import os
import re
import time
import functools
from typing import Dict, Any, List

import pandas as pd

# LangGraph
from langgraph.graph import StateGraph, START, END, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition

# LangChain Core
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool

# Google Gemini
from langchain_google_genai import ChatGoogleGenerativeAI

# Tools
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.utilities.wikipedia import WikipediaAPIWrapper

# Python REPL Tool
try:
    from langchain_experimental.tools.python.tool import PythonAstREPLTool
except ImportError:
    from langchain.tools.python.tool import PythonAstREPLTool

# ---------------------------------------------------------------------
# 0) Optionale LangSmith-Tracing  (setze ENV: LANGCHAIN_API_KEY)
# ---------------------------------------------------------------------

if os.getenv("LANGCHAIN_API_KEY"):
    os.environ["LANGCHAIN_TRACING_V2"] = "true"
    os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
    os.environ.setdefault("LANGCHAIN_PROJECT", "gaia-agent")
    print("📡 LangSmith tracing enabled.")

# ---------------------------------------------------------------------
# 1) Helfer:  Fehler-Decorator + Backoff-Wrapper
# ---------------------------------------------------------------------
def error_guard(fn):
    """Fängt Tool-Fehler ab & gibt String zurück (bricht Agent nicht ab)."""
    @functools.wraps(fn)
    def wrapper(*args, **kw):
        try:
            return fn(*args, **kw)
        except Exception as e:
            return f"ERROR: {e}"
    return wrapper


def with_backoff(fn, tries: int = 4, delay: int = 4):
    """Synchrones Retry-Wrapper für LLM-Aufrufe."""
    for t in range(tries):
        try:
            return fn()
        except Exception as e:
            if ("429" in str(e) or "RateLimit" in str(e)) and t < tries - 1:
                time.sleep(delay)
                delay *= 2
                continue
            raise

# ---------------------------------------------------------------------
# 2) Eigene Tools  (CSV / Excel)
# ---------------------------------------------------------------------
@tool
@error_guard
def parse_csv(file_path: str, query: str = "") -> str:
    """Load a CSV file and (optional) run a pandas query."""
    df = pd.read_csv(file_path)
    if not query:
        return f"Rows={len(df)}, Cols={list(df.columns)}"
    try:
        return df.query(query).to_markdown(index=False)
    except Exception as e:
        return f"ERROR query: {e}"


@tool
@error_guard
def parse_excel(file_path: str, sheet: str | int | None = None, query: str = "") -> str:
    """Load an Excel sheet (name or index) and (optional) run a pandas query."""
    sheet_arg = int(sheet) if isinstance(sheet, str) and sheet.isdigit() else sheet or 0
    df = pd.read_excel(file_path, sheet_name=sheet_arg)
    if not query:
        return f"Rows={len(df)}, Cols={list(df.columns)}"
    try:
        return df.query(query).to_markdown(index=False)
    except Exception as e:
        return f"ERROR query: {e}"

# ---------------------------------------------------------------------
# 3) Externe Search-Tools (Tavily, Wikipedia)
# ---------------------------------------------------------------------
@tool
@error_guard
def web_search(query: str, max_results: int = 5) -> str:
    """Search the web via Tavily and return markdown list of results."""
    api_key = os.getenv("TAVILY_API_KEY")
    hits = TavilySearchResults(max_results=max_results, api_key=api_key).invoke(query)
    if not hits:
        return "No results."
    return "\n".join(f"{h['title']} – {h['url']}" for h in hits)


@tool
@error_guard
def wiki_search(query: str, sentences: int = 3) -> str:
    """Quick Wikipedia summary."""
    wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=4000)
    res = wrapper.run(query)
    return "\n".join(res.split(". ")[:sentences]) if res else "No article found."

# ---------------------------------------------------------------------
# 4) Python-REPL Tool (fertig aus LangChain)
# ---------------------------------------------------------------------
python_repl = PythonAstREPLTool()

# ---------------------------------------------------------------------
# 5) LLM – Gemini Flash, an Tools gebunden
# ---------------------------------------------------------------------
gemini_llm = ChatGoogleGenerativeAI(
    google_api_key=os.getenv("GOOGLE_API_KEY"),
    model="gemini-2.0-flash",
    temperature=0,
    max_output_tokens=2048,
)

# ---------------------------------------------------------------------
# 6) System-Prompt (ReAct, keine Prefixe im Final-Output!)
# ---------------------------------------------------------------------
SYSTEM_PROMPT = SystemMessage(
    content=(
        "You are a helpful assistant with access to several tools.\n"
        "You can think step by step and use tools to find answers.\n\n"
        "When you want to use a tool, write it like this:\n"
        "Tool: <tool_name>\n"
        "Input: <input for the tool>\n\n"
        "Wait for the tool result before continuing.\n"
        "When you know the final answer, reply with the answer **only**.\n"
        "Don't include any prefix, explanation or formatting around the answer.\n"
        "Answer formatting:\n"
        "- For numbers: no units unless requested\n"
        "- For strings: no articles or abbreviations\n"
        "- For lists: comma + space separated, correct order\n"
    )
)

# ---------------------------------------------------------------------
# 7) LangGraph – Planner + Tools + Router
# ---------------------------------------------------------------------

def planner(state: MessagesState):
    msgs = state["messages"]
    if msgs[0].type != "system":
        msgs = [SYSTEM_PROMPT] + msgs

    resp = with_backoff(lambda: gemini_llm.invoke(msgs))

    # WICHTIG: Gib tool_calls weiter – sie lösen im ToolNode die Ausführung aus
    return {
        "messages": msgs + [resp],
        "should_end": (
            not getattr(resp, "tool_calls", None)  # kein Tool gewünscht
            and "\n" not in resp.content           # einfache Heuristik
        )
    }

def route(state):
    return "END" if state["should_end"] else "tools"

# Tool-Knoten
TOOLS = [web_search, wiki_search, parse_csv, parse_excel, python_repl]

graph = StateGraph(MessagesState)

graph.add_node("planner", planner)
graph.add_node("tools", ToolNode(TOOLS))

graph.add_edge(START, "planner")
graph.add_edge("tools", "planner")  # 🔁 Rücksprung zum Planner nach Tool-Ausführung

graph.add_conditional_edges("planner", route, {
    "tools": "tools",
    "END": END,
})

# compile → LangGraph-Executor
agent_executor = graph.compile()

# ---------------------------------------------------------------------
# 8) Öffentliche Klasse  –  wird von app.py / logic.py verwendet
# ---------------------------------------------------------------------
class GaiaAgent:
    """LangChain·LangGraph-Agent für GAIA Level 1."""

    def __init__(self):
        print("✅ GaiaAgent initialised (LangGraph)")

    def __call__(self, task_id: str, question: str) -> str:
        """Run the agent on a single GAIA question → exact answer string."""
        start_state = {"messages": [HumanMessage(content=question)]}
        final_state = agent_executor.invoke(start_state)
        # letze Message enthält Antwort
        answer = final_state["messages"][-1].content
        return answer.strip()