Evaluation")
-    test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)
-    st.write(f"Test Accuracy: **{test_acc:.4f}**")
-
-    y_pred = np.argmax(model.predict(X_test), axis=1)
-    cm = confusion_matrix(y_test, y_pred)
-    st.write("### Confusion Matrix")
-    fig, ax = plt.subplots()
-    sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", ax=ax)
-    st.pyplot(fig)
-
-    report = classification_report(y_test, y_pred, target_names=le.classes_, output_dict=True)
-    st.write("### Classification Report")
-    st.dataframe(pd.DataFrame(report).transpose())