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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() |