import json
import os
from typing import List, Optional

import pandas as pd
import pytesseract
import requests
from dotenv import load_dotenv
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.tools import tool
from langchain_groq import ChatGroq
from langgraph.graph import START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition
from PIL import Image

from code_interpreter import CodeInterpreter
from extract_answer import extract_final_answer
from files_util import format_user_message

load_dotenv()

ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
PROMPT_PATH = os.path.join(ROOT_DIR, "system_prompt.txt")
MAX_TOOL_ROUNDS = 8
RECURSION_LIMIT = 20

_interpreter = None


def get_system_prompt() -> str:
    with open(PROMPT_PATH, "r", encoding="utf-8") as handle:
        return handle.read()


def get_interpreter() -> CodeInterpreter:
    global _interpreter
    if _interpreter is None:
        _interpreter = CodeInterpreter()
    return _interpreter


@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia and return up to 2 page excerpts.

    Args:
        query: Search query, preferably a proper name or article title.
    """
    docs = WikipediaLoader(query=query, load_max_docs=2).load()
    if not docs:
        return "No Wikipedia results."
    chunks = []
    for doc in docs:
        source = doc.metadata.get("source", "")
        chunks.append(f'<Document source="{source}">\n{doc.page_content}\n</Document>')
    return "\n\n---\n\n".join(chunks)


@tool
def web_search(query: str) -> str:
    """Search the public web via Tavily and return up to 3 results.

    Args:
        query: Search query. Be specific; include years, full names, and distinctive terms.
    """
    results = TavilySearchResults(max_results=3).invoke(query)
    if not results:
        return "No web results."
    chunks = []
    for doc in results:
        chunks.append(
            f'<Document source="{doc.get("url", "")}" title="{doc.get("title", "")}">\n'
            f'{doc.get("content", "")}\n</Document>'
        )
    return "\n\n---\n\n".join(chunks)


@tool
def execute_python(code: str) -> str:
    """Run Python for calculation, pandas/Excel/CSV analysis, dates, and string parsing.

    Args:
        code: Complete Python source. Print the values you need. Use absolute file paths from the question.
    """
    result = get_interpreter().execute_code(code, language="python")
    lines = []
    if result["status"] == "success":
        lines.append("Python execution succeeded.")
        if result.get("stdout"):
            lines.append("stdout:\n" + result["stdout"].strip())
        if result.get("stderr"):
            lines.append("stderr:\n" + result["stderr"].strip())
        if result.get("result") is not None:
            lines.append("result:\n" + str(result["result"]).strip())
        for df_info in result.get("dataframes") or []:
            preview = pd.DataFrame(df_info["head"])
            lines.append(f"DataFrame {df_info['name']} shape={df_info['shape']}\n{preview}")
    else:
        lines.append("Python execution failed.")
        if result.get("stderr"):
            lines.append(result["stderr"].strip())
    return "\n\n".join(lines)[:12000]


@tool
def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
    """Download a file from an http(s) URL to a local path and return that path.

    Args:
        url: Direct download URL.
        filename: Optional filename to use in the temp directory.
    """
    from urllib.parse import urlparse
    import tempfile
    import uuid

    try:
        if not filename:
            filename = os.path.basename(urlparse(url).path) or f"download_{uuid.uuid4().hex[:8]}"
        filepath = os.path.join(tempfile.gettempdir(), filename)
        response = requests.get(url, timeout=60, stream=True)
        response.raise_for_status()
        with open(filepath, "wb") as handle:
            for chunk in response.iter_content(chunk_size=8192):
                handle.write(chunk)
        return f"Downloaded to {filepath}"
    except Exception as exc:
        return f"Download failed: {exc}"


@tool
def read_file(file_path: str, max_chars: int = 8000) -> str:
    """Read a local text, CSV, Excel, JSON, or PDF file and return a preview.

    Args:
        file_path: Absolute path on disk.
        max_chars: Truncate text-like previews to this many characters.
    """
    if not os.path.exists(file_path):
        return f"File not found: {file_path}"

    ext = os.path.splitext(file_path)[1].lower()
    try:
        if ext in {".csv"}:
            df = pd.read_csv(file_path)
            return (
                f"CSV rows={len(df)} cols={list(df.columns)}\n"
                f"{df.head(20).to_string()}\n\n{df.describe(include='all')}"
            )[:max_chars]
        if ext in {".xlsx", ".xls"}:
            df = pd.read_excel(file_path)
            return (
                f"Excel rows={len(df)} cols={list(df.columns)}\n"
                f"{df.head(20).to_string()}\n\n{df.describe(include='all')}"
            )[:max_chars]
        if ext == ".pdf":
            try:
                import fitz
            except ImportError:
                return "PyMuPDF is not installed; use execute_python if available."
            doc = fitz.open(file_path)
            text = "\n".join(page.get_text() for page in doc)
            doc.close()
            return text[:max_chars] or "PDF had no extractable text."
        with open(file_path, "r", encoding="utf-8", errors="replace") as handle:
            return handle.read(max_chars)
    except Exception as exc:
        return f"Failed to read {file_path}: {exc}"


@tool
def extract_text_from_image(image_path: str) -> str:
    """OCR text from a local image with Tesseract.

    Args:
        image_path: Absolute path to a jpg/png/webp/gif/bmp file.
    """
    try:
        image = Image.open(image_path)
        text = pytesseract.image_to_string(image)
        return text.strip() or "OCR returned no text."
    except Exception as exc:
        return f"OCR failed: {exc}"


TOOLS = [
    web_search,
    wiki_search,
    execute_python,
    download_file_from_url,
    read_file,
    extract_text_from_image,
]


def _tool_signature(message) -> Optional[str]:
    calls = getattr(message, "tool_calls", None) or []
    if not calls:
        return None
    parts = []
    for call in calls:
        name = call.get("name") if isinstance(call, dict) else getattr(call, "name", "")
        args = call.get("args") if isinstance(call, dict) else getattr(call, "args", {})
        parts.append(f"{name}:{json.dumps(args, sort_keys=True, default=str)}")
    return "|".join(parts)


def _should_force_final(messages) -> bool:
    tool_round_messages = [m for m in messages if getattr(m, "tool_calls", None)]
    if len(tool_round_messages) >= MAX_TOOL_ROUNDS:
        return True
    if len(tool_round_messages) >= 2:
        if _tool_signature(tool_round_messages[-1]) == _tool_signature(tool_round_messages[-2]):
            return True
    return False


def require_env(name: str) -> str:
    value = os.getenv(name, "").strip()
    if value:
        return value
    raise RuntimeError(
        f"Missing {name}. "
        "Local: put it in `.env`. "
        "Hugging Face Space: Settings → Variables and secrets → New secret."
    )


def build_graph(provider: str = "groq", use_retriever: bool = False):
    """Compile the tool-calling graph. Retriever is off unless explicitly enabled."""
    if provider != "groq":
        raise ValueError("Only provider='groq' is supported after the refactor.")

    api_key = require_env("GROQ_API_KEY")
    llm = ChatGroq(model="qwen/qwen3-32b", temperature=0, api_key=api_key)
    llm_with_tools = llm.bind_tools(TOOLS)

    retriever_store = _build_optional_retriever() if use_retriever else None

    def maybe_example(state: MessagesState):
        if retriever_store is None:
            return {}
        question = ""
        for message in state["messages"]:
            if isinstance(message, HumanMessage):
                question = message.content
                break
        hits = retriever_store.similarity_search(question, k=1)
        if not hits:
            return {}
        return {
            "messages": [
                HumanMessage(
                    content=(
                        "Optional similar example for format only. "
                        "Do not copy if it is a different question.\n\n"
                        f"{hits[0].page_content}"
                    )
                )
            ]
        }

    def assistant(state: MessagesState):
        messages = state["messages"]
        if _should_force_final(messages):
            stop = SystemMessage(
                content=(
                    "Stop calling tools. Using only the information already in this "
                    "conversation, reply with one line: FINAL ANSWER: <answer>"
                )
            )
            return {"messages": [llm.invoke(messages + [stop])]}
        return {"messages": [llm_with_tools.invoke(messages)]}

    builder = StateGraph(MessagesState)
    builder.add_node("maybe_example", maybe_example)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(TOOLS))
    builder.add_edge(START, "maybe_example")
    builder.add_edge("maybe_example", "assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")
    return builder.compile()


def _build_optional_retriever():
    url = os.environ.get("SUPABASE_URL")
    key = os.environ.get("SUPABASE_SERVICE_ROLE_KEY")
    if not url or not key:
        print("Retriever requested but SUPABASE_* env vars are missing; skipping.")
        return None
    from langchain_community.vectorstores import SupabaseVectorStore
    from langchain_huggingface import HuggingFaceEmbeddings
    from supabase.client import create_client

    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
    client = create_client(url, key)
    return SupabaseVectorStore(
        client=client,
        embedding=embeddings,
        table_name="documents2",
        query_name="match_documents_2",
    )


def run_agent(
    question: str,
    file_paths: Optional[List[str]] = None,
    graph=None,
    use_retriever: bool = False,
) -> dict:
    """Run one question and return raw last message plus extracted FINAL ANSWER."""
    if graph is None:
        graph = build_graph(use_retriever=use_retriever)
    payload = format_user_message(question, file_paths)
    result = graph.invoke(
        {
            "messages": [
                SystemMessage(content=get_system_prompt()),
                HumanMessage(content=payload),
            ]
        },
        {"recursion_limit": RECURSION_LIMIT},
    )
    last = result["messages"][-1]
    raw = last.content if hasattr(last, "content") else str(last)
    if isinstance(raw, list):
        raw = "".join(
            part.get("text", "") if isinstance(part, dict) else str(part) for part in raw
        )
    return {
        "raw": raw,
        "final": extract_final_answer(raw),
        "messages": result["messages"],
    }


if __name__ == "__main__":
    demo = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
    output = run_agent(demo)
    print("RAW:\n", output["raw"])
    print("FINAL:", output["final"])