import streamlit as st
import joblib
import numpy as np
import plotly.graph_objects as go

# 1. Page Configuration
st.set_page_config(
    page_title="Purchase Intention AI",
    page_icon="🛍️",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for styling
# UPDATED: Added 'color: white;' to .main-header and .sub-text
st.markdown("""
    <style>
    /* Main background dark mode adjustment (optional, for contrast) */
    .stApp {
        background-color: #0E1117;
    }
    
    .stButton>button {
        width: 100%;
        background-color: #FF4B4B;
        color: white;
        font-weight: bold;
        padding: 0.5rem;
        border-radius: 10px;
    }
    
    /* TARGET 1: Main Title */
    .main-header {
        font-size: 2.5rem;
        font-weight: 700;
        color: white !important; /* Forced white color */
        text-align: center;
        margin-bottom: 1rem;
    }
    
    /* TARGET 2: Sub-header text */
    .sub-text {
        text-align: center;
        color: white !important; /* Forced white color */
        font-size: 1.1rem;
        margin-bottom: 2rem;
    }
    </style>
    """, unsafe_allow_html=True)

# 2. Load Model
@st.cache_resource
def load_model():
    try:
        # Try loading the smart pipeline first
        model = joblib.load('src/svm_model.pkl')
        return model, None 
    except:
        try:
            # Fallback to separate files
            model = joblib.load('src/svm_model.pkl')
            scaler = joblib.load('src/scaler.pkl')
            return model, scaler
        except FileNotFoundError:
            return None, None

model, scaler = load_model()

if model is None:
    st.error("🚨 Model files not found! Please run `train_model.py` first.")
    st.stop()

# 3. Header Section (Applies the CSS classes defined above)
st.markdown('<div class="main-header">🛍️ Purchase Intention Predictor</div>', unsafe_allow_html=True)
st.markdown('<p class="sub-text">Adjust the psychometric drivers in the sidebar to predict user behavior.</p>', unsafe_allow_html=True)
st.markdown("---")

# 4. Sidebar - User Inputs
st.sidebar.header("🧠 Psychometric Profiling")
st.sidebar.markdown("Adjust the behavioral scores (1-7 scale):")

def create_slider(label, key, help_text):
    return st.sidebar.slider(
        label, 
        min_value=1.0, 
        max_value=7.0, 
        value=4.5, 
        step=0.1,
        help=help_text
    )

att = create_slider("Attitude (ATT)", "att", "The user's positive or negative feelings toward the behavior.")
sns = create_slider("Subjective Norms (SNs)", "sns", "Social pressure or influence from others to perform the behavior.")
pbc = create_slider("Perceived Control (PBC)", "pbc", "The user's perception of the ease or difficulty of performing the behavior.")
eo  = create_slider("Env. Outcome (EO)", "eo", "Expected environmental benefits resulting from the behavior.")
ec  = create_slider("Env. Concern (EC)", "ec", "General concern for environmental issues.")

# 5. Main Content Area
col1, col2 = st.columns([1, 1.5])

# Prepare Input
input_values = np.array([[att, sns, pbc, eo, ec]])

# Handle Scaling
if scaler:
    final_input = scaler.transform(input_values)
else:
    final_input = input_values 

# Real-time Prediction
prediction = model.predict(final_input)[0]
prediction = max(1.0, min(7.0, prediction))

with col1:
    st.subheader("📊 Prediction Result")
    
    # Gauge Chart
    fig_gauge = go.Figure(go.Indicator(
        mode = "gauge+number",
        value = prediction,
        domain = {'x': [0, 1], 'y': [0, 1]},
        title = {'text': "Purchase Intention (PI)"},
        gauge = {
            'axis': {'range': [1, 7]},
            'bar': {'color': "#FF4B4B"},
            'steps': [
                {'range': [1, 3.5], 'color': "#f8f9fa"},
                {'range': [3.5, 5.5], 'color': "#e9ecef"},
                {'range': [5.5, 7], 'color': "#dee2e6"}
            ],
            'threshold': {
                'line': {'color': "red", 'width': 4},
                'thickness': 0.75,
                'value': prediction
            }
        }
    ))
    fig_gauge.update_layout(height=350, margin=dict(l=20,r=20,t=50,b=20))
    st.plotly_chart(fig_gauge, use_container_width=True)

    # Text Interpretation
    if prediction >= 5.5:
        st.success("High Probability: User is likely to purchase.")
    elif prediction >= 3.5:
        st.info("Moderate Probability: User is undecided.")
    else:
        st.warning("Low Probability: User is unlikely to purchase.")

with col2:
    st.subheader("🕸️ User Profile Analysis")
    
    # Radar Chart
    categories = ['Attitude', 'Social Norms', 'Control', 'Outcome', 'Concern']
    r_values = [att, sns, pbc, eo, ec]
    
    fig_radar = go.Figure()
    fig_radar.add_trace(go.Scatterpolar(
        r=r_values,
        theta=categories,
        fill='toself',
        name='User Profile',
        line_color='#00CC96'
    ))
    
    fig_radar.update_layout(
        polar=dict(
            radialaxis=dict(
                visible=True,
                range=[0, 7]
            )),
        showlegend=False,
        height=350,
        margin=dict(l=40,r=40,t=20,b=20)
    )
    st.plotly_chart(fig_radar, use_container_width=True)

st.markdown("---")
st.markdown("###### *Model: Support Vector Machine (RBF Kernel) | Data Scale: 1 (Strongly Disagree) - 7 (Strongly Agree)*")