import sqlite3
import json
import os
import hashlib
from datetime import datetime

DATASET_CONFIG = {
    "dataset_dir": "./dataset_ai_vs_real",
    "real_dir": "./dataset_ai_vs_real/real",
    "ai_dir": "./dataset_ai_vs_real/fake"
}

DB_NAME = "app_database.db"

def get_connection():
    conn = sqlite3.connect(DB_NAME)
    conn.row_factory = sqlite3.Row
    return conn

def init_db():
    conn = get_connection()
    cursor = conn.cursor()
    cursor.execute('''CREATE TABLE IF NOT EXISTS users (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        username TEXT UNIQUE NOT NULL,
                        password TEXT NOT NULL,
                        name TEXT NOT NULL,
                        join_date TEXT,
                        trust_score INTEGER DEFAULT 50)''')
    try:
        cursor.execute("ALTER TABLE users ADD COLUMN trust_score INTEGER DEFAULT 50")
    except sqlite3.OperationalError:
        pass
    cursor.execute('''CREATE TABLE IF NOT EXISTS scan_history (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        username TEXT NOT NULL,
                        filename TEXT,
                        file_type TEXT,
                        file_size TEXT,
                        source TEXT,
                        is_ai INTEGER,
                        accuracy REAL,
                        scan_date TEXT)''')
    cursor.execute('''CREATE TABLE IF NOT EXISTS test_batches (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        username TEXT NOT NULL,
                        total_images INTEGER,
                        correct_count INTEGER,
                        wrong_count INTEGER,
                        accuracy REAL,
                        test_date TEXT)''')
    cursor.execute('''CREATE TABLE IF NOT EXISTS test_results (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        batch_id INTEGER,
                        username TEXT NOT NULL,
                        filename TEXT,
                        folder_label TEXT,
                        prediction TEXT,
                        confidence REAL,
                        is_mismatch INTEGER,
                        user_correction TEXT,
                        corrected_label TEXT,
                        scan_date TEXT)''')
    cursor.execute('''CREATE TABLE IF NOT EXISTS learning_data (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        username TEXT NOT NULL,
                        filename TEXT,
                        original_prediction TEXT,
                        correct_label TEXT,
                        confidence REAL,
                        source TEXT,
                        scan_date TEXT)''')
    conn.commit()
    conn.close()
    for path in DATASET_CONFIG.values():
        if not os.path.exists(path):
            os.makedirs(path, exist_ok=True)

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

def register_user(username, password, name):
    conn = get_connection()
    try:
        conn.execute("INSERT INTO users (username, password, name, join_date) VALUES (?, ?, ?, ?)",
                     (username, hash_password(password), name, datetime.now().isoformat()))
        conn.commit()
        return True
    except sqlite3.IntegrityError:
        return False
    finally:
        conn.close()

def login_user(username, password):
    conn = get_connection()
    user = conn.execute("SELECT * FROM users WHERE username=? AND password=?", 
                        (username, hash_password(password))).fetchone()
    conn.close()
    return dict(user) if user else None

def add_scan_history(username, filename, file_type, file_size, source, is_ai, accuracy):
    conn = get_connection()
    conn.execute('''INSERT INTO scan_history (username, filename, file_type, file_size, source, is_ai, accuracy, scan_date)
                   VALUES (?, ?, ?, ?, ?, ?, ?, ?)''', 
                 (username, filename, file_type, file_size, source, is_ai, accuracy, datetime.now().isoformat()))
    conn.commit()
    conn.close()

def get_user_history(username):
    conn = get_connection()
    scan_rows = conn.execute("SELECT * FROM scan_history WHERE username=? ORDER BY id DESC LIMIT 20", (username,)).fetchall()
    batch_rows = conn.execute('''SELECT tb.id, tb.total_images, tb.correct_count, tb.wrong_count, tb.accuracy, tb.test_date
                                  FROM test_batches tb WHERE tb.username=? ORDER BY tb.id DESC LIMIT 20''', (username,)).fetchall()
    conn.close()

    history = []
    for row in scan_rows:
        r = dict(row)
        r["_type"] = "scan"
        history.append(r)
    for row in batch_rows:
        r = dict(row)
        r["_type"] = "batch"
        r["filename"] = f"Batch #{r['id']} ({r['total_images']} gambar)"
        r["file_type"] = "Batch"
        r["file_size"] = "-"
        r["source"] = f"{r['correct_count']} benar / {r['wrong_count']} salah"
        r["is_ai"] = 0
        r["accuracy"] = r["accuracy"]
        r["scan_date"] = r["test_date"]
        history.append(r)

    history.sort(key=lambda x: x.get("scan_date") or "", reverse=True)
    return history[:20]

def clear_all_history():
    conn = get_connection()
    conn.execute("DELETE FROM scan_history")
    conn.execute("DELETE FROM test_batches")
    conn.execute("DELETE FROM test_results")
    conn.execute("DELETE FROM learning_data")
    conn.commit()
    conn.close()

def create_test_batch(username, total_images, correct_count, wrong_count, accuracy):
    conn = get_connection()
    cur = conn.execute('''INSERT INTO test_batches (username, total_images, correct_count, wrong_count, accuracy, test_date)
                          VALUES (?, ?, ?, ?, ?, ?)''',
                       (username, total_images, correct_count, wrong_count, accuracy, datetime.now().isoformat()))
    conn.commit()
    batch_id = cur.lastrowid
    conn.close()
    return batch_id

def save_test_result(batch_id, username, filename, folder_label, prediction, confidence, is_mismatch):
    conn = get_connection()
    conn.execute('''INSERT INTO test_results (batch_id, username, filename, folder_label, prediction, confidence, is_mismatch, scan_date)
                    VALUES (?, ?, ?, ?, ?, ?, ?, ?)''',
                 (batch_id, username, filename, folder_label, prediction, confidence, is_mismatch, datetime.now().isoformat()))
    conn.commit()
    conn.close()

def update_test_result_correction(result_id, user_correction, corrected_label):
    conn = get_connection()
    conn.execute("UPDATE test_results SET user_correction=?, corrected_label=? WHERE id=?",
                 (user_correction, corrected_label, result_id))
    conn.commit()
    conn.close()

def save_learning_data(username, filename, original_prediction, correct_label, confidence, source="user_correction"):
    conn = get_connection()
    conn.execute('''INSERT INTO learning_data (username, filename, original_prediction, correct_label, confidence, source, scan_date)
                    VALUES (?, ?, ?, ?, ?, ?, ?)''',
                 (username, filename, original_prediction, correct_label, confidence, source, datetime.now().isoformat()))
    conn.commit()
    conn.close()

def get_all_test_batches(username=None):
    conn = get_connection()
    if username:
        rows = conn.execute("SELECT * FROM test_batches WHERE username=? ORDER BY id DESC", (username,)).fetchall()
    else:
        rows = conn.execute("SELECT * FROM test_batches ORDER BY id DESC").fetchall()
    conn.close()
    return [dict(row) for row in rows]

def get_test_results_by_batch(batch_id):
    conn = get_connection()
    rows = conn.execute("SELECT * FROM test_results WHERE batch_id=?", (batch_id,)).fetchall()
    conn.close()
    return [dict(row) for row in rows]

def get_overall_accuracy(username=None):
    conn = get_connection()

    total = 0
    correct = 0

    if username:
        rows = conn.execute("SELECT * FROM test_results WHERE username=?", (username,)).fetchall()
        ld_rows = conn.execute("SELECT * FROM learning_data WHERE username=? AND source='user_correction'", (username,)).fetchall()
    else:
        rows = conn.execute("SELECT * FROM test_results").fetchall()
        ld_rows = conn.execute("SELECT * FROM learning_data WHERE source='user_correction'").fetchall()

    conn.close()

    for r in rows:
        if not r["folder_label"]:
            continue
        total += 1
        final_label = r["corrected_label"] or r["prediction"]
        expected = "AI" if r["folder_label"].lower() in ("fake", "ai") else "REAL"
        if final_label == expected:
            correct += 1

    for r in ld_rows:
        total += 1
        if r["original_prediction"] == r["correct_label"]:
            correct += 1

    wrong = total - correct
    acc = round((correct / total * 100), 1) if total > 0 else 0
    return {"total": total, "correct": correct, "wrong": wrong, "accuracy": acc}

def get_learning_data_count():
    conn = get_connection()
    row = conn.execute("SELECT COUNT(*) as cnt FROM learning_data").fetchone()
    conn.close()
    return row["cnt"] or 0

def migrate_existing_learning_data():
    conn = get_connection()
    rows = conn.execute('''SELECT tr.* FROM test_results tr
                           LEFT JOIN learning_data ld ON tr.filename = ld.filename AND tr.username = ld.username
                           WHERE tr.is_mismatch = 1 AND tr.folder_label IS NOT NULL AND ld.id IS NULL''').fetchall()
    for r in rows:
        expected = "AI" if r["folder_label"].lower() in ("fake", "ai") else "REAL"
        conn.execute('''INSERT INTO learning_data (username, filename, original_prediction, correct_label, confidence, source, scan_date)
                        VALUES (?, ?, ?, ?, ?, ?, ?)''',
                     (r["username"], r["filename"], r["prediction"], expected, r["confidence"], "batch_mismatch", r["scan_date"]))
    conn.commit()
    conn.close()

def sync_scan_history_to_test_results():
    conn = get_connection()
    # Cari seluruh scan di scan_history yang belum ada di test_results (batch_id IS NULL)
    scans = conn.execute('''
        SELECT sh.username, sh.filename, sh.is_ai, sh.accuracy, sh.scan_date
        FROM scan_history sh
        LEFT JOIN test_results tr ON sh.username = tr.username AND sh.filename = tr.filename AND tr.batch_id IS NULL
        WHERE tr.id IS NULL
    ''').fetchall()
    
    for s in scans:
        prediction_label = "AI" if s["is_ai"] == 1 else "REAL"
        
        # Inferred label
        inferred_label = None
        fn_lower = s["filename"].lower()
        if "real" in fn_lower:
            inferred_label = "REAL"
        elif "fake" in fn_lower or "ai" in fn_lower:
            inferred_label = "AI"
        else:
            inferred_label = prediction_label # Default correct
            
        is_mismatch = 1 if prediction_label != inferred_label else 0
        
        # Cek jika ada user_correction di learning_data
        ld = conn.execute("SELECT correct_label FROM learning_data WHERE username=? AND filename=? AND source='user_correction' ORDER BY id DESC LIMIT 1",
                          (s["username"], s["filename"])).fetchone()
        corrected_label = None
        if ld:
            corrected_label = "AI" if ld["correct_label"].upper() in ("FAKE", "AI") else "REAL"
            is_mismatch = 1 if prediction_label != corrected_label else 0
            inferred_label = corrected_label
            
        conn.execute('''INSERT INTO test_results (batch_id, username, filename, folder_label, prediction, confidence, is_mismatch, corrected_label, scan_date)
                        VALUES (NULL, ?, ?, ?, ?, ?, ?, ?, ?)''',
                     (s["username"], s["filename"], inferred_label, prediction_label, s["accuracy"], is_mismatch, corrected_label, s["scan_date"]))
    conn.commit()
    conn.close()