import streamlit as st
import pandas as pd
import numpy as np
import joblib
import os
import sys

# Add project root to sys.path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import path_utils

# Page Configuration
st.set_page_config(
    page_title="ReliabilityPulse | AI-Driven Maintenance", 
    layout="wide", 
    page_icon="⚡", 
    initial_sidebar_state="expanded"
)

# Premium Custom CSS (Glassmorphism + Neon Industrial Theme)
st.markdown("""
<style>
    @import url('https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;600&family=Inter:wght@300;400;600&display=swap');
    
    html, body, [class*="css"]  {
        font-family: 'Inter', sans-serif;
    }
    
    h1, h2, h3 {
        font-family: 'Outfit', sans-serif;
        font-weight: 600;
        letter-spacing: -0.5px;
    }

    .main {
        background-color: #0B0E14;
        color: #E6E6E6;
    }

    /* Glassmorphism Sidebar */
    [data-testid="stSidebar"] {
        background-color: rgba(22, 27, 34, 0.95);
        backdrop-filter: blur(10px);
        border-right: 1px solid rgba(255, 255, 255, 0.1);
    }

    /* Premium Metric Card */
    div[data-testid="stMetric"] {
        background: rgba(255, 255, 255, 0.03);
        backdrop-filter: blur(12px);
        border: 1px solid rgba(255, 255, 255, 0.1);
        border-radius: 15px;
        padding: 20px !important;
        box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.3);
        transition: all 0.3s ease;
    }
    
    div[data-testid="stMetric"]:hover {
        border-color: #00D4FF;
        transform: translateY(-5px);
    }

    /* Custom Progress Bar Color */
    .stProgress > div > div > div > div {
        background-image: linear-gradient(to right, #00D4FF, #B026FF);
    }

    /* Tab Customization */
    .stTabs [data-baseweb="tab-list"] {
        gap: 10px;
        background-color: transparent;
    }

    .stTabs [data-baseweb="tab"] {
        height: 44px;
        background-color: #161B22;
        border-radius: 8px 8px 0px 0px;
        padding: 8px 24px;
        font-weight: 600;
        transition: all 0.2s;
    }

    .stTabs [data-baseweb="tab"]:hover {
        background-color: #1F242D;
        color: #00D4FF;
    }

    .stTabs [aria-selected="true"] {
        background-color: #21262D !important;
        border-bottom: 2px solid #00D4FF !important;
    }
    
    .stImage > img {
        border-radius: 10px;
        border: 1px solid rgba(255, 255, 255, 0.1);
    }
</style>
""", unsafe_allow_html=True)

# Helper to load models
@st.cache_resource
def load_all_resources():
    try:
        models = {
            "XGBoost": joblib.load(path_utils.get_model_path('xgboost_model.pkl')),
            "Random Forest": joblib.load(path_utils.get_model_path('random_forest.pkl')),
            "Decision Tree": joblib.load(path_utils.get_model_path('decision_tree.pkl'))
        }
        scaler = joblib.load(path_utils.get_model_path('scaler.pkl'))
        return models, scaler
    except Exception as e:
        return None, None

all_models, scaler = load_all_resources()
models_ready = all_models is not None

# SIDEBAR: Control Center
with st.sidebar:
    st.image(path_utils.get_output_path('failure_distribution.png'), use_container_width=True)
    st.title("🛡️ Control Center")
    st.caption("Industrial Reliability Forecasting")
    
    st.markdown("---")
    selected_model_name = st.selectbox("AI Model", ["XGBoost", "Random Forest", "Decision Tree"])
    st.markdown("---")
    st.header("Sensor Telemetry")
    
    with st.expander("🌡️ Temperature", expanded=True):
        input_air_temp = st.slider("Ambient [K]", 295.0, 305.0, 300.0, 0.1)
        input_proc_temp = st.slider("Process [K]", 305.0, 315.0, 310.0, 0.1)
    
    with st.expander("⚙️ Mechanical", expanded=True):
        input_rpm = st.number_input("Speed [rpm]", 1200, 2800, 1500)
        input_torque = st.number_input("Torque [Nm]", 3.0, 76.0, 40.0, 0.1)
    
    with st.expander("🛠️ Tool Wear", expanded=True):
        input_type = st.selectbox("Market Grade", ["L", "M", "H"])
        input_tool_wear = st.slider("Duration [min]", 0, 250, 100)

# MAIN: ReliabilityPulse
st.title("⚡ ReliabilityPulse")
st.markdown("### Industrial Reliability Forecasting & Maintenance Hub")

# Tabs
tab_predict, tab_viz, tab_about = st.tabs([
    "🚀 Real-time Diagnostics", 
    "📈 Visual Data Engine", 
    "📁 Asset Documentation"
])

# FRAGMENT: Prediction Stability
@st.fragment
def run_stable_diagnostics():
    if not models_ready:
        st.warning("Core engine is offline. Please ensure models/ directory is populated.")
        return

    type_map = {"L": 0, "M": 1, "H": 2}
    type_val = type_map[input_type]
    temp_diff = input_proc_temp - input_air_temp
    power = input_torque * (input_rpm * 2 * np.pi / 60)
    tool_wear_torque = input_tool_wear * input_torque

    input_df = pd.DataFrame([[
        type_val, input_air_temp, input_proc_temp, input_rpm, input_torque, input_tool_wear, 
        temp_diff, power, tool_wear_torque
    ]], columns=['Type', 'Air temperature [K]', 'Process temperature [K]', 
                'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 
                'temp_diff', 'power', 'tool_wear_torque'])

    input_scaled = scaler.transform(input_df)
    model = all_models[selected_model_name]
    prob = model.predict_proba(input_scaled)[0, 1]

    if prob < 0.2: status, s_icon = "OPTIMAL", "✅"
    elif prob < 0.5: status, s_icon = "MONITOR", "⚠️"
    elif prob < 0.8: status, s_icon = "WARNING", "🚨"
    else: status, s_icon = "CRITICAL", "🛑"

    # RESTORED 3-COLUMN METRICS
    c1, c2, c3 = st.columns(3)
    with c1: st.metric("Failure Probability", f"{prob*100:.1f}%")
    with c2: st.metric("System Health", status)
    with c3: st.metric("Tool Stress", f"{tool_wear_torque:.1f}")

    st.write("---")
    st.markdown(f"#### Asset Health: {s_icon} **{status}**")
    st.progress(min(int(prob * 100), 100))
    st.info(f"**Diagnostic Metric**: `Power: {power:.1f}W` | `Delta: {temp_diff:.1f}K` | `Stress: {tool_wear_torque:.1f}`")

# FRAGMENT: Visual Stability
@st.fragment
def render_visual_engine():
    st.header("📈 Visual Data Engine")
    
    # ROW 1
    v_c1, v_c2 = st.columns(2)
    with v_c1:
        st.image(path_utils.get_output_path('failure_distribution.png'), use_container_width=True)
        st.info("**Insight**: Class imbalance handled via SMOTE.")
    with v_c2:
        st.image(path_utils.get_output_path('failure_rate_by_type.png'), use_container_width=True)
        st.info("**Insight**: Baseline risk varies significantly by asset grade.")

    st.write("---")
    # ROW 2
    v_c3, v_c4 = st.columns(2)
    with v_c3:
        st.image(path_utils.get_output_path('numeric_distributions.png'), use_container_width=True)
        st.info("**Insight**: Envelopes show high variance in Torque and RPM.")
    with v_c4:
        st.image(path_utils.get_output_path('sensor_boxplots.png'), use_container_width=True)
        st.info("**Insight**: Outliers typically precede tool failure events.")

    st.write("---")
    # ROW 3
    v_c5, v_c6 = st.columns(2)
    with v_c5:
        st.image(path_utils.get_output_path('feature_importance.png'), use_container_width=True)
        st.info("**Insight**: Tool Wear & Torque interaction is the top predictor.")
    with v_c6:
        st.image(path_utils.get_output_path('correlation_heatmap.png'), use_container_width=True)
        st.info("**Insight**: Thermal delta captures heat transfer efficiency.")

    st.write("---")
    # ROW 4
    v_c7, v_c8 = st.columns(2)
    with v_c7:
        st.image(path_utils.get_output_path('sub_label_counts.png'), use_container_width=True)
        st.info("**Insight**: HDF and PWF are dominant failure modes.")
    with v_c8:
        st.image(path_utils.get_output_path('anomaly_scores.png'), use_container_width=True)
        st.info("**Insight**: Isolation Forest effectively flags novel signatures.")

    st.write("---")
    # ROW 5
    v_c9, v_c10 = st.columns(2)
    with v_c9:
        st.image(path_utils.get_output_path('roc_curve_comparison.png'), use_container_width=True)
        st.info("**Insight**: XGBoost maintains ~0.95 AUC accuracy.")
    with v_c10:
        st.image(path_utils.get_output_path('confusion_matrix_xgboost.png'), use_container_width=True)
        st.info("**Insight**: High Recall prioritizes asset uptime.")

# --- Render Tabs ---
with tab_predict:
    run_stable_diagnostics()

with tab_viz:
    render_visual_engine()

with tab_about:
    st.header("🏭 Project Overview & Roadmap")
    st.markdown("""
    **ReliabilityPulse** is an industrial-grade intelligence suite designed to protect manufacturing revenue. 
    By forecasting failures **24-72 hours** in advance, it transforms reactive maintenance into a proactive strategy.
    
    ### 🎯 Tech Stack:
    - **Modeling**: Python 3.12, XGBoost, Scikit-Learn
    - **Frontend**: Streamlit with Custom Glassmorphism CSS
    - **Storage**: Git LFS for large scale binary assets
    - **Data**: UCI AI4I 2020 Predictive Maintenance
    
    ### 🛠️ Operational Flow:
    1. **Sensing**: Telemetry ingestion via the Control Center.
    2. **Analysis**: Real-time cross-referencing with historical failure signatures.
    3. **Strategy**: Automated maintenance triggers based on calculated Risk Levels.
    """)

# Footer
st.markdown("---")
st.write(f"Built by **Divyanshi Singh** | [LinkedIn](https://www.linkedin.com/in/divyanshi-singh-/) | [GitHub](https://github.com/Divyanshi018572)")