"""
GAIA benchmark agent using the OpenAI Agents SDK.
"""

from __future__ import annotations

import asyncio
import os
import time
import datetime
from typing import Any, Sequence, Callable, List, Optional

from dotenv import load_dotenv
from agents import Agent, Runner, FunctionTool, Tool

# Import all function tools
from tools import (
    python_run,
    load_spreadsheet,
    youtube_transcript,
    transcribe_audio,
    image_ocr,
    duckduckgo_search,
)

# ---------------------------------------------------------------------------
# Logging Utility
# ---------------------------------------------------------------------------
def log(msg):
    print(f"[{datetime.datetime.now():%Y-%m-%d %H:%M:%S}] {msg}")

# ---------------------------------------------------------------------------
# Load the added system prompt
# ---------------------------------------------------------------------------
ADDED_PROMPT_PATH = os.path.join(os.path.dirname(__file__), "added_prompt.txt")
with open(ADDED_PROMPT_PATH, "r", encoding="utf-8") as f:
    ADDED_PROMPT = f.read().strip()

load_dotenv()


def _select_model() -> str:
    """Return a model identifier appropriate for the Agents SDK based on environment settings."""
    provider = os.getenv("MODEL_PROVIDER", "hf").lower()

    if provider == "openai":
        model_name = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
        return f"openai/{model_name}"

    if provider == "hf":
        hf_model_id = os.getenv("HF_MODEL", "Qwen/Qwen2.5-Coder-32B-Instruct")
        return f"litellm/huggingface/{hf_model_id}"

    raise ValueError(
        f"Unsupported MODEL_PROVIDER: {provider!r}. Expected 'openai' or 'hf'."
    )


DEFAULT_TOOLS: List[FunctionTool] = [
    python_run,
    load_spreadsheet,
    youtube_transcript,
    transcribe_audio,
    image_ocr,
    duckduckgo_search,
]


def _build_agent(extra_tools: Sequence[FunctionTool] | None = None) -> Agent:
    """Construct the underlying Agents SDK `Agent` instance."""
    instructions = (
        "You are a helpful assistant tasked with answering questions using the available tools.\n\n"
        + ADDED_PROMPT
    )

    tools: Sequence[Tool] = list(DEFAULT_TOOLS)
    if extra_tools:
        tools = list(tools) + list(extra_tools)

    return Agent(
        name="GAIA Agent",
        instructions=instructions,
        tools=tools,
        model=_select_model(),
    )


class GAIAAgent:
    """Thin synchronous wrapper around an asynchronous Agents SDK agent."""

    def __init__(self, *, extra_tools: Sequence[FunctionTool] | None = None):
        self._agent = _build_agent(extra_tools=extra_tools)
        # Store the model id for logging
        self.model_id = _select_model()

    async def _arun(self, question: str, q_index: Optional[int] = None) -> str:
        q_num = q_index + 1 if q_index is not None else "?"
        log(f"Answering question {q_num}:")
        log(f"    Question: {question!r}")
        log(f"    Model: {self.model_id}")

        t0 = time.time()
        try:
            result = await Runner.run(self._agent, question)
            duration = time.time() - t0
            log(f"    Total duration: {duration:.2f} seconds.")
        except Exception as e:
            log(f"    Error during answer: {e}")
            raise
        return str(result.final_output).strip()

    def __call__(self, question: str, q_index: Optional[int] = None, **kwargs: Any) -> str:
        try:
            loop = asyncio.get_running_loop()
        except RuntimeError:
            return asyncio.run(self._arun(question, q_index=q_index))
        else:
            return loop.run_until_complete(self._arun(question, q_index=q_index))


def gaia_agent(*, extra_tools: Sequence[FunctionTool] | None = None) -> GAIAAgent:
    """Factory returning a ready‑to‑use GAIAAgent instance."""
    return GAIAAgent(extra_tools=extra_tools)


__all__ = ["GAIAAgent", "gaia_agent"]