# rstat_tool/main.py

import argparse
import json
import os
import time

import praw
import yfinance as yf
from dotenv import load_dotenv

from . import database
from .ticker_extractor import extract_tickers
from .sentiment_analyzer import get_sentiment_score

load_dotenv()
MARKET_CAP_REFRESH_INTERVAL = 86400

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 in .env file.")
        return None
    
    # --- THIS IS THE CORRECTED LINE ---
    # The argument is 'client_secret', not 'secret_client'.
    return praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent)

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):
                
                # --- LOGIC PART 1: PROCESS INDIVIDUAL MENTIONS ---
                # 1a. Process the Post Title and Body for mentions
                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)
                    
                    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'])
                
                # 1b. Process Comments for mentions
                submission.comments.replace_more(limit=0)
                for comment in submission.comments.list()[:comment_limit]:
                    tickers_in_comment = extract_tickers(comment.body)
                    if not tickers_in_comment:
                        continue
                    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)
                        database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(comment.created_utc), comment_sentiment)
                
                # --- LOGIC PART 2: DEEP DIVE ANALYSIS ---
                all_comment_sentiments = []
                for comment in submission.comments.list()[:comment_limit]:
                    all_comment_sentiments.append(get_sentiment_score(comment.body))

                avg_sentiment = sum(all_comment_sentiments) / len(all_comment_sentiments) if all_comment_sentiments else 0

                post_analysis_data = {
                    "post_id": submission.id, "title": submission.title,
                    "post_url": f"https://reddit.com{submission.permalink}",
                    "subreddit_id": subreddit_id, "post_timestamp": int(submission.created_utc),
                    "comment_count": len(all_comment_sentiments), "avg_comment_sentiment": avg_sentiment
                }
                database.add_or_update_post_analysis(conn, post_analysis_data)
                
        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.", formatter_class=argparse.RawTextHelpFormatter)
    parser.add_argument("config_file", help="Path to the JSON file containing subreddits.")
    parser.add_argument("-p", "--posts", type=int, default=25, help="Number of posts to scan per subreddit.\n(Default: 25)")
    parser.add_argument("-c", "--comments", type=int, default=100, help="Number of comments to scan per post.\n(Default: 100)")
    parser.add_argument("-l", "--limit", type=int, default=20, help="Number of tickers to show in the final report.\n(Default: 20)")
    args = parser.parse_args()

    database.initialize_db()
    database.clean_stale_tickers()

    subreddits = load_subreddits(args.config_file)
    if not subreddits: return

    reddit = get_reddit_instance()
    if not reddit: return

    scan_subreddits(reddit, subreddits, post_limit=args.posts, comment_limit=args.comments)
    database.generate_summary_report(limit=args.limit)

if __name__ == "__main__":
    main()