#!/usr/bin/env python3
"""
Synthesis Engine for Deep Dive
Generates intelligence briefings from filtered content
"""

import json
from datetime import datetime
from typing import List, Any
from dataclasses import dataclass

# Load prompt template
with open("synthesis_prompt.txt") as f:
    SYSTEM_PROMPT = f.read()

@dataclass
class Briefing:
    date: str
    headlines: List[dict]
    deep_dives: List[dict]
    implications: str
    reading_list: List[dict]
    raw_text: str

def generate_briefing(
    papers: List[Any],
    blogs: List[Any],
    model_client=None,  # Hermes AIAgent or similar
    date: str = None
) -> Briefing:
    """Generate a briefing from ranked papers and blog posts."""
    
    date = date or datetime.now().strftime("%Y-%m-%d")
    
    # Build input for LLM
    input_data = {
        "date": date,
        "papers": [
            {
                "title": p.title,
                "authors": p.authors,
                "abstract": p.abstract[:500] + "..." if len(p.abstract) > 500 else p.abstract,
                "url": p.url,
                "arxiv_id": p.arxiv_id,
                "relevance_score": score
            }
            for p, score in papers[:10]  # Top 10 papers
        ],
        "blogs": [
            {
                "title": b.title,
                "source": b.source,
                "url": b.url,
                "summary": b.summary[:300] if b.summary else ""
            }
            for b in blogs[:5]  # Top 5 blog posts
        ]
    }
    
    # Call LLM for synthesis (placeholder - integrate with Hermes routing)
    if model_client:
        response = model_client.chat(
            system_message=SYSTEM_PROMPT,
            message=f"Generate briefing from this data:\n```json\n{json.dumps(input_data, indent=2)}\n```"
        )
        raw_text = response
    else:
        # Mock output for testing
        raw_text = f"# Deep Dive Briefing — {date}\n\n(Mock output - integrate LLM)"
    
    # Parse structured data from raw_text
    # (In production, use structured output or regex parsing)
    
    return Briefing(
        date=date,
        headlines=[],
        deep_dives=[],
        implications="",
        reading_list=[],
        raw_text=raw_text
    )

if __name__ == "__main__":
    print("Synthesis engine loaded")
    print(f"Prompt length: {len(SYSTEM_PROMPT)} chars")