#!/usr/bin/env python3
"""
Run Show — orchestrates two autonomous agents in a conversation.
Agents take turns reading the shared conversation log and posting responses.
No scripting — just two agents talking to each other.
"""

import json
import sys
import time
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent))
from agent import Agent

# ── Config ──────────────────────────────────────────────────────────
MAX_TURNS = 8  # safety ceiling, not a rigid target
PROJECT_DIR = Path(__file__).parent.parent
OUTPUT_DIR = PROJECT_DIR / "outputs"

def load_topic_brief(episode_id: str) -> str:
    """Load the raw topic material for this episode."""
    brief_path = PROJECT_DIR / "prompts" / f"producer_brief_{episode_id}.md"
    if brief_path.exists():
        return brief_path.read_text().strip()
    # Fallback: generic Hermes v0.18 brief
    return """
Hermes Agent v0.18 "Judgment Release"

What's New:
- Mixture of Agents — combine multiple AI models for stronger builds
- /goal command — step-by-step plans with beginning, middle, end
- /learn command — teach Hermes from a link, saved to Obsidian vault
- /journey command — timeline of everything learned, editable
- Background fan-out — parallel sub-agents without blocking chat
- Goal-mode with judge agent — verifies completion, not just claims
""".strip()

def run_show(episode_id: str, topic: str):
    """Run the autonomous agent conversation."""
    print(f"=== Agent AI Talk Show — Episode {episode_id} ===\n", flush=True)
    
    # Load topic brief
    topic_brief = load_topic_brief(episode_id)
    
    # Create conversation log
    conv_path = OUTPUT_DIR / f"ep{episode_id}_{topic}_conversation.json"
    conv_path.write_text(json.dumps({"messages": []}, indent=2))
    
    # Initialize agents
    leonard = Agent("Leonard", conv_path, topic_brief)
    charlie = Agent("Charlie", conv_path, topic_brief)
    
    # Run conversation
    turn = 0
    agents = [leonard, charlie]
    
    while turn < MAX_TURNS:
        # Alternate agents: Leonard opens, then Charlie, then back and forth
        if turn == 0:
            agent = leonard
        elif turn % 2 == 1:
            agent = charlie
        else:
            agent = leonard
        
        print(f"\n--- Turn {turn+1}/{MAX_TURNS}: {agent.name} ---", flush=True)
        result = agent.speak()
        
        turn += 1
        if turn < MAX_TURNS:
            time.sleep(1)  # Brief pause between turns
    
    # ── Save final transcript ─────────────────────────────────────
    transcript_path = OUTPUT_DIR / f"ep{episode_id}_{topic}_transcript.json"
    conversation = json.loads(conv_path.read_text())["messages"]
    
    transcript_data = {
        "episode": episode_id,
        "topic": topic,
        "turns": len(conversation),
        "conversation": conversation,
    }
    transcript_path.write_text(json.dumps(transcript_data, indent=2))
    print(f"\n✅ Transcript: {transcript_path}", flush=True)
    
    # Save readable transcript
    readable_path = OUTPUT_DIR / f"ep{episode_id}_{topic}_readable.txt"
    lines = [
        f"AGENT AI TALK SHOW — Episode {episode_id}\n",
        f"Topic: {topic}\n",
        "=" * 60 + "\n\n",
    ]
    for msg in conversation:
        lines.append(f"**{msg['agent']}**\n")
        lines.append(msg["content"] + "\n\n")
    readable_path.write_text("\n".join(lines))
    print(f"✅ Readable: {readable_path}", flush=True)
    
    # Summary
    total_words = sum(len(m["content"].split()) for m in conversation)
    print(f"\n📊 {total_words} words across {len(conversation)} turns", flush=True)
    print(f"   ~{total_words/150:.1f} min runtime", flush=True)
    
    return conversation

if __name__ == "__main__":
    episode = sys.argv[1] if len(sys.argv) > 1 else "003"
    topic = sys.argv[2] if len(sys.argv) > 2 else "hermes-v018"
    run_show(episode, topic)