import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
from datetime import datetime

# 1. PAGE CONFIGURATION
st.set_page_config(
    page_title="S.T.A.R.K AI | UIDAI Fraud Detection",
    page_icon="",
    layout="wide",
    initial_sidebar_state="expanded"
)

# 2. PROFESSIONAL STYLING (THEME OVERRIDE)
st.markdown("""
    <style>
        /* IMPORT FONTS */
        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
        
        /* FORCE LIGHT THEME BACKGROUNDS & TEXT */
        .stApp {
            background-color: #f8fafc; /* Light Blue-Grey */
            color: #0f172a; /* Slate 900 */
            font-family: 'Inter', sans-serif;
        }

        /* METRIC CARDS - GLASSMORPHISM */
        div[data-testid="stMetric"] {
            background-color: #ffffff;
            border: 1px solid #e2e8f0;
            border-radius: 8px;
            padding: 15px;
            box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
            transition: transform 0.2s;
        }
        div[data-testid="stMetric"]:hover {
            transform: translateY(-2px);
            box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1);
        }
        
        /* FORCE DARK TEXT FOR METRICS (Fixes White-on-White) */
        div[data-testid="stMetricValue"] {
            color: #0f172a !important; 
            font-weight: 700 !important;
        }
        div[data-testid="stMetricLabel"] {
            color: #64748b !important; /* Slate 500 */
        }

        /* DATAFRAME STYLING (Fixes White-on-White) */
        div[data-testid="stDataFrame"] div[role="grid"] {
            color: #334155 !important; /* Slate 700 */
            background-color: white !important;
        }
        div[data-testid="stDataFrame"] div[role="columnheader"] {
            color: #0f172a !important;
            font-weight: 600 !important;
            background-color: #f1f5f9 !important;
        }

        /* SIDEBAR STYLING */
        [data-testid="stSidebar"] {
            background-color: #1e293b; /* Slate 800 */
        }
        [data-testid="stSidebar"] * {
            color: #f8fafc !important; /* Light text for sidebar */
        }
        [data-testid="stSidebar"] .stSelectbox label, 
        [data-testid="stSidebar"] .stMultiSelect label {
            color: #94a3b8 !important;
        }

        /* HEADERS */
        h1, h2, h3 {
            color: #0f172a !important;
            font-weight: 700 !important;
        }
        
        /* CUSTOM BADGES */
        .status-badge {
            display: inline-flex;
            align-items: center;
            padding: 4px 12px;
            border-radius: 9999px;
            font-size: 12px;
            font-weight: 600;
        }
        .bg-red { background-color: #fee2e2; color: #991b1b; }
        .bg-green { background-color: #dcfce7; color: #166534; }
        
        /* MAP CANVAS FIX */
        .js-plotly-plot .plotly .main-svg {
            background-color: rgba(0,0,0,0) !important;
        }
    </style>
""", unsafe_allow_html=True)

# 3. SMART DATA LOADING (MAPPING)
@st.cache_data
def load_data():
    # 1. Load or Generate Data
    try:
        df = pd.read_csv('analyzed_aadhaar_data.csv')
    except FileNotFoundError:
        # Dummy Data Generator if file missing
        dates = pd.date_range(start="2025-01-01", periods=200)
        df = pd.DataFrame({
            'date': dates,
            'state': np.random.choice(['Maharashtra', 'Uttar Pradesh', 'Bihar', 'Karnataka', 'Delhi', 'West Bengal', 'Tamil Nadu', 'Gujarat', 'Rajasthan', 'Kerala'], 200),
            'district': np.random.choice(['North', 'South', 'East', 'West', 'Central', 'Rural A', 'Urban B'], 200),
            'pincode': np.random.randint(110001, 800000, 200),
            'RISK_SCORE': np.random.uniform(15, 99, 200),
            'total_activity': np.random.randint(50, 800, 200),
            'enrol_adult': np.random.randint(10, 400, 200),
            'ratio_deviation': np.random.uniform(-0.15, 0.6, 200),
            'is_weekend': np.random.choice([0, 1], 200, p=[0.7, 0.3])
        })

    # Standardize Date
    if 'date' in df.columns:
        df['date'] = pd.to_datetime(df['date'])
    
    # SMART GEO-CLUSTERING LOGIC
    # Comprehensive Center Points for Indian States & UTs
    state_centers = {
        'Andaman and Nicobar Islands': (11.7401, 92.6586),
        'Andhra Pradesh': (15.9129, 79.7400),
        'Arunachal Pradesh': (28.2180, 94.7278),
        'Assam': (26.2006, 92.9376),
        'Bihar': (25.0961, 85.3131),
        'Chandigarh': (30.7333, 76.7794),
        'Chhattisgarh': (21.2787, 81.8661),
        'Dadra and Nagar Haveli and Daman and Diu': (20.4283, 72.8397),
        'Delhi': (28.7041, 77.1025),
        'Goa': (15.2993, 74.1240),
        'Gujarat': (22.2587, 71.1924),
        'Haryana': (29.0588, 76.0856),
        'Himachal Pradesh': (31.9579, 77.1095),
        'Jammu and Kashmir': (33.7782, 76.5762),
        'Jharkhand': (23.6102, 85.2799),
        'Karnataka': (15.3173, 75.7139),
        'Kerala': (10.8505, 76.2711),
        'Ladakh': (34.1526, 77.5770),
        'Lakshadweep': (10.5667, 72.6417),
        'Madhya Pradesh': (22.9734, 78.6569),
        'Maharashtra': (19.7515, 75.7139),
        'Manipur': (24.6637, 93.9063),
        'Meghalaya': (25.4670, 91.3662),
        'Mizoram': (23.1645, 92.9376),
        'Nagaland': (26.1584, 94.5624),
        'Odisha': (20.9517, 85.0985),
        'Puducherry': (11.9416, 79.8083),
        'Punjab': (31.1471, 75.3412),
        'Rajasthan': (27.0238, 74.2179),
        'Sikkim': (27.5330, 88.5122),
        'Tamil Nadu': (11.1271, 78.6569),
        'Telangana': (18.1124, 79.0193),
        'Tripura': (23.9408, 91.9882),
        'Uttar Pradesh': (26.8467, 80.9462),
        'Uttarakhand': (30.0668, 79.0193),
        'West Bengal': (22.9868, 87.8550)
    }

    def get_coords(row):
        state = row.get('state', 'Delhi')
        district = str(row.get('district', 'Unknown'))
        
        # 1. Get State Base Coordinates
        base_lat, base_lon = state_centers.get(state, (20.5937, 78.9629)) # Default to India Center
        
        # 2. DETERMINISTIC HASHING FOR DISTRICT
        # This ensures "District A" is ALWAYS in the same spot relative to the State Center
        # Creates distinct clusters instead of random noise
        district_hash = hash(state + district)
        np.random.seed(district_hash % 2**32) 
        
        # Offset the district center by up to 1.5 degrees (~150km) from state center
        dist_lat_offset = np.random.uniform(-1.5, 1.5)
        dist_lon_offset = np.random.uniform(-1.5, 1.5)
        
        # 3. INDIVIDUAL CENTER JITTER
        # Add tiny random noise (~4km) so points don't stack perfectly
        # We re-seed with None to get true randomness for the jitter
        np.random.seed(None) 
        noise_lat = np.random.normal(0, 0.04) 
        noise_lon = np.random.normal(0, 0.04)
        
        return pd.Series({
            'lat': base_lat + dist_lat_offset + noise_lat,
            'lon': base_lon + dist_lon_offset + noise_lon
        })
    
    # Apply coordinates
    coords = df.apply(get_coords, axis=1)
    df['lat'] = coords['lat']
    df['lon'] = coords['lon']
    
    # Risk Categories
    df['risk_category'] = pd.cut(
        df['RISK_SCORE'],
        bins=[-1, 50, 75, 85, 100],
        labels=['Low', 'Medium', 'High', 'Critical']
    )
    
    return df

# Load Data
df = load_data()

# 4. SIDEBAR & FILTERS
with st.sidebar:
    st.markdown("### S.T.A.R.K AI Control")
    st.markdown("---")
    
    # State Filter
    state_list = ['All'] + sorted(df['state'].unique().tolist())
    selected_state = st.selectbox("Select State", state_list)
    
    # District Filter
    if selected_state != 'All':
        filtered_df = df[df['state'] == selected_state]
        district_list = ['All'] + sorted(filtered_df['district'].unique().tolist())
    else:
        filtered_df = df.copy()
        district_list = ['All']
        
    selected_district = st.selectbox("Select District", district_list)
    
    if selected_district != 'All':
        filtered_df = filtered_df[filtered_df['district'] == selected_district]
        
    st.markdown("---")
    
    # Risk Filter
    risk_filter = st.multiselect(
        "Risk Level",
        options=['Low', 'Medium', 'High', 'Critical'],
        default=['High', 'Critical']
    )
    
    if risk_filter:
        filtered_df = filtered_df[filtered_df['risk_category'].isin(risk_filter)]
    
    st.markdown("---")
    
    # Links
    st.markdown("**Resources**")
    st.link_button("Open Notebook in Colab", "https://colab.research.google.com/drive/1YAQ4nfxltvG_cts3fmGc_zi2JQc4oPOT?usp=sharing")

    st.markdown("---")
    st.info(f"**User:** UIDAI_Officer\n\n**Team:** UIDAI_4571")

# 5. HEADER & KPI METRICS
col1, col2 = st.columns([3, 1])
with col1:
    st.title("Project S.T.A.R.K AI Dashboard")
    st.markdown("Context-Aware Fraud Detection System")

with col2:
    st.markdown("""
    <div style="text-align: right; padding-top: 20px;">
        <span class="status-badge bg-green">System Online</span>
        <div style="font-size: 12px; color: #64748b; margin-top: 5px;">Live Monitor</div>
    </div>
    """, unsafe_allow_html=True)

st.markdown("---")

# METRICS ROW
m1, m2, m3, m4 = st.columns(4)
total_centers = len(filtered_df)
high_risk = len(filtered_df[filtered_df['RISK_SCORE'] > 75])
avg_risk = filtered_df['RISK_SCORE'].mean() if not filtered_df.empty else 0
weekend_alerts = len(filtered_df[(filtered_df['is_weekend'] == 1) & (filtered_df['RISK_SCORE'] > 70)])

m1.metric("Total Centers", f"{total_centers:,}", border=True)
m2.metric("High Risk Alerts", f"{high_risk}", delta="Action Required", delta_color="inverse", border=True)
m3.metric("Avg. Risk Score", f"{avg_risk:.1f}/100", border=True)
m4.metric("Weekend Spikes", f"{weekend_alerts}", "Unauthorized", delta_color="off", border=True)

st.markdown("##") # Spacer

# 6. MAIN TABS
tab_map, tab_list, tab_charts = st.tabs(["Geographic Risk", "Priority List", "Pattern Analytics"])

# TAB 1: GEOGRAPHIC RISK (MAP)
with tab_map:
    col_map, col_details = st.columns([3, 1])
    
    with col_map:
        if not filtered_df.empty:
            # Using Open-Street-Map for better contrast and no-token requirement
            fig_map = px.scatter_mapbox(
                filtered_df,
                lat="lat",
                lon="lon",
                color="RISK_SCORE",
                size="total_activity",
                # Traffic Light Colors: Green -> Yellow -> Red
                color_continuous_scale=["#22c55e", "#eab308", "#ef4444"], 
                size_max=20,
                zoom=4.5 if selected_state != 'All' else 3.5,
                center={"lat": 22.0, "lon": 80.0}, # Center of India
                hover_name="pincode",
                hover_data={"district": True, "state": True, "RISK_SCORE": True, "lat": False, "lon": False},
                mapbox_style="open-street-map", 
                height=600,
                title="<b>Live Fraud Risk Heatmap</b>"
            )
            fig_map.update_layout(margin={"r":0,"t":40,"l":0,"b":0})
            st.plotly_chart(fig_map, use_container_width=True)
        else:
            st.warning("No data matches current filters.")

    with col_details:
        st.subheader("Top Hotspots")
        if not filtered_df.empty:
            top_districts = filtered_df.groupby('district')['RISK_SCORE'].mean().sort_values(ascending=False).head(5)
            for district, score in top_districts.items():
                # Color code the side bar
                color = "#ef4444" if score > 80 else "#f59e0b"
                st.markdown(f"""
                <div style="background: white; padding: 12px; border-radius: 8px; border-left: 5px solid {color}; margin-bottom: 10px; box-shadow: 0 2px 4px rgba(0,0,0,0.05);">
                    <div style="font-weight: 600; color: #1e293b;">{district}</div>
                    <div style="font-size: 13px; color: #64748b;">Avg Risk: <b>{score:.1f}</b></div>
                </div>
                """, unsafe_allow_html=True)

# TAB 2: PRIORITY LIST (DATAFRAME)
with tab_list:
    st.subheader("Target Investigation List")
    st.markdown("Filter: *Showing centers with Risk Score > 75*")
    
    target_list = filtered_df[filtered_df['RISK_SCORE'] > 75].sort_values('RISK_SCORE', ascending=False)
    
    st.dataframe(
        target_list[['date', 'state', 'district', 'pincode', 'enrol_adult', 'total_activity', 'RISK_SCORE']],
        column_config={
            "RISK_SCORE": st.column_config.ProgressColumn(
                "Risk Probability",
                help="Probability of fraud based on context analysis",
                format="%d%%",
                min_value=0,
                max_value=100,
            ),
            "date": st.column_config.DateColumn("Date", format="DD MMM YYYY"),
            "total_activity": st.column_config.NumberColumn("Volume"),
            "enrol_adult": st.column_config.NumberColumn("Adult Enrols"),
        },
        use_container_width=True,
        hide_index=True,
        height=400
    )
    
    # Export Button
    csv = target_list.to_csv(index=False).encode('utf-8')
    st.download_button(
        "Download CSV",
        data=csv,
        file_name="uidai_S.T.A.R.K AI_priority_list.csv",
        mime="text/csv",
        type="primary"
    )

# --- TAB 3: CHARTS ---
with tab_charts:
    c1, c2 = st.columns(2)
    
    with c1:
        st.subheader("Ghost ID Pattern (Ratio Deviation)")
        # Scatter Plot
        fig_scatter = px.scatter(
            filtered_df,
            x="total_activity",
            y="ratio_deviation",
            color="risk_category",
            color_discrete_map={'Critical': '#ef4444', 'High': '#f97316', 'Medium': '#eab308', 'Low': '#22c55e'},
            title="Deviation from District Baseline",
            labels={"ratio_deviation": "Deviation Score", "total_activity": "Daily Transactions"},
            hover_data=['pincode', 'district']
        )
        fig_scatter.add_hline(y=0.2, line_dash="dash", line_color="red", annotation_text="Fraud Threshold")
        st.plotly_chart(fig_scatter, use_container_width=True)

    with c2:
        st.subheader("Risk Distribution")
        # Histogram
        fig_hist = px.histogram(
            filtered_df, 
            x="RISK_SCORE", 
            nbins=20,
            color_discrete_sequence=['#3b82f6'],
            title="Frequency of Risk Scores"
        )
        fig_hist.update_layout(bargap=0.1)
        st.plotly_chart(fig_hist, use_container_width=True)

# 7. FOOTER
st.markdown("---")
st.markdown("""
<div style="text-align: center; font-size: 13px; color: #94a3b8;">
    <b>Project S.T.A.R.K AI</b> | UIDAI Hackathon 2026 | Team UIDAI_4571<br>
    <i>Confidential - For Official Use Only</i>
</div>
""", unsafe_allow_html=True)