"""
Multi-model setup — two agents backed by different LLMs.

Agent 1 (Doctor)    — a stronger model that suggests immunity-boosting foods.
Agent 2 (Organiser) — a lighter model that picks the single best option.

Demonstrates:
  - Per-agent LLM configuration (different endpoints, models, temperatures)
  - LLMCallerFactory dispatching callers by agent
  - Sequential workflow: Doctor → Organiser

Configure your models via environment variables:
    DOCTOR_API_KEY / DOCTOR_BASE_URL / DOCTOR_MODEL
    ORGANIZER_API_KEY / ORGANIZER_BASE_URL / ORGANIZER_MODEL

Run:
    python -m examples.multi_model_example
"""

import os

from builder import GraphBuilder
from execution import LLMCallerFactory, MACPRunner

# ── Model configurations ─────────────────────────────────────────────────────

DOCTOR_CONFIG = {
    "api_key": os.getenv("DOCTOR_API_KEY", "your-doctor-api-key"),
    "base_url": os.getenv("DOCTOR_BASE_URL", "http://localhost:8000/v1"),
    "model_name": os.getenv("DOCTOR_MODEL", "gpt-4o"),
}

ORGANIZER_CONFIG = {
    "api_key": os.getenv("ORGANIZER_API_KEY", "your-organizer-api-key"),
    "base_url": os.getenv("ORGANIZER_BASE_URL", "http://localhost:8001/v1"),
    "model_name": os.getenv("ORGANIZER_MODEL", "gpt-4o-mini"),
}


# ── Graph construction ────────────────────────────────────────────────────────


def _build_graph():
    builder = GraphBuilder()

    builder.add_agent(
        "doctor",
        display_name="Nutrition Doctor",
        persona="You are an experienced nutritional doctor.",
        description=("Suggest 3–5 foods that boost immunity, with a brief explanation of each benefit."),
        llm_backbone=DOCTOR_CONFIG["model_name"],
        base_url=DOCTOR_CONFIG["base_url"],
        api_key=DOCTOR_CONFIG["api_key"],
        temperature=0.7,
        max_tokens=1000,
    )

    builder.add_agent(
        "organizer",
        display_name="Organiser",
        persona="You are a practical organiser.",
        description=(
            "Choose THE SINGLE best option from those proposed. "
            "Answer briefly: 'Best choice: [food] — [1–2 sentence justification]'"
        ),
        llm_backbone=ORGANIZER_CONFIG["model_name"],
        base_url=ORGANIZER_CONFIG["base_url"],
        api_key=ORGANIZER_CONFIG["api_key"],
        temperature=0.1,
        max_tokens=200,
    )

    builder.add_workflow_edge("doctor", "organizer")
    builder.add_task(query="What food is best for boosting immunity?")
    builder.connect_task_to_agents()

    return builder.build()


# ── Entry point ───────────────────────────────────────────────────────────────


def main():
    graph = _build_graph()

    print("Agents and their models:")
    for agent in graph.agents:
        if hasattr(agent, "llm_config") and agent.llm_config:
            model = agent.llm_config.get("model_name", "?")
            print(f"  {agent.agent_id:<12} → {model}")

    factory = LLMCallerFactory.create_openai_factory()
    runner = MACPRunner(llm_factory=factory)

    print("\nRunning consultation…")
    result = runner.run_round(graph, final_agent_id="organizer")

    print("\n" + "=" * 50)
    print("RESULTS")
    print("=" * 50)

    if "doctor" in result.messages:
        print("\nDoctor's recommendations:")
        print(result.messages["doctor"])

    if "organizer" in result.messages:
        print("\nOrganiser's pick:")
        print(result.messages["organizer"])

    print(f"\nTotal tokens : {result.total_tokens}")
    print(f"Total time   : {result.total_time:.2f}s")
    print(f"Final answer : {result.final_answer}")


if __name__ == "__main__":
    main()