import streamlit as st

def render_header():
    st.markdown("<h1>Deepfake Image Detector</h1>", unsafe_allow_html=True)
    st.markdown("<p class='subtitle'>Select or drag an image below to analyze it.</p>", unsafe_allow_html=True)

def render_result(is_fake, confidence_percentage):
    if is_fake:
        box_class = "result-box-fake"
        display_label = "AI-Generated (Fake)"
        color_fill = "#8b5cf6"
    else:
        box_class = "result-box-real"
        display_label = "Authentic (Real)"
        color_fill = "#7dd3fc"
        st.balloons()
        
    html_result = f"""
    <div class='{box_class}'>
        <h3 style="color: #fff; margin-bottom: 5px;">Result: {display_label}</h3>
        <div class='confidence'>
            Confidence: {confidence_percentage}%
            <div class='confidence-bar-container'>
                <div class='confidence-bar-fill' style='width: {confidence_percentage}%; background: linear-gradient(90deg, #7dd3fc, #8b5cf6);'></div>
            </div>
        </div>
    </div>
    """
    st.markdown(html_result, unsafe_allow_html=True)

def render_footer():
    st.markdown("<div style='margin-top: 20px; font-size: 0.8em; color: #777; text-align: center;'>Powered by Deep Learning • v1.0</div>", unsafe_allow_html=True)