from pathlib import Path
from typing import Any, Dict, List, Optional

from langchain_openai import ChatOpenAI
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent


def _make_file_tools(workspace: Path):
    workspace = workspace.resolve()

    @tool
    def read_file(path: str) -> str:
        """Read the contents of a file from the workspace."""
        full = (workspace / path).resolve()
        if not str(full).startswith(str(workspace)):
            return "Error: path outside workspace."
        if not full.exists():
            return f"Error: file not found: {path}"
        return full.read_text(encoding="utf-8", errors="replace")

    @tool
    def write_file(path: str, content: str) -> str:
        """Write content to a file in the workspace (creates parent dirs)."""
        full = (workspace / path).resolve()
        if not str(full).startswith(str(workspace)):
            return "Error: path outside workspace."
        full.parent.mkdir(parents=True, exist_ok=True)
        full.write_text(content, encoding="utf-8")
        return f"Written: {path}"

    @tool
    def list_files(path: str = ".") -> str:
        """List files and directories under a path in the workspace."""
        full = (workspace / path).resolve()
        if not str(full).startswith(str(workspace)):
            return "Error: path outside workspace."
        if not full.exists():
            return f"Error: path not found: {path}"
        items = []
        for p in sorted(full.iterdir()):
            tag = "[DIR]" if p.is_dir() else "[FILE]"
            items.append(f"{tag} {p.relative_to(workspace)}")
        return "\n".join(items) if items else "(empty)"

    @tool
    def delete_file(path: str) -> str:
        """Delete a file or empty directory in the workspace."""
        full = (workspace / path).resolve()
        if not str(full).startswith(str(workspace)):
            return "Error: path outside workspace."
        if not full.exists():
            return f"Error: not found: {path}"
        if full.is_file():
            full.unlink()
            return f"Deleted file: {path}"
        if full.is_dir():
            full.rmdir()
            return f"Deleted empty directory: {path}"
        return f"Error: {path} is neither a file nor empty directory."

    return [read_file, write_file, list_files, delete_file]


def _build_system_message(workspace: Path) -> SystemMessage:
    return SystemMessage(
        content=(
            f"Your workspace is at {workspace}.\n"
            "You have file tools to read, write, list, and delete files here.\n"
            "You have built-in web search capabilities through your model.\n\n"
            "=== LEAN WORKSPACE RULES ===\n"
            "1. KEEP ONLY HIGH-VALUE FILES: Retain only final artifacts the user would want\n"
            "   (reports, generated scripts, datasets, or files the user explicitly asked for).\n"
            "2. AUTO-DELETE JUNK: Before finishing your response, use 'delete_file' to remove\n"
            "   all intermediate scratch files, .tmp files, raw HTML dumps, logs, or any file\n"
            "   that is NOT a final deliverable.\n"
            "3. OVERWRITE — NEVER DUPLICATE: If you need to update a file, overwrite it in place\n"
            "   using 'write_file' with the same path. Never create versioned copies (e.g.,\n"
            "   report_v1.txt, report_v2.txt) unless the user explicitly asks for a new version.\n\n"
            "When the user asks about their files or folder contents, use the file tools."
        )
    )


async def run_agent(
    api_key: str,
    model: str,
    provider: str,
    workspace: Path,
    user_message: str,
    chat_history: List[Dict[str, str]],
) -> Dict[str, Any]:
    tools = _make_file_tools(workspace)

    if provider == "gemini":
        from langchain_google_genai import ChatGoogleGenerativeAI

        llm = ChatGoogleGenerativeAI(
            model=model or "gemini-2.0-flash",
            google_api_key=api_key,
            temperature=0.7,
        )
    else:
        llm = ChatOpenAI(
            model=model or "perplexity/llama-3-sonar-large-32k-online",
            openai_api_key=api_key,
            base_url="https://openrouter.ai/api/v1",
            default_headers={
                "HTTP-Referer": "https://huggingface.co/spaces/react-agent",
                "X-Title": "React Agent",
            },
            temperature=0.7,
        )

    agent = create_react_agent(model=llm, tools=tools)

    messages = [_build_system_message(workspace)]

    for msg in chat_history:
        role = msg.get("role", "")
        content = msg.get("content", "")
        if role == "user":
            messages.append(HumanMessage(content=content))
        elif role == "assistant":
            messages.append(AIMessage(content=content))

    messages.append(HumanMessage(content=user_message))

    result = await agent.ainvoke({"messages": messages})

    agent_response = ""
    for msg in reversed(result.get("messages", [])):
        if isinstance(msg, AIMessage) and msg.content:
            agent_response = msg.content
            break

    updated_chat_history = []
    for msg in result.get("messages", []):
        if isinstance(msg, HumanMessage):
            updated_chat_history.append({"role": "user", "content": msg.content})
        elif isinstance(msg, AIMessage):
            updated_chat_history.append({"role": "assistant", "content": msg.content})

    return {
        "agent_response": agent_response,
        "updated_chat_history": updated_chat_history,
    }