# main.py

import argparse
import json
import os
import time

import praw
import yfinance as yf
from dotenv import load_dotenv

import database
from ticker_extractor import extract_tickers
from sentiment_analyzer import get_sentiment_score

load_dotenv()
MARKET_CAP_REFRESH_INTERVAL = 86400

# (load_subreddits, get_market_cap, get_reddit_instance functions are unchanged)
def load_subreddits(filepath):
    try:
        with open(filepath, 'r') as f:
            return json.load(f).get("subreddits", [])
    except (FileNotFoundError, json.JSONDecodeError) as e:
        print(f"Error loading config: {e}")
        return None

def get_market_cap(ticker_symbol):
    try:
        ticker = yf.Ticker(ticker_symbol)
        return ticker.fast_info.get('marketCap')
    except Exception:
        return None

def get_reddit_instance():
    client_id = os.getenv("REDDIT_CLIENT_ID")
    client_secret = os.getenv("REDDIT_CLIENT_SECRET")
    user_agent = os.getenv("REDDIT_USER_AGENT")
    if not all([client_id, client_secret, user_agent]):
        print("Error: Reddit API credentials not found.")
        return None
    return praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent)

# --- UPDATED: Function now accepts post_limit and comment_limit ---
def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
    """Scans subreddits, analyzes posts and comments, and stores results in the database."""
    conn = database.get_db_connection()
    
    print(f"\nScanning {len(subreddits_list)} subreddits (Top {post_limit} posts, {comment_limit} comments/post)...")
    for subreddit_name in subreddits_list:
        try:
            subreddit_id = database.get_or_create_entity(conn, 'subreddits', 'name', subreddit_name)
            subreddit = reddit.subreddit(subreddit_name)
            print(f"Scanning r/{subreddit_name}...")
            
            for submission in subreddit.hot(limit=post_limit):
                # --- 1. Process the Post Title and Body ---
                post_text = submission.title + " " + submission.selftext
                tickers_in_post = extract_tickers(post_text)
                post_sentiment = get_sentiment_score(submission.title)
                
                for ticker_symbol in set(tickers_in_post):
                    ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol)
                    database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(submission.created_utc), post_sentiment)
                    # (Market cap logic remains the same)
                    ticker_info = database.get_ticker_info(conn, ticker_id)
                    current_time = int(time.time())
                    if not ticker_info['last_updated'] or (current_time - ticker_info['last_updated'] > MARKET_CAP_REFRESH_INTERVAL):
                        print(f"  -> Fetching market cap for {ticker_symbol}...")
                        market_cap = get_market_cap(ticker_symbol)
                        database.update_ticker_market_cap(conn, ticker_id, market_cap or ticker_info['market_cap'])
                
                # --- 2. Process the Comments ---
                # Expand "MoreComments" objects. limit=None means we try to get all, but PRAW is protective.
                # A limit of 32 is the max PRAW will do in a single call. We'll iterate to be safe.
                submission.comments.replace_more(limit=10) 
                comment_count = 0
                for comment in submission.comments.list():
                    if comment_count >= comment_limit:
                        break # Stop processing comments for this post if we hit our limit

                    tickers_in_comment = extract_tickers(comment.body)
                    if not tickers_in_comment:
                        continue # Skip comments that don't mention any tickers

                    comment_sentiment = get_sentiment_score(comment.body)

                    for ticker_symbol in set(tickers_in_comment):
                        ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol)
                        # We use the submission.id as the post_id to group mentions correctly
                        database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(comment.created_utc), comment_sentiment)
                    
                    comment_count += 1

        except Exception as e:
            print(f"Could not scan r/{subreddit_name}. Error: {e}")
            
    conn.close()
    print("\n--- Scan Complete ---")

def main():
    parser = argparse.ArgumentParser(description="Analyze stock ticker mentions on Reddit.")
    parser.add_argument("config_file", help="Path to the JSON file containing subreddits.")
    args = parser.parse_args()

    database.initialize_db()
    subreddits = load_subreddits(args.config_file)
    if not subreddits: return
    reddit = get_reddit_instance()
    if not reddit: return

    # We now pass the limits to the scan function
    scan_subreddits(reddit, subreddits, post_limit=25, comment_limit=100)
    database.generate_summary_report()

if __name__ == "__main__":
    main()