import streamlit as st

# CSS style for the table
css_style = """
<style>
table {
    width: 100%;
    border-collapse: collapse;
    border: 1px solid black;
}

th {
    background-color: #f2f2f2;
    border: 1px solid black;
    padding: 10px;
    text-align: center;
}

td {
    border: 1px solid black;
    padding: 10px;
}

tr:nth-child(even) {
    background-color: #f9f9f9;
}

tr:nth-child(odd) {
    background-color: #ffffff;
}
</style>
"""

# HTML code for the differences table
html_code = """
<table>
    <tr>
        <th>Aspect</th>
        <th>Machine Learning (ML)</th>
        <th>Deep Learning (DL)</th>
    </tr>
    <tr>
        <td>Definition</td>
        <td>A subset of AI focused on enabling systems to learn from data.</td>
        <td>A subset of ML that uses neural networks to process data.</td>
    </tr>
    <tr>
        <td>Data Dependency</td>
        <td>Performs well on small to medium-sized datasets.</td>
        <td>Requires large datasets to perform effectively.</td>
    </tr>
    <tr>
        <td>Model Complexity</td>
        <td>Uses simple algorithms like linear regression or decision trees.</td>
        <td>Utilizes complex architectures like CNNs and RNNs.</td>
    </tr>
    <tr>
        <td>Computation Power</td>
        <td>Less computationally intensive.</td>
        <td>Highly computationally intensive, often requires GPUs.</td>
    </tr>
    <tr>
        <td>Feature Engineering</td>
        <td>Feature engineering is essential for performance.</td>
        <td>Automatically learns features from data.</td>
    </tr>
    <tr>
        <td>Applications</td>
        <td>Fraud detection, recommendation systems, etc.</td>
        <td>Image recognition, natural language processing, etc.</td>
    </tr>
</table>
"""

# Inject CSS into Streamlit
st.markdown(css_style, unsafe_allow_html=True)

# Render the HTML in Streamlit
st.markdown(html_code, unsafe_allow_html=True)