import os
from langchain_community.tools import WikipediaQueryRun, ArxivQueryRun
from langchain_community.utilities import WikipediaAPIWrapper, ArxivAPIWrapper
from langchain_huggingface import HuggingFacePipeline
from langchain.agents import initialize_agent, AgentType
from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline
from huggingface_hub import login
import torch
import traceback

# ✅ Login to HF
token = os.getenv("HF_TOKEN")
print("🔑 HF_TOKEN available?", token is not None)
if token:
    login(token=token)
else:
    print("❌ No HF_TOKEN found in environment")


def build_qa():
    print("🚀 Starting QA pipeline...")

    # ---- 1. Tools ----
    try:
        print("🔹 Initializing Wikipedia tool...")
        wiki_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200)
        wiki = WikipediaQueryRun(api_wrapper=wiki_wrapper)

        print("🔹 Initializing Arxiv tool...")
        arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
        arxiv = ArxivQueryRun(api_wrapper=arxiv_wrapper)

        tools = [wiki, arxiv]
        print("✅ Tools initialized")
    except Exception as e:
        print("❌ Tools initialization failed:", e)
        traceback.print_exc()
        return None

    # ---- 2. Model ----
    try:
        print("🔹 Loading Mistral 7B model...")
        model_name = "mistralai/Mistral-7B-Instruct-v0.3"  # or your CPU-quantized version
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForCausalLM.from_pretrained(
            model_name,
            device_map="auto",   # works after installing accelerate
            dtype=torch.float16, # instead of torch_dtype
        )
        llm = TextGenerationPipeline(
            model=model,
            tokenizer=tokenizer,
            max_new_tokens=256,
            temperature=0.2,
            do_sample=False,
            top_p=0.9,
            repetition_penalty=1.2,
            eos_token_id=tokenizer.eos_token_id,
            return_full_text=False,
        )
        hf_llm = HuggingFacePipeline(pipeline=llm)
        print(f"✅ Model loaded: {model_name}")
    except Exception as e:
        print("❌ Model load failed:", e)
        traceback.print_exc()
        return None

    # ---- 3. Agent ----
    try:
        print("🔹 Initializing agent...")
        agent = initialize_agent(
            tools=tools,
            llm=hf_llm,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            verbose=True,
            handle_parsing_errors=True,
        )
        print("✅ Agent initialized")
    except Exception as e:
        print("❌ Agent initialization failed:", e)
        traceback.print_exc()
        return None

    print("✅ QA pipeline ready")
    return agent


# ---- Build once ----
try:
    agent = build_qa()
    if agent:
        print("✅ QA pipeline built successfully:", type(agent))
    else:
        print("❌ QA pipeline build returned None")
except Exception as e:
    agent = None
    print("❌ Failed to build QA pipeline:", e)
    traceback.print_exc()


def get_response(user_message, history):
    if agent is None:
        return "⚠️ QA pipeline not initialized."

    try:
        print("💬 User query:", user_message)
        response = agent.invoke({"input": user_message})
        print("🤖 Agent response:", response)
        return response
    except Exception as e:
        print("❌ Agent execution failed:", e)
        traceback.print_exc()
        return f"❌ QA run failed: {e}"