import json
import ai_client
import mma_prompts
import aggregate
import summarize
from pathlib import Path

CONDUCTOR_PATH: Path = Path("conductor")

def get_track_history_summary() -> str:
 """
    Scans conductor/archive/ and conductor/tracks/ to build a summary of past work.
    """
 summary_parts = []
 archive_path = CONDUCTOR_PATH / "archive"
 tracks_path = CONDUCTOR_PATH / "tracks"
 paths_to_scan = []
 if archive_path.exists():
  paths_to_scan.extend(list(archive_path.iterdir()))
 if tracks_path.exists():
  paths_to_scan.extend(list(tracks_path.iterdir()))
 for track_dir in paths_to_scan:
  if not track_dir.is_dir():
   continue
  metadata_file = track_dir / "metadata.json"
  spec_file = track_dir / "spec.md"
  title = track_dir.name
  status = "unknown"
  overview = "No overview available."
  if metadata_file.exists():
   try:
    with open(metadata_file, "r", encoding="utf-8") as f:
     meta = json.load(f)
     title = meta.get("title", title)
     status = meta.get("status", status)
   except Exception:
    pass
  if spec_file.exists():
   try:
    with open(spec_file, "r", encoding="utf-8") as f:
     content = f.read()
     # Basic extraction of Overview section if it exists
     if "## Overview" in content:
      overview = content.split("## Overview")[1].split("##")[0].strip()
     else:
     # Just take a snippet of the beginning
      overview = content[:200] + "..."
   except Exception:
    pass
  summary_parts.append(f"Track: {title}\nStatus: {status}\nOverview: {overview}\n---")
 if not summary_parts:
  return "No previous tracks found."
 return "\n".join(summary_parts)

def generate_tracks(user_request: str, project_config: dict, file_items: list[dict], history_summary: str = None) -> list[dict]:
 """
    Tier 1 (Strategic PM) call. 
    Analyzes the project state and user request to generate a list of Tracks.
    """
 # 1. Build Repository Map (Summary View)
 repo_map = summarize.build_summary_markdown(file_items)
 # 2. Construct Prompt
 system_prompt = mma_prompts.PROMPTS.get("tier1_epic_init")
 user_message_parts = [
  f"### USER REQUEST:\n{user_request}\n",
  f"### REPOSITORY MAP:\n{repo_map}\n"
 ]
 if history_summary:
  user_message_parts.append(f"### TRACK HISTORY:\n{history_summary}\n")
 user_message_parts.append("Please generate the implementation tracks for this request.")
 user_message = "\n".join(user_message_parts)
 # Set custom system prompt for this call
 old_system_prompt = ai_client._custom_system_prompt
 ai_client.set_custom_system_prompt(system_prompt)
 try:
 # 3. Call Tier 1 Model (Strategic - Pro)
 # Note: We use gemini-1.5-pro or similar high-reasoning model for Tier 1
  response = ai_client.send(
   md_content="", # We pass everything in user_message for clarity
   user_message=user_message
  )
  # 4. Parse JSON Output
  try:
  # The prompt asks for a JSON array. We need to extract it if the AI added markdown blocks.
   json_match = response.strip()
   if "```json" in json_match:
    json_match = json_match.split("```json")[1].split("```")[0].strip()
   elif "```" in json_match:
    json_match = json_match.split("```")[1].split("```")[0].strip()
   tracks = json.loads(json_match)
   # Ensure each track has a 'title' for the GUI
   for t in tracks:
    if "title" not in t:
     t["title"] = t.get("goal", "Untitled Track")[:50]
   return tracks
  except Exception as e:
   print(f"Error parsing Tier 1 response: {e}")
   print(f"Raw response: {response}")
   return []
 finally:
 # Restore old system prompt
  ai_client.set_custom_system_prompt(old_system_prompt)

if __name__ == "__main__":
# Quick CLI test
 import project_manager
 proj = project_manager.load_project("manual_slop.toml")
 flat = project_manager.flat_config(proj)
 file_items = aggregate.build_file_items(Path("."), flat.get("files", {}).get("paths", []))
 print("Testing Tier 1 Track Generation...")
 history = get_track_history_summary()
 tracks = generate_tracks("Implement a basic unit test for the ai_client.py module.", flat, file_items, history_summary=history)
 print(json.dumps(tracks, indent=2))