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

def generate_tracks(user_request: str, project_config: dict, file_items: list[dict]) -> 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 = (
        f"### USER REQUEST:
{user_request}

"
        f"### REPOSITORY MAP:
{repo_map}

"
        "Please generate the implementation tracks for this request."
    )
    
    # 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,
        system_prompt=system_prompt,
        model_name="gemini-1.5-pro" # Strategic Tier
    )
    
    # 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)
        return tracks
    except Exception as e:
        print(f"Error parsing Tier 1 response: {e}")
        print(f"Raw response: {response}")
        return []

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...")
    tracks = generate_tracks("Implement a basic unit test for the ai_client.py module.", flat, file_items)
    print(json.dumps(tracks, indent=2))