Delete app.py (#1)

- Delete app.py (8140b7ff1f785835599fe01917ec3e3503e168c4)


Co-authored-by: moncher <[email protected]>

app.py

@@ -1,108 +0,0 @@
-import streamlit as st
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import classification_report, confusion_matrix
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# Set the title of the app
-st.title("Cybersecurity Model Training App")
-
-# Sidebar for dataset upload and parameter selection
-st.sidebar.header("Upload Dataset and Parameters")
-
-# File uploader for dataset
-uploaded_file = st.sidebar.file_uploader("Upload your CSV dataset", type=["csv"])
-
-# Function to load and display dataset
-def load_data(file):
-    data = pd.read_csv(file)
-    st.write("Dataset Preview:")
-    st.dataframe(data.head())
-    return data
-
-# Load dataset if file is uploaded
-if uploaded_file is not None:
-    data = load_data(uploaded_file)
-
-    # Select target variable
-    target = st.sidebar.selectbox("Select the target variable", data.columns)
-
-    # Select features
-    features = st.sidebar.multiselect("Select feature variables", [col for col in data.columns if col != target])
-
-    # Split ratio
-    test_size = st.sidebar.slider("Test size ratio", 0.1, 0.5, 0.3)
-
-    # Model selection
-    model_choice = st.sidebar.selectbox("Select Model", ["Random Forest", "Support Vector Machine", "Logistic Regression"])
-
-    # Hyperparameters
-    if model_choice == "Random Forest":
-        n_estimators = st.sidebar.slider("Number of trees in the forest", 10, 100, 50)
-        max_depth = st.sidebar.slider("Maximum depth of the tree", 1, 20, 10)
-    elif model_choice == "Support Vector Machine":
-        c_value = st.sidebar.slider("Regularization parameter (C)", 0.01, 10.0, 1.0)
-        kernel = st.sidebar.selectbox("Kernel type", ["linear", "rbf", "poly"])
-    elif model_choice == "Logistic Regression":
-        c_value = st.sidebar.slider("Inverse of regularization strength (C)", 0.01, 10.0, 1.0)
-
-    # Train model button
-    if st.sidebar.button("Train Model"):
-        if len(features) == 0:
-            st.warning("Please select at least one feature.")
-        else:
-            X = data[features]
-            y = data[target]
-
-            # Split the data
-            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
-
-            # Initialize and train the model
-            if model_choice == "Random Forest":
-                model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42)
-            elif model_choice == "Support Vector Machine":
-                from sklearn.svm import SVC
-                model = SVC(C=c_value, kernel=kernel, probability=True, random_state=42)
-            elif model_choice == "Logistic Regression":
-                from sklearn.linear_model import LogisticRegression
-                model = LogisticRegression(C=c_value, max_iter=1000, random_state=42)
-
-            model.fit(X_train, y_train)
-
-            # Make predictions
-            y_pred = model.predict(X_test)
-
-            # Display evaluation metrics
-            st.subheader("Model Evaluation")
-            st.text("Classification Report:")
-            st.text(classification_report(y_test, y_pred))
-
-            # Confusion matrix
-            st.text("Confusion Matrix:")
-            cm = confusion_matrix(y_test, y_pred)
-            fig, ax = plt.subplots()
-            sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", xticklabels=model.classes_, yticklabels=model.classes_)
-            plt.xlabel("Predicted")
-            plt.ylabel("Actual")
-            st.pyplot(fig)
-
-            # Feature importance for Random Forest
-            if model_choice == "Random Forest":
-                st.subheader("Feature Importance")
-                feature_importance = pd.DataFrame({'Feature': features, 'Importance': model.feature_importances_})
-                feature_importance = feature_importance.sort_values(by='Importance', ascending=False)
-                st.bar_chart(feature_importance.set_index('Feature'))
-
-# Instructions when no file is uploaded
-else:
-    st.write("Please upload a CSV file to get started.")
-
-# Additional resources
-st.sidebar.header("Additional Resources")
-st.sidebar.markdown("""
-- [Streamlit Documentation](https://docs.streamlit.io/)
-- [Scikit-learn Documentation](https://scikit-learn.org/stable/user_guide.html)
-- [Cybersecurity Datasets](https://www.kaggle.com/datasets?search=cybersecurity)
-""")