#!/usr/bin/env python3
"""
Deep Dive Orchestrator
Main entry point for daily briefing generation
"""

import os
import sys
import asyncio
import argparse
from datetime import datetime
from pathlib import Path

# Add subdirectories to path
sys.path.insert(0, "./aggregator")
sys.path.insert(0, "./relevance")
sys.path.insert(0, "./synthesis")
sys.path.insert(0, "./tts")
sys.path.insert(0, "./delivery")

from arxiv_fetcher import fetch_all_sources, keyword_score
from blog_fetcher import fetch_all_blogs
from relevance_engine import RelevanceEngine
from synthesis_engine import generate_briefing
from tts_pipeline import generate_briefing_audio
from delivery_pipeline import deliver_briefing

def run_deep_dive(dry_run: bool = False, skip_tts: bool = False):
    """Run the full Deep Dive pipeline."""
    
    print(f"\n{'='*60}")
    print(f"Deep Dive Briefing — {datetime.now().strftime('%Y-%m-%d %H:%M')}")
    print(f"{'='*60}\n")
    
    # Phase 1: Aggregate
    print("📚 Phase 1: Aggregating sources...")
    papers = fetch_all_sources(days_back=1)
    blogs = fetch_all_blogs()
    print(f"   Fetched {len(papers)} papers, {len(blogs)} blog posts")
    
    # Phase 2: Relevance
    print("\n🎯 Phase 2: Ranking relevance...")
    engine = RelevanceEngine()
    
    # Rank papers
    ranked_papers = engine.rank_items(
        papers,
        text_fn=lambda p: f"{p.title} {p.abstract}",
        top_k=10
    )
    
    # Filter blogs by keywords for now
    blog_keywords = ["agent", "llm", "model", "research", "ai"]
    filtered_blogs = engine.filter_by_keywords(
        blogs,
        text_fn=lambda b: f"{b.title} {b.summary}",
        keywords=blog_keywords
    )[:5]
    
    print(f"   Top paper: {ranked_papers[0][0].title if ranked_papers else 'None'}")
    
    # Phase 3: Synthesis
    print("\n🧠 Phase 3: Synthesizing briefing...")
    briefing = generate_briefing(ranked_papers, filtered_blogs)
    
    # Save text version
    output_dir = Path("./output")
    output_dir.mkdir(exist_ok=True)
    
    text_path = output_dir / f"briefing_{datetime.now().strftime('%Y%m%d')}.md"
    with open(text_path, "w") as f:
        f.write(briefing.raw_text)
    print(f"   Saved: {text_path}")
    
    # Phase 4: TTS (optional)
    audio_path = None
    if not skip_tts:
        print("\n🔊 Phase 4: Generating audio...")
        try:
            audio_path = generate_briefing_audio(briefing.raw_text, str(output_dir))
            print(f"   Generated: {audio_path}")
        except Exception as e:
            print(f"   TTS skipped: {e}")
    
    # Phase 5: Delivery
    print("\n📤 Phase 5: Delivering...")
    success = deliver_briefing(
        audio_path=audio_path,
        text_summary=briefing.raw_text[:1000] + "...",
        dry_run=dry_run
    )
    
    print(f"\n{'='*60}")
    print(f"Complete! Status: {'✅ Success' if success else '❌ Failed'}")
    print(f"{'='*60}")
    
    return success

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Deep Dive Daily Briefing")
    parser.add_argument("--dry-run", action="store_true", help="Don't actually send")
    parser.add_argument("--skip-tts", action="store_true", help="Skip audio generation")
    parser.add_argument("--cron", action="store_true", help="Run in cron mode (minimal output)")
    
    args = parser.parse_args()
    
    success = run_deep_dive(dry_run=args.dry_run, skip_tts=args.skip_tts)
    sys.exit(0 if success else 1)