import streamlit as st

def main():
    st.title("Step 8: Model Evaluation")

    st.markdown("""
        ### **Model Evaluation** :clipboard:

        After training your model, it's time to **evaluate** how well it performs. This step is crucial to determine if your model can generalize well to unseen data.

        **:scales: Why Evaluate the Model?**
        - **Measure Accuracy**: Check how accurately your model makes predictions.
        - **Avoid Overfitting**: Ensure your model performs well not only on training data but also on new, unseen data.

        **Key Evaluation Metrics**:
        - **Accuracy**: The percentage of correct predictions.
        - **Precision**: How many of the predicted positive cases are actually positive.
        - **Recall**: How many of the actual positive cases are correctly predicted.
        - **F1 Score**: The balance between precision and recall.
        - **Confusion Matrix**: A visual representation of true vs. predicted values.

        **:key: Evaluation Flow**:
        - If your **evaluation score is less than 90%**, it's time to go **back to Step 6 (Feature Engineering)**. This means the features might need improvement.
        - If the score is still **below 90% after revisiting Step 6**, consider **changing the model**. Sometimes, a different algorithm or model might perform better.
        - If your **score is greater than 90%**, congratulations! You can move forward to **model deployment**.

        **:rocket: In Short**: Model evaluation helps you assess how well your model performs. Based on the evaluation score, you'll either refine your model or proceed to deployment.
    """)

    st.divider()
main()