# ui/server_api.py
from __future__ import annotations

import os
from collections.abc import Iterator
from typing import Any

import gradio as gr
from gradio import ChatMessage

from ui.agent.config import GPU_DURATION, local_inference_enabled
from ui.agent.graph import respond_with_graph
from ui.agent.respond import respond as respond_legacy
from ui.agent.system_prompt import BORDERLESS_SYSTEM_PROMPT
from ui.chat.defaults import DEFAULT_MAX_TOKENS, DEFAULT_TEMPERATURE, DEFAULT_TOP_P
from ui.globe_commands import empty_globe_state
from ui.intake.choices import (
    BUDGET_CHOICES,
    EDUCATION_CHOICES,
    EXPERIENCE_CHOICES,
    FAMILY_CHOICES,
    OCCUPATION_CHOICES,
    RESIDENCE_STATUS_CHOICES,
    TIMELINE_CHOICES,
    country_choices,
)
from ui.intake.examples import demo_personas, persona_prompt
from ui.intake.prompts import build_profile_prompt, build_session_title

DropdownValue = str | int | float | list[str | int | float] | None

AGENT_MODE = os.environ.get("BORDERLESS_AGENT_MODE", "graph").strip().lower()


def _respond_fn():
    if AGENT_MODE == "legacy":
        return respond_legacy
    return respond_with_graph


def _chat_message_to_dict(message: ChatMessage) -> dict[str, Any]:
    payload: dict[str, Any] = {
        "role": message.role,
        "content": message.content,
    }
    if message.metadata:
        payload["metadata"] = message.metadata
    return payload


def get_intake_choices() -> dict[str, Any]:
    return {
        "countries": country_choices(),
        "education": EDUCATION_CHOICES,
        "occupation": OCCUPATION_CHOICES,
        "experience": EXPERIENCE_CHOICES,
        "budget": BUDGET_CHOICES,
        "family": FAMILY_CHOICES,
        "timeline": TIMELINE_CHOICES,
        "residence_status": RESIDENCE_STATUS_CHOICES,
        "personas": demo_personas(),
    }


def build_research_prompt(
    current_country: DropdownValue,
    residence_status: DropdownValue,
    education: DropdownValue,
    occupation: DropdownValue,
    experience: DropdownValue,
    budget: DropdownValue,
    family: DropdownValue,
    timeline: DropdownValue,
    goals: str,
) -> dict[str, str]:
    prompt = build_profile_prompt(
        None,
        current_country,
        residence_status,
        education,
        occupation,
        experience,
        None,
        budget,
        family,
        timeline,
        goals,
    )
    return {
        "text": str(prompt.get("text") or ""),
        "title": build_session_title(
            current_country,
            residence_status,
            education,
            occupation,
            experience,
            budget,
            family,
            timeline,
            goals,
        ),
    }


def build_persona_prompt(persona_id: str) -> str:
    for persona in demo_personas():
        if persona["id"] == persona_id:
            prompt = persona_prompt(persona["profile"])
            return str(prompt.get("text") or "")
    return ""


def _merge_chat_history(
    history: list[dict[str, Any]],
    message: str,
    ui_messages: list[ChatMessage],
    assistant_text: str,
) -> list[dict[str, Any]]:
    updated_history = list(history)
    updated_history.append({"role": "user", "content": message})
    for ui_message in ui_messages:
        updated_history.append(_chat_message_to_dict(ui_message))
    if assistant_text and (
        not ui_messages
        or ui_messages[-1].role != "assistant"
        or ui_messages[-1].metadata
    ):
        updated_history.append({"role": "assistant", "content": assistant_text})
    return updated_history


def _stream_agent_response(
    message: str,
    history: list[dict[str, Any]],
    globe_state: dict[str, Any],
    hf_token: gr.OAuthToken | None,
) -> Iterator[tuple[Any, dict[str, Any]]]:
    yield from _respond_fn()(
        message,
        history,
        BORDERLESS_SYSTEM_PROMPT,
        DEFAULT_MAX_TOKENS,
        DEFAULT_TEMPERATURE,
        DEFAULT_TOP_P,
        globe_state,
        hf_token,
    )


def stream_chat(
    message: str,
    history: list[dict[str, Any]],
    globe_state: dict[str, Any] | None,
    hf_token: gr.OAuthToken | None,
):
    state = globe_state if globe_state else empty_globe_state()
    ui_messages: list[ChatMessage] = []
    assistant_text = ""

    if local_inference_enabled() and AGENT_MODE != "legacy":
        import spaces

        @spaces.GPU(duration=GPU_DURATION)
        def _run_agent_graph_gpu(
            message: str,
            history: list[dict[str, Any]],
            globe_state: dict[str, Any],
            hf_token: gr.OAuthToken | None,
        ) -> Iterator[tuple[Any, dict[str, Any]]]:
            yield from _stream_agent_response(message, history, globe_state, hf_token)

        agent_stream = _run_agent_graph_gpu(message, history, state, hf_token)
    else:
        agent_stream = _stream_agent_response(message, history, state, hf_token)

    for chunk in agent_stream:
        payload, state = chunk
        if isinstance(payload, list):
            ui_messages = payload
        elif isinstance(payload, str):
            assistant_text = payload

        yield {
            "history": _merge_chat_history(
                history, message, ui_messages, assistant_text
            ),
            "globe_state": state,
            "done": False,
        }

    yield {
        "history": _merge_chat_history(history, message, ui_messages, assistant_text),
        "globe_state": state,
        "done": True,
    }