import streamlit as st
import pickle
import numpy as np
import sqlite3
import hashlib

# ======================
# Dark FinTech Theme
# ======================
st.set_page_config(page_title="SecurePay AI", page_icon="🔐", layout="wide")

st.markdown("""
<style>
body { background-color: #0E1117; color: white; }
div.stButton > button {
    background-color: #1F77B4;
    color: white;
    border-radius: 8px;
}
</style>
""", unsafe_allow_html=True)

# ======================
# Database Setup
# ======================
def init_db():
    conn = sqlite3.connect("database.db")
    c = conn.cursor()
    c.execute("CREATE TABLE IF NOT EXISTS users (username TEXT, password TEXT)")
    c.execute("""CREATE TABLE IF NOT EXISTS transactions
                 (amount REAL, hour INT, risk REAL)""")
    conn.commit()
    conn.close()

init_db()

def hash_password(password):
    return hashlib.sha256(password.encode()).hexdigest()

def register_user(username, password):
    conn = sqlite3.connect("database.db")
    c = conn.cursor()
    c.execute("INSERT INTO users VALUES (?,?)",
              (username, hash_password(password)))
    conn.commit()
    conn.close()

def login_user(username, password):
    conn = sqlite3.connect("database.db")
    c = conn.cursor()
    c.execute("SELECT * FROM users WHERE username=? AND password=?",
              (username, hash_password(password)))
    result = c.fetchone()
    conn.close()
    return result

def save_transaction(amount, hour, risk):
    conn = sqlite3.connect("database.db")
    c = conn.cursor()
    c.execute("INSERT INTO transactions VALUES (?,?,?)",
              (amount, hour, risk))
    conn.commit()
    conn.close()

# ======================
# Load Model
# ======================
model = pickle.load(open("fraud_model.pkl", "rb"))

# ======================
# Session State
# ======================
if "logged_in" not in st.session_state:
    st.session_state.logged_in = False

# ======================
# Login / Register
# ======================
if not st.session_state.logged_in:

    st.title("🔐 SecurePay AI Login")

    menu = st.radio("Select Option", ["Login", "Register"])

    username = st.text_input("Username")
    password = st.text_input("Password", type="password")

    if menu == "Register":
        if st.button("Create Account"):
            register_user(username, password)
            st.success("Account Created Successfully!")

    if menu == "Login":
        if st.button("Login"):
            if login_user(username, password):
                st.session_state.logged_in = True
                st.success("Login Successful")
            else:
                st.error("Invalid Credentials")

# ======================
# Main Dashboard
# ======================
else:

    st.title("💳 SecurePay AI Dashboard")
    st.subheader("Fraud Detection for Small Businesses")

    col1, col2 = st.columns(2)

    with col1:
        amount = st.number_input("Transaction Amount ($)", min_value=0.0)
        hour = st.slider("Transaction Hour", 0, 23, 12)

    with col2:
        new_customer = st.selectbox("New Customer?", ["No", "Yes"])
        refund_request = st.selectbox("Refund Requested?", ["No", "Yes"])
        location_mismatch = st.selectbox("Location Mismatch?", ["No", "Yes"])

    new_customer = 1 if new_customer == "Yes" else 0
    refund_request = 1 if refund_request == "Yes" else 0
    location_mismatch = 1 if location_mismatch == "Yes" else 0

    if st.button("Analyze Transaction"):

        features = np.array([[amount, hour, new_customer,
                              refund_request, location_mismatch]])

        prediction = model.predict(features)[0]
        probability = model.predict_proba(features)[0][1]

        save_transaction(amount, hour, probability)

        if prediction == 1:
            st.error("⚠️ High Fraud Risk Detected")
        else:
            st.success("✅ Transaction Appears Safe")

        st.progress(int(probability * 100))
        st.write(f"Risk Score: {round(probability * 100, 2)}%")

    st.markdown("---")
    st.subheader("📊 Live Analytics")

    conn = sqlite3.connect("database.db")
    data = conn.execute("SELECT risk FROM transactions").fetchall()
    conn.close()

    risk_values = [r[0] for r in data]

    if risk_values:
        st.line_chart(risk_values)