import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import math
import matplotlib.transforms as transforms
import sqlite3

# Import FPSO-specific modules
from reparos.clv import *
from reparos.paz import *
from reparos.dal import *
from reparos.gir import *

# --- UI CONFIG & STYLE ---
st.set_page_config(page_title="Inspekta Deck - FPSO Notifications", layout="wide")

st.markdown("""
    <style>
    @import url('https://fonts.cdnfonts.com/css/tw-cen-mt');
    * {
        font-family: 'Tw Cen MT', sans-serif !important;
    }

    /* Sidebar arrow fix */
    section[data-testid="stSidebar"] [data-testid="stSidebarNav"]::before {
        content: "▶";
        font-size: 1.3rem;
        margin-right: 0.4rem;
    }
    
    /* Fix sidebar expander layout */
    section[data-testid="stSidebar"] [data-testid="stExpander"] {
        margin-bottom: 1rem;
    }
    
    section[data-testid="stSidebar"] [data-testid="stExpander"] [data-testid="stExpanderHeader"] {
        padding: 0.5rem 0.75rem;
        font-size: 0.9rem;
        line-height: 1.2;
        word-wrap: break-word;
        overflow-wrap: break-word;
    }
    
    section[data-testid="stSidebar"] [data-testid="stExpander"] [data-testid="stExpanderContent"] {
        padding: 0.5rem 0.75rem;
    }
    
    /* Ensure proper spacing for sidebar elements */
    section[data-testid="stSidebar"] .stMarkdown {
        margin-bottom: 0.5rem;
    }
    
    section[data-testid="stSidebar"] .stButton {
        margin-top: 0.5rem;
    }
    
    /* Ensure sidebar has proper width */
    section[data-testid="stSidebar"] {
        min-width: 300px;
    }
    
    /* Improve expander content readability */
    section[data-testid="stSidebar"] [data-testid="stExpander"] .stMarkdown {
        font-size: 0.85rem;
        line-height: 1.3;
    }
    
    section[data-testid="stSidebar"] [data-testid="stExpander"] .stMarkdown p {
        margin-bottom: 0.25rem;
    }

    /* Top-right logo placement - responsive to scrolling */
    .logo-container {
        position: absolute;
        top: 1rem;
        right: 2rem;
        z-index: 1000;
        transition: all 0.3s ease;
    }
    
    /* Adjust logo position when scrolling */
    .logo-container.scrolled {
        position: fixed;
        top: 0.5rem;
        right: 1rem;
        transform: scale(0.8);
    }
    
    /* Ensure main content doesn't overlap with logo */
    .main .block-container {
        padding-top: 2rem !important;
    }
    
    /* Smooth transitions for logo */
    .logo-container img {
        transition: all 0.3s ease;
    }
    
    /* Custom styling for better UX */
    .stButton > button {
        border-radius: 8px;
        border: 1px solid #e0e0e0;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        font-weight: 500;
        transition: all 0.3s ease;
    }
    
    .stButton > button:hover {
        transform: translateY(-2px);
        box-shadow: 0 4px 12px rgba(0,0,0,0.15);
    }
    
    /* Custom styling for metrics */
    .metric-container {
        background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
        padding: 1rem;
        border-radius: 10px;
        color: white;
        text-align: center;
        margin: 0.5rem 0;
    }
    
    /* Custom styling for charts */
    .chart-container {
        background: white;
        padding: 1rem;
        border-radius: 10px;
        box-shadow: 0 2px 10px rgba(0,0,0,0.1);
        margin: 1rem 0;
    }
    
    /* Responsive design */
    @media (max-width: 768px) {
        .logo-container {
            position: relative;
            top: auto;
            right: auto;
            text-align: center;
            margin-bottom: 1rem;
        }
        
        .main .block-container {
            padding-top: 1rem !important;
        }
    }
    </style>
""", unsafe_allow_html=True)

# Add logo and title
st.markdown("""
    <div class="logo-container">
        <h1 style="color: #1f77b4; margin: 0; font-size: 2rem;">🚢 Inspekta Deck</h1>
        <p style="color: #666; margin: 0; font-size: 1rem;">FPSO Notifications Analysis</p>
    </div>
""", unsafe_allow_html=True)

# Main title
st.title("🚢 FPSO Notifications Analysis Platform")
st.markdown("---")

# Initialize session state
if 'data_loaded' not in st.session_state:
    st.session_state.data_loaded = False
if 'df' not in st.session_state:
    st.session_state.df = None
if 'fpsos' not in st.session_state:
    st.session_state.fpsos = []

# Sidebar
with st.sidebar:
    st.header("📊 Data Management")
    
    # File upload
    uploaded_file = st.file_uploader("Upload Excel file", type=['xlsx', 'xls'])
    
    if uploaded_file is not None:
        try:
            # Load data
            df = pd.read_excel(uploaded_file)
            st.session_state.df = df
            st.session_state.data_loaded = True
            st.session_state.fpsos = df['FPSO'].unique().tolist()
            st.success("Data loaded successfully!")
        except Exception as e:
            st.error(f"Error loading file: {str(e)}")
    
    # Database connection (if available)
    if st.button("Load from Database"):
        try:
            # Try multiple possible database paths
            db_paths = [
                'reparos/notifs_data.db',
                'notifs_data.db',
                './reparos/notifs_data.db',
                '../reparos/notifs_data.db'
            ]
            
            df = None
            for db_path in db_paths:
                try:
                    conn = sqlite3.connect(db_path)
                    df = pd.read_sql_query("SELECT * FROM notifications", conn)
                    conn.close()
                    st.success(f"Data loaded from database: {db_path}")
                    break
                except Exception:
                    continue
            
            if df is not None:
                st.session_state.df = df
                st.session_state.data_loaded = True
                st.session_state.fpsos = df['FPSO'].unique().tolist()
            else:
                st.error("Database file not found. Please upload an Excel file instead.")
        except Exception as e:
            st.error(f"Error loading from database: {str(e)}")
    
    if st.session_state.data_loaded:
        st.markdown("---")
        st.header("🎯 Analysis Options")
        
        # FPSO selection
        selected_fpsos = st.multiselect(
            "Select FPSOs to analyze",
            st.session_state.fpsos,
            default=st.session_state.fpsos[:3] if len(st.session_state.fpsos) >= 3 else st.session_state.fpsos
        )
        
        # Date range
        if 'Date' in st.session_state.df.columns:
            st.session_state.df['Date'] = pd.to_datetime(st.session_state.df['Date'])
            min_date = st.session_state.df['Date'].min()
            max_date = st.session_state.df['Date'].max()
            
            date_range = st.date_input(
                "Select date range",
                value=(min_date, max_date),
                min_value=min_date,
                max_value=max_date
            )

# Main content
if st.session_state.data_loaded:
    # Create tabs
    tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([
        "📈 Overview", "🔍 CLV Analysis", "⚡ PAZ Analysis", 
        "🏗️ DAL Analysis", "⚙️ GIR Analysis", "🤖 RAG Assistant"
    ])
    
    with tab1:
        st.header("📊 Overview Dashboard")
        
        # Key metrics
        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            st.metric("Total Notifications", len(st.session_state.df))
        
        with col2:
            st.metric("Unique FPSOs", len(st.session_state.fpsos))
        
        with col3:
            if 'Priority' in st.session_state.df.columns:
                high_priority = len(st.session_state.df[st.session_state.df['Priority'] == 'High'])
                st.metric("High Priority", high_priority)
        
        with col4:
            if 'Status' in st.session_state.df.columns:
                open_notifications = len(st.session_state.df[st.session_state.df['Status'] == 'Open'])
                st.metric("Open Notifications", open_notifications)
        
        # Charts
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("Notifications by FPSO")
            fpso_counts = st.session_state.df['FPSO'].value_counts()
            fig, ax = plt.subplots(figsize=(10, 6))
            fpso_counts.plot(kind='bar', ax=ax)
            plt.xticks(rotation=45)
            plt.tight_layout()
            st.pyplot(fig)
        
        with col2:
            if 'Priority' in st.session_state.df.columns:
                st.subheader("Notifications by Priority")
                priority_counts = st.session_state.df['Priority'].value_counts()
                fig, ax = plt.subplots(figsize=(10, 6))
                priority_counts.plot(kind='pie', autopct='%1.1f%%', ax=ax)
                plt.tight_layout()
                st.pyplot(fig)
    
    with tab2:
        st.header("🔍 CLV Analysis")
        # CLV-specific analysis would go here
        st.info("CLV analysis features will be implemented here")
    
    with tab3:
        st.header("⚡ PAZ Analysis")
        # PAZ-specific analysis would go here
        st.info("PAZ analysis features will be implemented here")
    
    with tab4:
        st.header("🏗️ DAL Analysis")
        # DAL-specific analysis would go here
        st.info("DAL analysis features will be implemented here")
    
    with tab5:
        st.header("⚙️ GIR Analysis")
        # GIR-specific analysis would go here
        st.info("GIR analysis features will be implemented here")
    
    with tab6:
        st.header("🤖 RAG Assistant")
        try:
            # Import RAG chatbot
            from reparos.rag_chatbot import DigiTwinRAG
            
            # Initialize RAG system
            if 'rag_system' not in st.session_state:
                st.session_state.rag_system = DigiTwinRAG()
            
            # Load data into RAG system
            if st.session_state.df is not None:
                st.session_state.rag_system.load_notifications_data(st.session_state.df)
            
            # Render chat interface
            st.session_state.rag_system.render_chat_interface()
            
        except ImportError:
            st.error("RAG module not available. Please install the required dependencies.")
            st.code("pip install -r reparos/requirements_rag.txt")
        except Exception as e:
            st.error(f"Error initializing RAG system: {str(e)}")

else:
    st.info("👆 Please upload an Excel file or load data from database to begin analysis.")
    
    # Show sample data structure
    st.subheader("📋 Expected Data Structure")
    st.markdown("""
    Your Excel file should contain the following columns:
    - **FPSO**: FPSO identifier
    - **Date**: Notification date
    - **Priority**: Notification priority (High, Medium, Low)
    - **Status**: Notification status (Open, Closed, etc.)
    - **Description**: Notification description
    - **Category**: Notification category
    """)
    
    # Show sample data
    sample_data = pd.DataFrame({
        'FPSO': ['CLV', 'PAZ', 'DAL', 'GIR'],
        'Date': ['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04'],
        'Priority': ['High', 'Medium', 'Low', 'High'],
        'Status': ['Open', 'Closed', 'Open', 'Closed'],
        'Description': ['Sample notification 1', 'Sample notification 2', 'Sample notification 3', 'Sample notification 4'],
        'Category': ['Safety', 'Maintenance', 'Operations', 'Safety']
    })
    
    st.dataframe(sample_data)

# Footer
st.markdown("---")
st.markdown("""
<div style="text-align: center; color: #666; padding: 1rem;">
    <p>🚢 <strong>Inspekta Deck</strong> - FPSO Notifications Analysis Platform</p>
    <p>Built with ❤️ by ValonyLabs | Powered by Streamlit</p>
</div>
""", unsafe_allow_html=True)