Create app.py

app.py

@@ -0,0 +1,102 @@
+import streamlit as st
+import pandas as pd
+from utils.data_loader import load_data
+from utils.model_loader import evaluate_model, get_test_predictions
+from visualizations.plot_functions import plot_metrics
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix, roc_curve, auc
+
+def load_css():
+    with open("static/style.css") as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+def load_js():
+    with open("static/script.js") as f:
+        st.markdown(f"<script>{f.read()}</script>", unsafe_allow_html=True)
+
+def main():
+    st.set_page_config(page_title="ML Dashboard", layout="wide")
+    load_css()
+    load_js()
+
+    # Sidebar for filters or model selection
+    st.sidebar.title("Options")
+    selected_model = st.sidebar.selectbox("Select Model", ["Logistic Regression", "Decision Tree", "Random Forest", "Gradient Boosting", "SVM"])
+
+    # Main sections
+    st.title("ML Dashboard")
+
+    # Dataset display
+    st.header("Dataset")
+    df = load_data()
+    st.dataframe(df.head())
+
+    # Model evaluation metrics
+    st.header("Model Evaluation")
+    metrics = evaluate_model(selected_model)
+
+    # Tabs for better organization
+    tab1, tab2, tab3 = st.tabs(["Metrics Table", "Bar Plot", "Confusion Matrix"])
+
+    # Load results from the notebook
+    @st.cache_data
+    def load_results():
+        # Simulate loading results from the notebook
+        results = {
+            'Model': ['Logistic Regression', 'Decision Tree', 'Random Forest', 'Gradient Boosting', 'SVM'],
+            'Accuracy': [0.85, 0.83, 0.87, 0.88, 0.84],
+            'Precision': [0.82, 0.80, 0.86, 0.87, 0.81],
+            'Recall': [0.78, 0.76, 0.84, 0.85, 0.79],
+            'F1 Score': [0.80, 0.78, 0.85, 0.86, 0.80]
+        }
+        return pd.DataFrame(results)
+
+    # Display results in the app
+    st.header("Model Results from Notebook")
+    results_df = load_results()
+    st.dataframe(results_df)
+
+    # Update Metrics Table to match the style of Model Results from Notebook
+    with tab1:
+        st.write("### Metrics Table")
+        st.dataframe(results_df)  # Use the same dataframe display style as the notebook results
+
+    # Update Bar Plot to use notebook results
+    with tab2:
+        st.write("### F1 Score Comparison")
+        fig, ax = plt.subplots()
+        sns.barplot(data=results_df, x='Model', y='F1 Score', ax=ax, palette="viridis")
+        ax.set_title("F1 Score by Model")
+        ax.set_ylabel("F1 Score")
+        ax.set_xlabel("Model")
+        st.pyplot(fig)
+
+    # Update Confusion Matrix to use notebook results
+    with tab3:
+        st.write("### Confusion Matrix")
+        # Simulate confusion matrix data
+        cm = [[50, 10], [5, 35]]  # Example data
+        fig, ax = plt.subplots()
+        sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", ax=ax)
+        ax.set_title("Confusion Matrix")
+        ax.set_xlabel("Predicted")
+        ax.set_ylabel("Actual")
+        st.pyplot(fig)
+
+    # Optional: ROC Curve
+    if st.sidebar.checkbox("Show ROC Curve"):
+        st.write("### ROC Curve")
+        fpr, tpr, _ = roc_curve(y_test, y_pred)
+        roc_auc = auc(fpr, tpr)
+        fig, ax = plt.subplots()
+        ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
+        ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+        ax.set_title("Receiver Operating Characteristic")
+        ax.set_xlabel("False Positive Rate")
+        ax.set_ylabel("True Positive Rate")
+        ax.legend(loc="lower right")
+        st.pyplot(fig)
+
+if __name__ == "__main__":
+    main()