from datasets import load_dataset
import pandas as pd
import praw
import time
from tqdm import tqdm

def initialize_reddit():
    return praw.Reddit(
        client_id="RPAW_CLIENT_ID",
        client_secret="RPAW_CLIENT_SECRET",
        user_agent="PRAW_AGENT"
    )

def get_author_info(reddit, submission_id):
    try:
        submission = reddit.submission(id=submission_id)
        author = submission.author
        if author is None:
            return {
                'author_name': '[deleted]',
                'karma': None,
                'account_age_days': None,
                'is_mod': None
            }
        
        return {
            'author_name': author.name,
            'karma': author.link_karma + author.comment_karma,
            'account_age_days': (time.time() - author.created_utc) / 86400,
            'is_mod': author.is_mod if hasattr(author, 'is_mod') else None
        }
    except Exception as e:
        print(f"Error fetching author info for submission {submission_id}: {e}")
        return {
            'author_name': None,
            'karma': None,
            'account_age_days': None,
            'is_mod': None
        }

def praw_auhtors_to_path(ds_repo_id, file_path):
    # Initialize Reddit API
    reddit = initialize_reddit()
    
    # Load dataset from Hugging Face
    dataset = load_dataset(ds_repo_id, 
                         data_files={'train': file_path},
                         split='train')
    df = pd.DataFrame(dataset)
    
    # Fetch author info for each submission
    author_data = []
    for submission_id in tqdm(df['id']):
        author_info = get_author_info(reddit, submission_id)
        author_data.append(author_info)
        time.sleep(1)  # Rate limiting
    
    # Create DataFrame with author info
    author_df = pd.DataFrame(author_data)
    
    # Merge with original data
    result_df = pd.concat([df, author_df], axis=1)
    
    # Save result
    output_file = f"submissions_with_authors_{file_path}.csv"
    result_df.to_csv(output_file, index=False)
    print(f"Saved to {output_file}")