"""Reusable tooling for browser-agent action selection.

This module gives the browser agents a shared, validated action surface.
It supports both:
- native tool calling when the LLM/provider can emit tool calls
- JSON fallback when the model only returns plain text
"""

from __future__ import annotations

import json
from typing import Any

from app.agents.tooling import ToolCall, ToolRegistry, tool


def _normalize_points(values: list[str] | str | None) -> list[str]:
    """Trim and deduplicate short research memory lists.

    Models occasionally return a plain string instead of a list. Treat that as a
    single item instead of iterating character-by-character.
    """
    cleaned: list[str] = []
    if values is None:
        iterable: list[Any] = []
    elif isinstance(values, str):
        iterable = [values]
    else:
        iterable = list(values)

    for value in iterable:
        text = str(value).strip()
        if len(text) <= 1 and text.isalpha():
            continue
        if text and text not in cleaned:
            cleaned.append(text)
    return cleaned


@tool(description="Search the web with a fresh query when the current page is insufficient.")
def search_web(
    query: str,
    reason: str = "",
    known_facts: list[str] | None = None,
    missing_points: list[str] | None = None,
) -> dict[str, Any]:
    """Search the web.

    Args:
        query: Query terms to search for next.
        reason: Why a new search is needed.
        known_facts: Short facts already established.
        missing_points: What information is still missing.
    """
    return {
        "action": "SEARCH",
        "value": query.strip(),
        "reason": reason.strip(),
        "known_facts": _normalize_points(known_facts),
        "missing_points": _normalize_points(missing_points),
    }


@tool(description="Open a new URL that was discovered in the current page or results.")
def navigate_to_url(
    url: str,
    reason: str = "",
    known_facts: list[str] | None = None,
    missing_points: list[str] | None = None,
) -> dict[str, Any]:
    """Navigate to a specific URL.

    Args:
        url: Absolute URL to visit next.
        reason: Why that URL is the best next step.
        known_facts: Short facts already established.
        missing_points: What information is still missing.
    """
    return {
        "action": "NAVIGATE",
        "value": url.strip(),
        "reason": reason.strip(),
        "known_facts": _normalize_points(known_facts),
        "missing_points": _normalize_points(missing_points),
    }


@tool(description="Scroll the current page to reveal more content.")
def scroll_page(
    reason: str = "",
    known_facts: list[str] | None = None,
    missing_points: list[str] | None = None,
) -> dict[str, Any]:
    """Scroll the current page.

    Args:
        reason: Why scrolling is useful right now.
        known_facts: Short facts already established.
        missing_points: What information is still missing.
    """
    return {
        "action": "SCROLL",
        "value": "",
        "reason": reason.strip(),
        "known_facts": _normalize_points(known_facts),
        "missing_points": _normalize_points(missing_points),
    }


@tool(description="Finish the task and provide the final answer based on the collected evidence.")
def finish_task(
    answer: str,
    reason: str = "",
    known_facts: list[str] | None = None,
    missing_points: list[str] | None = None,
) -> dict[str, Any]:
    """Finish the task.

    Args:
        answer: Final user-facing answer.
        reason: Why the task is complete.
        known_facts: Short facts already established.
        missing_points: Remaining uncertainty, if any.
    """
    return {
        "action": "DONE",
        "value": "",
        "answer": answer.strip(),
        "reason": reason.strip(),
        "known_facts": _normalize_points(known_facts),
        "missing_points": _normalize_points(missing_points),
    }


BROWSER_TOOL_REGISTRY = ToolRegistry([
    search_web,
    navigate_to_url,
    scroll_page,
    finish_task,
])


def get_browser_tools(allow_scroll: bool = True) -> list[dict[str, Any]]:
    """Return OpenAI-compatible tool schemas for the browser agent."""
    tools = []
    for schema in BROWSER_TOOL_REGISTRY.schemas:
        if not allow_scroll and schema.name == "scroll_page":
            continue
        tools.append(schema.to_openai_tool())
    return tools


def execute_browser_tool_call(tool_call: ToolCall, allow_scroll: bool = True) -> dict[str, Any]:
    """Execute a browser decision tool call and validate mode-specific constraints."""
    if not allow_scroll and tool_call.name == "scroll_page":
        raise ValueError("scroll_page is not allowed for this browser mode")

    result = BROWSER_TOOL_REGISTRY.execute(tool_call)
    return validate_browser_decision(result, allow_scroll=allow_scroll)


def parse_browser_json_response(text: str, allow_scroll: bool = True) -> dict[str, Any]:
    """Parse legacy JSON action output into the normalized browser-decision shape."""
    snippet = _extract_json_object(text)
    data = json.loads(snippet)

    action = str(data.get("action", "DONE")).strip().upper()
    normalized = {
        "action": action,
        "value": str(data.get("value", "")).strip(),
        "answer": str(data.get("answer", "")).strip(),
        "reason": str(data.get("reason", "")).strip(),
        "known_facts": _normalize_points(data.get("known_facts")),
        "missing_points": _normalize_points(data.get("missing_points")),
    }

    if action == "SEARCH":
        normalized["value"] = str(data.get("query", normalized["value"])).strip()
    elif action == "NAVIGATE":
        normalized["value"] = str(data.get("url", normalized["value"])).strip()
    elif action == "DONE":
        normalized["answer"] = str(data.get("answer", data.get("result", normalized["answer"]))).strip()
    elif action == "SCROLL":
        normalized["value"] = ""

    return validate_browser_decision(normalized, allow_scroll=allow_scroll)


def validate_browser_decision(decision: dict[str, Any], allow_scroll: bool = True) -> dict[str, Any]:
    """Validate and normalize a browser agent decision."""
    action = str(decision.get("action", "")).strip().upper()
    value = str(decision.get("value", "")).strip()
    answer = str(decision.get("answer", "")).strip()

    normalized = {
        "action": action or "DONE",
        "value": value,
        "answer": answer,
        "reason": str(decision.get("reason", "")).strip(),
        "known_facts": _normalize_points(decision.get("known_facts")),
        "missing_points": _normalize_points(decision.get("missing_points")),
    }

    if normalized["action"] == "SEARCH":
        if not normalized["value"]:
            raise ValueError("SEARCH decision requires a non-empty query")
    elif normalized["action"] == "NAVIGATE":
        if not normalized["value"].startswith("http"):
            raise ValueError("NAVIGATE decision requires an absolute URL")
    elif normalized["action"] == "SCROLL":
        if not allow_scroll:
            raise ValueError("SCROLL is not supported in this browser mode")
        normalized["value"] = ""
    elif normalized["action"] == "DONE":
        pass
    else:
        raise ValueError(f"Unsupported browser action '{normalized['action']}'")

    return normalized


def _extract_json_object(text: str) -> str:
    """Extract the first JSON object-looking slice from a model response."""
    raw = (text or "").strip()
    if raw.startswith("```"):
        parts = raw.split("```")
        if len(parts) >= 2:
            raw = parts[1]
            if raw.startswith("json"):
                raw = raw[4:]
            raw = raw.strip()

    start = raw.find("{")
    end = raw.rfind("}")
    if start == -1 or end == -1 or end <= start:
        raise ValueError("Model response did not contain a JSON object")
    return raw[start:end + 1]