import os
import time
import random
from dotenv import load_dotenv
from typing import List, Dict, Any, TypedDict, Annotated
import operator

from langchain_core.tools import tool
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.vectorstores import FAISS
from langchain.tools.retriever import create_retriever_tool
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_community.embeddings import SentenceTransformerEmbeddings

from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver

# Load environment variables
load_dotenv()

# ---- Tool Definitions ----
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers and return the product."""
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two integers and return the sum."""
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract the second integer from the first and return the difference."""
    return a - b

@tool
def divide(a: int, b: int) -> float:
    """Divide the first integer by the second and return the quotient."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Return the remainder of the division of the first integer by the second."""
    return a % b

@tool
def optimized_web_search(query: str) -> str:
    """Perform an optimized web search using TavilySearchResults and return concatenated document snippets."""
    try:
        time.sleep(random.uniform(1, 2))
        search_tool = TavilySearchResults(max_results=2)
        docs = search_tool.invoke({"query": query})
        return "\n\n---\n\n".join(
            f"<Doc url='{d.get('url','')}'>{d.get('content','')[:500]}</Doc>"
            for d in docs
        )
    except Exception as e:
        return f"Web search failed: {e}"

@tool
def optimized_wiki_search(query: str) -> str:
    """Perform an optimized Wikipedia search and return concatenated document snippets."""
    try:
        time.sleep(random.uniform(0.5, 1))
        docs = WikipediaLoader(query=query, load_max_docs=1).load()
        return "\n\n---\n\n".join(
            f"<Doc src='{d.metadata.get('source', 'Wikipedia')}'>{d.page_content[:800]}</Doc>"
            for d in docs
        )
    except Exception as e:
        return f"Wikipedia search failed: {e}"

# ---- LLM Integrations with Error Handling ----
try:
    from langchain_groq import ChatGroq
    GROQ_AVAILABLE = True
except ImportError:
    GROQ_AVAILABLE = False

import requests

def deepseek_generate(prompt, api_key=None):
    """Call DeepSeek API directly."""
    if not api_key:
        return "DeepSeek API key not provided"
    
    url = "https://api.deepseek.com/v1/chat/completions"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    data = {
        "model": "deepseek-chat",
        "messages": [{"role": "user", "content": prompt}],
        "stream": False
    }
    try:
        resp = requests.post(url, headers=headers, json=data, timeout=30)
        resp.raise_for_status()
        choices = resp.json().get("choices", [])
        if choices and "message" in choices[0]:
            return choices[0]["message"].get("content", "")
        return "No response from DeepSeek"
    except Exception as e:
        return f"DeepSeek API error: {e}"

def baidu_ernie_generate(prompt, api_key=None):
    """Call Baidu ERNIE API."""
    if not api_key:
        return "Baidu ERNIE API key not provided"
    
    # Baidu ERNIE API endpoint (replace with actual endpoint)
    url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    data = {
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.1,
        "top_p": 0.8
    }
    try:
        resp = requests.post(url, headers=headers, json=data, timeout=30)
        resp.raise_for_status()
        result = resp.json().get("result", "")
        return result if result else "No response from Baidu ERNIE"
    except Exception as e:
        return f"Baidu ERNIE API error: {e}"

# ---- Graph State ----
class EnhancedAgentState(TypedDict):
    messages: Annotated[List[HumanMessage|AIMessage], operator.add]
    query: str
    agent_type: str
    final_answer: str
    perf: Dict[str,Any]
    agno_resp: str

class HybridLangGraphMultiLLMSystem:
    def __init__(self, provider="groq"):
        self.provider = provider
        self.tools = [
            multiply, add, subtract, divide, modulus,
            optimized_web_search, optimized_wiki_search
        ]
        self.graph = self._build_graph()

    def _build_graph(self):
        # Initialize Groq LLM with error handling
        groq_llm = None
        
        if GROQ_AVAILABLE and os.getenv("GROQ_API_KEY"):
            try:
                # Use Groq for multiple model access
                groq_llm = ChatGroq(
                    model="llama-3.1-70b-versatile",  # Updated to a current model
                    temperature=0, 
                    api_key=os.getenv("GROQ_API_KEY")
                )
            except Exception as e:
                print(f"Failed to initialize Groq: {e}")

        def router(st: EnhancedAgentState) -> EnhancedAgentState:
            q = st["query"].lower()
            if "groq" in q and groq_llm:      
                t = "groq"
            elif "deepseek" in q: 
                t = "deepseek"
            elif "ernie" in q or "baidu" in q: 
                t = "baidu"
            else: 
                # Default to first available provider
                if groq_llm:
                    t = "groq"
                elif os.getenv("DEEPSEEK_API_KEY"):
                    t = "deepseek"
                else:
                    t = "baidu"
            return {**st, "agent_type": t}

        def groq_node(st: EnhancedAgentState) -> EnhancedAgentState:
            if not groq_llm:
                return {**st, "final_answer": "Groq not available", "perf": {"error": "No Groq LLM"}}
            
            t0 = time.time()
            try:
                sys = SystemMessage(content="You are a helpful AI assistant. Provide accurate and detailed answers. Be concise but thorough.")
                res = groq_llm.invoke([sys, HumanMessage(content=st["query"])])
                return {**st, "final_answer": res.content, "perf": {"time": time.time() - t0, "prov": "Groq"}}
            except Exception as e:
                return {**st, "final_answer": f"Groq error: {e}", "perf": {"error": str(e)}}

        def deepseek_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            try:
                prompt = f"You are a helpful AI assistant. Provide accurate and detailed answers. Be concise but thorough.\n\nUser question: {st['query']}"
                resp = deepseek_generate(prompt, api_key=os.getenv("DEEPSEEK_API_KEY"))
                return {**st, "final_answer": resp, "perf": {"time": time.time() - t0, "prov": "DeepSeek"}}
            except Exception as e:
                return {**st, "final_answer": f"DeepSeek error: {e}", "perf": {"error": str(e)}}

        def baidu_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            try:
                prompt = f"You are a helpful AI assistant. Provide accurate and detailed answers. Be concise but thorough.\n\nUser question: {st['query']}"
                resp = baidu_ernie_generate(prompt, api_key=os.getenv("BAIDU_API_KEY"))
                return {**st, "final_answer": resp, "perf": {"time": time.time() - t0, "prov": "Baidu ERNIE"}}
            except Exception as e:
                return {**st, "final_answer": f"Baidu ERNIE error: {e}", "perf": {"error": str(e)}}

        def pick(st: EnhancedAgentState) -> str:
            return st["agent_type"]

        g = StateGraph(EnhancedAgentState)
        g.add_node("router", router)
        g.add_node("groq", groq_node)
        g.add_node("deepseek", deepseek_node)
        g.add_node("baidu", baidu_node)
        g.set_entry_point("router")
        g.add_conditional_edges("router", pick, {
            "groq": "groq",
            "deepseek": "deepseek", 
            "baidu": "baidu"
        })
        for n in ["groq", "deepseek", "baidu"]:
            g.add_edge(n, END)
        return g.compile(checkpointer=MemorySaver())

    def process_query(self, q: str) -> str:
        state = {
            "messages": [HumanMessage(content=q)],
            "query": q,
            "agent_type": "",
            "final_answer": "",
            "perf": {},
            "agno_resp": ""
        }
        cfg = {"configurable": {"thread_id": f"hyb_{hash(q)}"}}
        try:
            out = self.graph.invoke(state, cfg)
            raw_answer = out.get("final_answer", "No answer generated")
            
            # Clean up the answer
            if isinstance(raw_answer, str):
                return raw_answer.strip()
            return str(raw_answer)
        except Exception as e:
            return f"Error processing query: {e}"

# Function expected by app.py
def build_graph(provider="groq"):
    """Build and return the graph for the agent system."""
    system = HybridLangGraphMultiLLMSystem(provider=provider)
    return system.graph

if __name__ == "__main__":
    query = "What are the main benefits of using multiple LLM providers?"
    system = HybridLangGraphMultiLLMSystem()
    result = system.process_query(query)
    print("LangGraph Multi-LLM Result:", result)