import pandas as pd
import streamlit as st
import joblib
import time
from datetime import datetime
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True  # Disables the check

# =============================================
# SETUP & CONFIGURATION
# =============================================

def set_page_config():
    st.set_page_config(
        page_title="WageWise",
        page_icon="💼",
        layout="wide",
        initial_sidebar_state="expanded"
    )
    
    # Inject custom CSS with dark teal color scheme
    st.markdown("""
    <style>
        :root {
            --primary: #012326;
            --secondary: #011a1c;
            --accent: #014d4f;
            --success: #017a7c;
            --text: #e0f2f3;
            --light: #f8f9fa;
            --gray: #adb5bd;
            --dark: #010f10;
        }
        
        /* Main container */
        .main {
            background-color: #f9fafc;
        }
        
        /* Sidebar styling */
        [data-testid="stSidebar"] {
            background: linear-gradient(180deg, #012326 0%, #011a1c 100%);
            color: white;
        }
        
        /* Input fields */
        .stTextInput, .stNumberInput, .stSelectbox {
            border-radius: 8px !important;
            border: 1px solid #e2e8f0 !important;
        }
        
        /* Input labels - make them visible on dark background */
        .stSelectbox > label, .stNumberInput > label, .stTextInput > label {
            color: white !important;
            font-weight: 500 !important;
        }
        
        /* Buttons */
        .stButton>button {
            background: linear-gradient(90deg, #012326 0%, #014d4f 100%);
            color: white;
            border: none;
            border-radius: 8px;
            padding: 12px 24px;
            font-weight: 500;
            transition: all 0.3s ease;
            box-shadow: 0 2px 10px rgba(1, 35, 38, 0.3);
        }
        
        .stButton>button:hover {
            transform: translateY(-2px);
            box-shadow: 0 4px 15px rgba(1, 35, 38, 0.4);
        }
        
        /* Cards */
        .custom-card {
            background: white;
            border-radius: 12px;
            padding: 24px;
            box-shadow: 0 4px 20px rgba(0,0,0,0.05);
            border: 1px solid #e2e8f0;
            margin-bottom: 20px;
        }
        
        /* Typography */
        h1, h2, h3 {
            color: var(--dark) !important;
        }
        
        .sidebar-title {
            color: white !important;
            font-weight: 700 !important;
        }
        
        /* Custom elements */
        .divider {
            height: 1px;
            background: linear-gradient(90deg, rgba(255,255,255,0) 0%, rgba(255,255,255,0.2) 50%, rgba(255,255,255,0) 100%);
            margin: 20px 0;
        }
        
        .metric-card {
            background: white;
            border-radius: 10px;
            padding: 16px;
            box-shadow: 0 2px 15px rgba(0,0,0,0.03);
        }
        
        /* Animations */
        @keyframes fadeIn {
            from { opacity: 0; transform: translateY(10px); }
            to { opacity: 1; transform: translateY(0); }
        }
        
        .fade-in {
            animation: fadeIn 0.5s ease-out forwards;
        }
        
        .job-title-info {
            color: #64748b;
            font-size: 18px;
            margin-top: 8px;
            font-weight: 500;
        }
        
        /* Update accent colors in prediction card */
        .custom-card h1 {
            color: #014d4f !important;
        }
    </style>
    """, unsafe_allow_html=True)

# =============================================
# DATA & MODEL LOADING
# =============================================

@st.cache_data
def load_data():
    df = pd.read_csv('cleaned_job_salaries.csv')
    if 'Posting Date' in df.columns:
        df['Posting Day'] = pd.to_datetime(df['Posting Date']).dt.day
        df['Posting Month'] = pd.to_datetime(df['Posting Date']).dt.month
    return df

@st.cache_resource
def load_model():
    model = joblib.load('best_decision_tree_model2.pkl')
    
    preprocessor = ColumnTransformer(
        transformers=[
            ('num', StandardScaler(), ['# Of Positions', 'min_experience', 'license_required','bar_admission','driver_license_required', 'Posting Day','Posting Month']),
            ('cat', OneHotEncoder(), ['Agency', 'Posting Type', 'Business Title',
           'Title Classification', 'Job Category', 'Full-Time/Part-Time indicator',
           'Career Level', 'Salary Frequency', 'Level Description',
           'required_degree', 'has_communication_skills'])
        ]
    )
    
    pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('regressor', model)])
    df = load_data()
    pipeline.fit(df[['Agency', 'Posting Type', '# Of Positions', 'Business Title',
           'Title Classification', 'Job Category', 'Full-Time/Part-Time indicator',
           'Career Level', 'Salary Frequency', 'Level Description',
           'required_degree', 'min_experience', 'license_required',
           'bar_admission', 'driver_license_required', 'Posting Day',
           'Posting Month', 'has_communication_skills']], df[['Salary']])
    return pipeline

# =============================================
# COMPONENTS
# =============================================

def prediction_card(title, value, job_title, career_level, icon="💵"):
    st.markdown(f"""
    <div class="custom-card fade-in">
        <div style="display: flex; align-items: center; margin-bottom: 16px;">
            <div style="background: linear-gradient(135deg, #012326 0%, #014d4f 100%); width: 48px; height: 48px; border-radius: 12px; display: flex; align-items: center; justify-content: center; margin-right: 16px;">
                <span style="font-size: 24px;">{icon}</span>
            </div>
            <h3 style="margin: 0;">{title}</h3>
        </div>
        <h1 style="color: #014d4f; margin: 0;">${value:,.2f}</h1>
        <p class="job-title-info">Monthly: ${value/12:,.2f}</p>
        <p class="job-title-info">{job_title} | {career_level}</p>
    </div>
    """, unsafe_allow_html=True)

def company_metric(label, value, change=None):
    st.markdown(f"""
    <div class="metric-card fade-in">
        <p style="color: #64748b; font-size: 14px; margin-bottom: 8px;">{label}</p>
        <h3 style="margin: 0;">{value}</h3>
        {f'<p style="color: {"#10b981" if change >=0 else "#ef4444"}; font-size: 14px; margin: 4px 0 0;">{"+" if change >=0 else ""}{change if change is not None else ""}</p>' if change is not None else ''}
    </div>
    """, unsafe_allow_html=True)

# =============================================
# MAIN APP
# =============================================

def main():
    set_page_config()
    
    # Header
    col1, col2 = st.columns([1, 5])
    with col1:
        st.image("LOGO3.jpg", width=125)
    with col2:
        st.markdown("""
        <div style="display: flex; flex-direction: column; justify-content: center; height: 100%; margin-top: 13px;">
            <h1 style="font-size: 80px; margin: 0; padding: 0; line-height: 1;">WageWise</h1>
            <p style="color: #64748b; font-size: 16px; margin: 5px 0 0 0;">
                AI-powered salary predictions with market intelligence
            </p>
        </div>
        """, unsafe_allow_html=True)

    st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
    
    # Sidebar with dark teal background
    with st.sidebar:
        st.markdown('<h2 class="sidebar-title">Job Details</h2>', unsafe_allow_html=True)
        
        df = load_data()
        
        # Business Title with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Business Title</p>', unsafe_allow_html=True)
        BusinessTitle = st.selectbox('Business Title', df['Business Title'].unique(), label_visibility="collapsed")
        
        Agency = df[df['Business Title'] == BusinessTitle]['Agency'].mode().iloc[0]
        
        # Posting Type with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Posting Type</p>', unsafe_allow_html=True)
        PostingType = st.selectbox('Posting Type', df['Posting Type'].unique(), label_visibility="collapsed")
        
        # Experience with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Experience (years)</p>', unsafe_allow_html=True)
        MinExperience = st.number_input('Experience (years)', 0, 50, 3, label_visibility="collapsed")
        
        # Title Classification with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Title Classification</p>', unsafe_allow_html=True)
        TitleClassification = st.selectbox('Title Classification', df['Title Classification'].unique(), label_visibility="collapsed")
        
        JobCategory = df[df['Business Title'] == BusinessTitle]['Job Category'].mode().iloc[0]
        
        # Employment Type with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Employment Type</p>', unsafe_allow_html=True)
        FullOrPartTime = st.selectbox('Employment Type', df['Full-Time/Part-Time indicator'].unique(), label_visibility="collapsed")
        
        # Career Level with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Career Level</p>', unsafe_allow_html=True)
        CareerLevel = st.selectbox('Career Level', df['Career Level'].unique(), label_visibility="collapsed")
        
        # Required Degree with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Required Degree</p>', unsafe_allow_html=True)
        RequiredDegree = st.selectbox('Required Degree', df['required_degree'].unique(), label_visibility="collapsed")
        
        st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
        st.markdown('<h3 class="sidebar-title">Additional Requirements</h3>', unsafe_allow_html=True)
        
        options = {'Yes': 1, 'No': 0}
        
        # License Required with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">License Required</p>', unsafe_allow_html=True)
        LicenseRequired_option = st.selectbox('License Required', list(options.keys()), label_visibility="collapsed")
        LicenseRequired = options[LicenseRequired_option]
        
        # Bar Admission with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Bar Admission</p>', unsafe_allow_html=True)
        BarAdmission_option = st.selectbox('Bar Admission', list(options.keys()), label_visibility="collapsed")
        BarAdmission = options[BarAdmission_option]
        
        # Driver License with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Driver License</p>', unsafe_allow_html=True)
        DriverLicenseRequired_option = st.selectbox('Driver License', list(options.keys()), label_visibility="collapsed")
        DriverLicenseRequired = options[DriverLicenseRequired_option]
        
        communication_options = {'Yes': True, 'No': False}
        
        # Communication Skills with visible label
        st.markdown('<p style="color: white; font-weight: 500; margin-bottom: 0.5rem;">Communication Skills</p>', unsafe_allow_html=True)
        hasCommunicationSkills = communication_options[st.selectbox('Communication Skills', list(communication_options.keys()), label_visibility="collapsed")]

        career_to_level_description = {
            'Intern / Student Role': ['Intern/Trainee'],
            'Entry-Level Professional': ['Junior-Level', 'Entry Specialist A', 'Entry Specialist B'],
            'Experienced Professional': ['Mid-Level', 'Specialist Level A', 'Specialist Level B', 'Advanced Tech Level'],
            'Mid-Level Manager': ['Manager Level 1', 'Manager Level 2', 'Manager Level 3', 'Manager Level 4', 'Manager Level 5'],
            'Executive / Senior Leadership': ['Executive Manager 1', 'Executive Manager 2', 'Executive Manager 3', 'Lead-Level', 'Mayoral Appointee']
        }
        LevelDescription = career_to_level_description.get(CareerLevel, [None])[0]

        df_forecast = pd.read_excel('forecast.xlsx')
        if BusinessTitle in df_forecast["Business Titles"].values:
            current_date = datetime.now()
            date = f'{current_date.month:02d}.2025'
            NumberOfPositions = df_forecast.loc[df_forecast["Business Titles"] == BusinessTitle, date].values[0]
        else:
            df = pd.read_csv('cleaned_job_salaries.csv')
            NumberOfPositions = df.loc[df["Business Title"] == BusinessTitle]['# Of Positions'].mean()

        if st.button('Predict Salary', use_container_width=True):
            st.session_state.predict_clicked = True
    
    # Main content
    if 'predict_clicked' in st.session_state and st.session_state.predict_clicked:
        pipeline = load_model()
        
        current_date = datetime.today().date()

        input_data = pd.DataFrame({
            'Agency': [Agency],
            'Posting Type': [PostingType],
            '# Of Positions': [NumberOfPositions],
            'Business Title': [BusinessTitle],
            'Title Classification': [TitleClassification],
            'Job Category': [JobCategory],
            'Full-Time/Part-Time indicator': [FullOrPartTime],
            'Career Level': [CareerLevel],
            'Salary Frequency': ["Annual"],
            'Level Description': [LevelDescription],
            'required_degree': [RequiredDegree],
            'min_experience': [MinExperience],
            'license_required': [LicenseRequired],
            'bar_admission': [BarAdmission],
            'driver_license_required': [DriverLicenseRequired],
            'Posting Day': [current_date.day],
            'Posting Month': [current_date.month],
            'has_communication_skills': [hasCommunicationSkills],
        })
        
        with st.spinner('Analyzing job details with our AI model...'):
            time.sleep(1.5)
            predicted_salary = pipeline.predict(input_data)[0]
        
        # Results section
        st.markdown("## Prediction Results")
        
        # Check if NumberOfPositions came from forecast and is not zero
        show_positions = (BusinessTitle in df_forecast["Business Titles"].values) and (NumberOfPositions != 0)

        if show_positions:
            prediction_card(
                "Annual Salary", 
                predicted_salary, 
                f"{BusinessTitle} | {NumberOfPositions:.0f} positions available",
                CareerLevel,
                "💵"
            )
        else:
            prediction_card(
                "Annual Salary", 
                predicted_salary, 
                BusinessTitle,
                CareerLevel,
                "💵"
            )
        
        # Job details
        st.markdown("### Job Summary")
        col1, col2, col3 = st.columns(3)
        with col1:
            company_metric("Position Type", FullOrPartTime)
        with col2:
            company_metric("Experience Required", f"{MinExperience} years")
        with col3:
            company_metric("Education Level", RequiredDegree)
    
    st.markdown("""
    <style>
        .footer {
            position: fixed;
            bottom: 0;
            width: 100%;
            text-align: center;
            padding: 20px 0;
            margin-top: 50px;
            background-color: #f9fafc;
            color: #64748b;
        }
    </style>
    <div class="footer">
        <hr style="border: 0.5px solid #e2e8f0;">
        Created with ❤️ by Senasu & Sude
    </div>
    """, unsafe_allow_html=True)

if __name__ == '__main__':
    main()