"""GAIA Unit 4 agent: tool-calling loop via Hugging Face Inference API."""

from __future__ import annotations

import os
from typing import Any, Optional

from huggingface_hub import InferenceClient

from answer_normalize import normalize_answer
from inference_client_factory import inference_client_kwargs
from tools.registry import TOOL_DEFINITIONS, deterministic_attempt, dispatch_tool

SYSTEM_PROMPT = """You solve GAIA benchmark questions for the Hugging Face Agents Course.

Hard rules:
- Call tools as needed (search, Wikipedia, fetch URL, Python, audio, image, Excel).
- Your final assistant message must contain ONLY the answer text required by the question — no labels like "FINAL ANSWER", no markdown fences, no extra sentences.
- Match the question's format exactly (comma-separated, alphabetical order, IOC codes, algebraic notation, two-decimal USD, first name only, etc.).
- When a local attachment path is given, use the appropriate tool with that exact path.
- For English Wikipedia tasks, use wikipedia_* tools; cross-check with web_search if needed.
- For YouTube URLs in the question, try youtube_transcript first.
"""


class GaiaAgent:
    def __init__(
        self,
        *,
        hf_token: Optional[str] = None,
        text_model: Optional[str] = None,
        max_iterations: int = 14,
    ):
        self.hf_token = (
            hf_token
            or os.environ.get("HF_TOKEN")
            or os.environ.get("HUGGINGFACEHUB_API_TOKEN")
        )
        self.text_model = text_model or os.environ.get(
            "GAIA_TEXT_MODEL", "Qwen/Qwen2.5-7B-Instruct"
        )
        self.max_iterations = max_iterations
        self._client: Optional[InferenceClient] = None

    def _get_client(self) -> InferenceClient:
        if self._client is None:
            if not self.hf_token:
                raise RuntimeError(
                    "HF_TOKEN or HUGGINGFACEHUB_API_TOKEN is required for GaiaAgent."
                )
            kw = inference_client_kwargs(self.hf_token)
            self._client = InferenceClient(**kw)
        return self._client

    def __call__(
        self,
        question: str,
        attachment_path: Optional[str] = None,
        task_id: Optional[str] = None,
    ) -> str:
        det = deterministic_attempt(question, attachment_path)
        if det is not None:
            return normalize_answer(det)

        if not self.hf_token:
            return normalize_answer(
                "Error: missing HF_TOKEN; cannot run LLM tools for this question."
            )

        user_text = _build_user_payload(question, attachment_path, task_id)
        messages: list[dict[str, Any]] = [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": user_text},
        ]

        client = self._get_client()
        last_text = ""

        for _ in range(self.max_iterations):
            try:
                completion = client.chat_completion(
                    messages=messages,
                    model=self.text_model,
                    tools=TOOL_DEFINITIONS,
                    tool_choice="auto",
                    max_tokens=1024,
                    temperature=0.15,
                )
            except Exception as e:
                last_text = f"Inference error: {e}"
                break

            choice = completion.choices[0]
            msg = choice.message
            last_text = (msg.content or "").strip()

            if msg.tool_calls:
                messages.append(
                    {
                        "role": "assistant",
                        "content": msg.content if msg.content else None,
                        "tool_calls": [
                            {
                                "id": tc.id,
                                "type": "function",
                                "function": {
                                    "name": tc.function.name,
                                    "arguments": tc.function.arguments,
                                },
                            }
                            for tc in msg.tool_calls
                        ],
                    }
                )
                for tc in msg.tool_calls:
                    name = tc.function.name
                    args = tc.function.arguments or "{}"
                    result = dispatch_tool(name, args, hf_token=self.hf_token)
                    messages.append(
                        {
                            "role": "tool",
                            "tool_call_id": tc.id,
                            "content": result[:24_000],
                        }
                    )
                continue

            if last_text:
                break

            if choice.finish_reason == "length":
                last_text = "Error: model hit max length without an answer."
                break

        return normalize_answer(last_text or "Error: empty response.")


def _build_user_payload(
    question: str,
    attachment_path: Optional[str],
    task_id: Optional[str],
) -> str:
    parts = []
    if task_id:
        parts.append(f"task_id: {task_id}")
    parts.append(f"Question:\n{question.strip()}")
    if attachment_path:
        parts.append(f"\nAttachment path (use with tools): {attachment_path}")
    else:
        parts.append("\nNo attachment.")
    return "\n".join(parts)