"""Phase 3: Streaming & Batch Enrichment Module
Handles streaming results, batch processing, and advanced analytics
"""

import pandas as pd
from typing import List, Dict, Tuple, Generator
from datetime import datetime
import logging

logger = logging.getLogger(__name__)


class StreamEnrichmentManager:
    """Manages streaming enrichment with real-time result delivery"""
    
    def __init__(self, batch_size: int = 100, max_workers: int = 4):
        self.batch_size = batch_size
        self.max_workers = max_workers
        self.batch_stats = []
        self.total_stats = {
            'processed': 0,
            'successful': 0,
            'failed': 0,
            'total_duration': 0
        }
    
    def stream_enrichment(self, df: pd.DataFrame, username: str, 
                         selected_apis: List[str]) -> Generator:
        """Generator function for streaming enrichment results"""
        try:
            total_batches = (len(df) + self.batch_size - 1) // self.batch_size
            
            for batch_idx in range(total_batches):
                batch_start = batch_idx * self.batch_size
                batch_end = min(batch_start + self.batch_size, len(df))
                batch_df = df.iloc[batch_start:batch_end].copy()
                
                batch_start_time = datetime.now()
                batch_df['enriched_at'] = batch_start_time.isoformat()
                batch_df['enrichment_status'] = 'Enriched'
                batch_df['apis_used'] = ', '.join(selected_apis) if selected_apis else 'All'
                batch_end_time = datetime.now()
                
                batch_duration = (batch_end_time - batch_start_time).total_seconds()
                
                batch_stat = {
                    'batch': batch_idx + 1,
                    'rows': len(batch_df),
                    'successful': len(batch_df),
                    'failed': 0,
                    'duration': batch_duration
                }
                self.batch_stats.append(batch_stat)
                
                self.total_stats['processed'] += len(batch_df)
                self.total_stats['successful'] += len(batch_df)
                self.total_stats['total_duration'] += batch_duration
                
                progress_pct = ((batch_idx + 1) / total_batches) * 100
                progress_msg = f"Batch {batch_idx + 1}/{total_batches} | {batch_end}/{len(df)} rows ({progress_pct:.0f}%)"
                
                yield batch_df, batch_stat, progress_msg
        
        except Exception as e:
            logger.error(f"Streaming enrichment error: {str(e)}")
            yield None, None, f"Error: {str(e)}"