# rstat_tool/database.py

import sqlite3
import time
from .ticker_extractor import COMMON_WORDS_BLACKLIST
from .logger_setup import get_logger
from datetime import datetime, timedelta

DB_FILE = "reddit_stocks.db"
log = get_logger()

def get_db_connection():
    """Establishes a connection to the SQLite database."""
    conn = sqlite3.connect(DB_FILE)
    conn.row_factory = sqlite3.Row
    return conn

def initialize_db():
    """
    Initializes the database and creates the necessary tables if they don't exist.
    """
    conn = get_db_connection()
    cursor = conn.cursor()

    # --- Create tickers table ---
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS tickers (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            symbol TEXT NOT NULL UNIQUE,
            market_cap INTEGER,
            closing_price REAL,
            last_updated INTEGER
        )
    """)

    # --- Create subreddits table ---
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS subreddits (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL UNIQUE
        )
    """)

    # --- Create mentions table ---
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS mentions (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            ticker_id INTEGER,
            subreddit_id INTEGER,
            post_id TEXT NOT NULL,
            mention_type TEXT NOT NULL,
            mention_sentiment REAL,         -- Renamed from sentiment_score for clarity
            post_avg_sentiment REAL,      -- NEW: Stores the avg sentiment of the whole post
            mention_timestamp INTEGER NOT NULL,
            FOREIGN KEY (ticker_id) REFERENCES tickers (id),
            FOREIGN KEY (subreddit_id) REFERENCES subreddits (id)
        )
    """)

    cursor.execute("""
        CREATE TABLE IF NOT EXISTS posts (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            post_id TEXT NOT NULL UNIQUE,
            title TEXT NOT NULL,
            post_url TEXT,
            subreddit_id INTEGER,
            post_timestamp INTEGER,
            comment_count INTEGER,
            avg_comment_sentiment REAL,
            FOREIGN KEY (subreddit_id) REFERENCES subreddits (id)
        )
    """)

    conn.commit()
    conn.close()
    log.info("Database initialized successfully.")

def clean_stale_tickers():
    """
    Removes tickers and their associated mentions from the database
    if the ticker symbol exists in the COMMON_WORDS_BLACKLIST.
    """
    log.info("\n--- Cleaning Stale Tickers from Database ---")
    conn = get_db_connection()
    cursor = conn.cursor()

    placeholders = ','.join('?' for _ in COMMON_WORDS_BLACKLIST)
    query = f"SELECT id, symbol FROM tickers WHERE symbol IN ({placeholders})"
    
    cursor.execute(query, tuple(COMMON_WORDS_BLACKLIST))
    stale_tickers = cursor.fetchall()

    if not stale_tickers:
        log.info("No stale tickers to clean.")
        conn.close()
        return

    for ticker in stale_tickers:
        ticker_id = ticker['id']
        ticker_symbol = ticker['symbol']
        log.info(f"Removing stale ticker '{ticker_symbol}' (ID: {ticker_id})...")
        cursor.execute("DELETE FROM mentions WHERE ticker_id = ?", (ticker_id,))
        cursor.execute("DELETE FROM tickers WHERE id = ?", (ticker_id,))
    
    deleted_count = conn.total_changes
    conn.commit()
    conn.close()
    log.info(f"Cleanup complete. Removed {deleted_count} records.")

def clean_stale_subreddits(active_subreddits):
    """
    Removes all data associated with subreddits that are NOT in the active list.
    """
    log.info("\n--- Cleaning Stale Subreddits from Database ---")
    conn = get_db_connection()
    cursor = conn.cursor()
    cursor.execute("SELECT id, name FROM subreddits")
    db_subreddits = cursor.fetchall()
    stale_sub_ids = []
    for sub in db_subreddits:
        if sub['name'] not in active_subreddits:
            log.info(f"Found stale subreddit to remove: r/{sub['name']}")
            stale_sub_ids.append(sub['id'])
    if not stale_sub_ids:
        log.info("No stale subreddits to clean.")
        conn.close()
        return
    for sub_id in stale_sub_ids:
        log.info(f"  -> Deleting associated data for subreddit ID: {sub_id}")
        cursor.execute("DELETE FROM mentions WHERE subreddit_id = ?", (sub_id,))
        cursor.execute("DELETE FROM posts WHERE subreddit_id = ?", (sub_id,))
        cursor.execute("DELETE FROM subreddits WHERE id = ?", (sub_id,))
    conn.commit()
    conn.close()
    log.info("Stale subreddit cleanup complete.")

def get_db_connection():
    conn = sqlite3.connect(DB_FILE)
    conn.row_factory = sqlite3.Row
    return conn

def initialize_db():
    conn = get_db_connection()
    cursor = conn.cursor()
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS tickers (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            symbol TEXT NOT NULL UNIQUE,
            market_cap INTEGER,
            closing_price REAL,
            last_updated INTEGER
        )
    """)
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS subreddits (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL UNIQUE
        )
    """)
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS mentions (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            ticker_id INTEGER,
            subreddit_id INTEGER,
            post_id TEXT NOT NULL,
            mention_type TEXT NOT NULL,
            mention_sentiment REAL,
            post_avg_sentiment REAL,
            mention_timestamp INTEGER NOT NULL,
            FOREIGN KEY (ticker_id) REFERENCES tickers (id),
            FOREIGN KEY (subreddit_id) REFERENCES subreddits (id)
        )
    """)
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS posts (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            post_id TEXT NOT NULL UNIQUE,
            title TEXT NOT NULL,
            post_url TEXT,
            subreddit_id INTEGER,
            post_timestamp INTEGER,
            comment_count INTEGER,
            avg_comment_sentiment REAL,
            FOREIGN KEY (subreddit_id) REFERENCES subreddits (id)
        )
    """)
    conn.commit()
    conn.close()
    log.info("Database initialized successfully.")

def add_mention(conn, ticker_id, subreddit_id, post_id, mention_type, timestamp, mention_sentiment, post_avg_sentiment=None):
    cursor = conn.cursor()
    try:
        cursor.execute(
            """
            INSERT INTO mentions (ticker_id, subreddit_id, post_id, mention_type, mention_timestamp, mention_sentiment, post_avg_sentiment)
            VALUES (?, ?, ?, ?, ?, ?, ?)
            """,
            (ticker_id, subreddit_id, post_id, mention_type, timestamp, mention_sentiment, post_avg_sentiment)
        )
        conn.commit()
    except sqlite3.IntegrityError:
        pass

def get_or_create_entity(conn, table_name, column_name, value):
    """Generic function to get or create an entity and return its ID."""
    cursor = conn.cursor()
    cursor.execute(f"SELECT id FROM {table_name} WHERE {column_name} = ?", (value,))
    result = cursor.fetchone()
    if result:
        return result['id']
    else:
        cursor.execute(f"INSERT INTO {table_name} ({column_name}) VALUES (?)", (value,))
        conn.commit()
        return cursor.lastrowid

def update_ticker_financials(conn, ticker_id, market_cap, closing_price):
    """Updates the financials and timestamp for a specific ticker."""
    cursor = conn.cursor()
    current_timestamp = int(time.time())
    cursor.execute(
        "UPDATE tickers SET market_cap = ?, closing_price = ?, last_updated = ? WHERE id = ?",
        (market_cap, closing_price, current_timestamp, ticker_id)
    )
    conn.commit()

def get_ticker_info(conn, ticker_id):
    """Retrieves all info for a specific ticker by its ID."""
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM tickers WHERE id = ?", (ticker_id,))
    return cursor.fetchone()

def generate_summary_report(limit=20):
    """Queries the DB to generate a summary for the command-line tool."""
    log.info(f"\n--- Top {limit} Tickers by Mention Count ---")
    conn = get_db_connection()
    cursor = conn.cursor()
    
    # --- UPDATED QUERY: Changed m.sentiment_score to m.mention_sentiment ---
    query = """
        SELECT
            t.symbol, t.market_cap, t.closing_price,
            COUNT(m.id) as mention_count,
            SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
            SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
            SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
        FROM mentions m JOIN tickers t ON m.ticker_id = t.id
        GROUP BY t.symbol, t.market_cap, t.closing_price
        ORDER BY mention_count DESC
        LIMIT ?;
    """
    results = cursor.execute(query, (limit,)).fetchall()
    
    header = f"{'Ticker':<8} | {'Mentions':<8} | {'Bullish':<8} | {'Bearish':<8} | {'Neutral':<8} | {'Market Cap':<15} | {'Close Price':<12}"
    print(header)
    print("-" * (len(header) + 2)) # Adjusted separator length

    for row in results:
        market_cap_str = "N/A"
        if row['market_cap'] and row['market_cap'] > 0:
            mc = row['market_cap']
            if mc >= 1e12: market_cap_str = f"${mc/1e12:.2f}T"
            elif mc >= 1e9: market_cap_str = f"${mc/1e9:.2f}B"
            else: market_cap_str = f"${mc/1e6:.2f}M"

        closing_price_str = f"${row['closing_price']:.2f}" if row['closing_price'] else "N/A"

        print(
            f"{row['symbol']:<8} | "
            f"{row['mention_count']:<8} | "
            f"{row['bullish_mentions']:<8} | "
            f"{row['bearish_mentions']:<8} | "
            f"{row['neutral_mentions']:<8} | "
            f"{market_cap_str:<15} | "
            f"{closing_price_str:<12}"
        )
    conn.close()

def get_all_scanned_subreddits():
    """Gets a unique list of all subreddits we have data for."""
    conn = get_db_connection()
    results = conn.execute("SELECT DISTINCT name FROM subreddits ORDER BY name ASC;").fetchall()
    conn.close()
    return [row['name'] for row in results]

def add_or_update_post_analysis(conn, post_data):
    """
    Inserts a new post analysis record or updates an existing one.
    This prevents duplicate entries for the same post.
    """
    cursor = conn.cursor()
    # Use the UNIQUE post_id to replace old data with new on conflict
    cursor.execute(
        """
        INSERT INTO posts (post_id, title, post_url, subreddit_id, post_timestamp, comment_count, avg_comment_sentiment)
        VALUES (:post_id, :title, :post_url, :subreddit_id, :post_timestamp, :comment_count, :avg_comment_sentiment)
        ON CONFLICT(post_id) DO UPDATE SET
            comment_count = excluded.comment_count,
            avg_comment_sentiment = excluded.avg_comment_sentiment;
        """,
        post_data
    )
    conn.commit()

def get_deep_dive_details(ticker_symbol):
    """
    Gets all analyzed posts that mention a specific ticker.
    """
    conn = get_db_connection()
    query = """
        SELECT DISTINCT p.*, s.name as subreddit_name FROM posts p
        JOIN mentions m ON p.post_id = m.post_id
        JOIN tickers t ON m.ticker_id = t.id
        JOIN subreddits s ON p.subreddit_id = s.id
        WHERE LOWER(t.symbol) = LOWER(?)
        ORDER BY p.post_timestamp DESC;
    """
    results = conn.execute(query, (ticker_symbol,)).fetchall()
    conn.close()
    return results

def get_overall_summary(limit=50):
    conn = get_db_connection()
    query = """
        SELECT
            t.symbol, t.market_cap, t.closing_price,
            COUNT(m.id) as mention_count,
            SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
            SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
            SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
        FROM mentions m JOIN tickers t ON m.ticker_id = t.id
        GROUP BY t.symbol, t.market_cap, t.closing_price
        ORDER BY mention_count DESC LIMIT ?;
    """
    results = conn.execute(query, (limit,)).fetchall()
    conn.close()
    return results

def get_subreddit_summary(subreddit_name, limit=50):
    conn = get_db_connection()
    query = """
        SELECT
            t.symbol, t.market_cap, t.closing_price,
            COUNT(m.id) as mention_count,
            SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
            SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
            SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
        FROM mentions m
        JOIN tickers t ON m.ticker_id = t.id
        JOIN subreddits s ON m.subreddit_id = s.id
        WHERE LOWER(s.name) = LOWER(?)
        GROUP BY t.symbol, t.market_cap, t.closing_price
        ORDER BY mention_count DESC LIMIT ?;
    """
    results = conn.execute(query, (subreddit_name, limit)).fetchall()
    conn.close()
    return results

def get_image_view_summary(subreddit_name):
    conn = get_db_connection()
    query = """
        SELECT
            t.symbol,
            COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
            COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
            COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
            COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
        FROM mentions m
        JOIN tickers t ON m.ticker_id = t.id
        JOIN subreddits s ON m.subreddit_id = s.id
        WHERE LOWER(s.name) = LOWER(?)
        GROUP BY t.symbol
        ORDER BY (post_mentions + comment_mentions) DESC
        LIMIT 10;
    """
    results = conn.execute(query, (subreddit_name,)).fetchall()
    conn.close()
    return results

def get_weekly_summary_for_subreddit(subreddit_name):
    conn = get_db_connection()
    seven_days_ago = datetime.utcnow() - timedelta(days=7)
    seven_days_ago_timestamp = int(seven_days_ago.timestamp())
    query = """
        SELECT
            t.symbol,
            COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
            COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
            COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
            COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
        FROM mentions m
        JOIN tickers t ON m.ticker_id = t.id
        JOIN subreddits s ON m.subreddit_id = s.id
        WHERE LOWER(s.name) = LOWER(?) AND m.mention_timestamp >= ?
        GROUP BY t.symbol
        ORDER BY (post_mentions + comment_mentions) DESC
        LIMIT 10;
    """
    results = conn.execute(query, (subreddit_name, seven_days_ago_timestamp)).fetchall()
    conn.close()
    return results

def get_overall_image_view_summary():
    """
    Gets a summary of top tickers across ALL subreddits for the image view.
    """
    conn = get_db_connection()
    query = """
        SELECT
            t.symbol,
            COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
            COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
            COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
            COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
        FROM mentions m
        JOIN tickers t ON m.ticker_id = t.id
        -- No JOIN or WHERE for subreddit, as we want all of them
        GROUP BY t.symbol
        ORDER BY (post_mentions + comment_mentions) DESC
        LIMIT 10;
    """
    results = conn.execute(query).fetchall()
    conn.close()
    return results