import os
import getpass
import spacy  # Import spaCy for NER functionality
import pandas as pd
from typing import Optional
from langchain.docstore.document import Document
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from smolagents import CodeAgent, DuckDuckGoSearchTool, ManagedAgent, LiteLLMModel
# import litellm

# Import the chain builders from our separate files
from classification_chain import get_classification_chain
from refusal_chain import get_refusal_chain
from tailor_chain import get_tailor_chain
from cleaner_chain import get_cleaner_chain, CleanerChain

# 1) Environment: set up keys if missing
if not os.environ.get("GEMINI_API_KEY"):
    os.environ["GEMINI_API_KEY"] = getpass.getpass("Enter your Gemini API Key: ")
if not os.environ.get("GROQ_API_KEY"):
    os.environ["GROQ_API_KEY"] = getpass.getpass("Enter your GROQ API Key: ")

# 2) Load spaCy model for NER
nlp = spacy.load("en_core_web_sm")

# Function to extract the main topic using NER
def extract_main_topic(query: str) -> str:
    """
    Extracts the main topic from the user's query using spaCy's NER.
    Returns the first named entity or noun found in the query.
    """
    doc = nlp(query)
    
    # Try to extract the main topic as a named entity (person, product, etc.)
    main_topic = None
    for ent in doc.ents:
        # Filter for specific entity types (you can adjust this based on your needs)
        if ent.label_ in ["ORG", "PRODUCT", "PERSON", "GPE", "TIME"]:  # Add more entity labels as needed
            main_topic = ent.text
            break
    
    # If no named entity found, fallback to extracting the first noun or proper noun
    if not main_topic:
        for token in doc:
            if token.pos_ in ["NOUN", "PROPN"]:  # Extract first noun or proper noun
                main_topic = token.text
                break
    
    # Return the extracted topic or a fallback value if no topic is found
    return main_topic if main_topic else "this topic"

# 3) build_or_load_vectorstore (no changes)
def build_or_load_vectorstore(csv_path: str, store_dir: str) -> FAISS:
    if os.path.exists(store_dir):
        print(f"DEBUG: Found existing FAISS store at '{store_dir}'. Loading...")
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/multi-qa-mpnet-base-dot-v1")
        vectorstore = FAISS.load_local(store_dir, embeddings)
        return vectorstore
    else:
        print(f"DEBUG: Building new store from CSV: {csv_path}")
        df = pd.read_csv(csv_path)
        df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
        df.columns = df.columns.str.strip()
        if "Answer" in df.columns:
            df.rename(columns={"Answer": "Answers"}, inplace=True)
        if "Question" not in df.columns and "Question " in df.columns:
            df.rename(columns={"Question ": "Question"}, inplace=True)
        if "Question" not in df.columns or "Answers" not in df.columns:
            raise ValueError("CSV must have 'Question' and 'Answers' columns.")
        docs = []
        for _, row in df.iterrows():
            q = str(row["Question"])
            ans = str(row["Answers"])
            doc = Document(page_content=ans, metadata={"question": q})
            docs.append(doc)
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/multi-qa-mpnet-base-dot-v1")
        vectorstore = FAISS.from_documents(docs, embedding=embeddings)
        vectorstore.save_local(store_dir)
        return vectorstore

# 4) Build RAG chain for Gemini (no changes)
def build_rag_chain(llm_model: LiteLLMModel, vectorstore: FAISS) -> RetrievalQA:
    class GeminiLangChainLLM(LLM):
        def _call(self, prompt: str, stop: Optional[list] = None, **kwargs) -> str:
            messages = [{"role": "user", "content": prompt}]
            return llm_model(messages, stop_sequences=stop)
        @property
        def _llm_type(self) -> str:
            return "custom_gemini"
    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 3})
    gemini_as_llm = GeminiLangChainLLM()
    rag_chain = RetrievalQA.from_chain_type(
        llm=gemini_as_llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True
    )
    return rag_chain

# 5) Initialize all the separate chains
classification_chain = get_classification_chain()
refusal_chain = get_refusal_chain()  # Refusal chain will now use dynamic topic
tailor_chain = get_tailor_chain()
cleaner_chain = get_cleaner_chain()

# 6) Build our vectorstores + RAG chains
wellness_csv = "AIChatbot.csv"
brand_csv = "BrandAI.csv"
wellness_store_dir = "faiss_wellness_store"
brand_store_dir = "faiss_brand_store"

wellness_vectorstore = build_or_load_vectorstore(wellness_csv, wellness_store_dir)
brand_vectorstore = build_or_load_vectorstore(brand_csv, brand_store_dir)

gemini_llm = LiteLLMModel(model_id="gemini/gemini-pro", api_key=os.environ.get("GEMINI_API_KEY"))
wellness_rag_chain = build_rag_chain(gemini_llm, wellness_vectorstore)
brand_rag_chain = build_rag_chain(gemini_llm, brand_vectorstore)

# 7) Tools / Agents for web search (no changes)
search_tool = DuckDuckGoSearchTool()
web_agent = CodeAgent(tools=[search_tool], model=gemini_llm)
managed_web_agent = ManagedAgent(agent=web_agent, name="web_search", description="Runs web search for you.")
manager_agent = CodeAgent(tools=[], model=gemini_llm, managed_agents=[managed_web_agent])

def do_web_search(query: str) -> str:
    print("DEBUG: Attempting web search for more info...")
    search_query = f"Give me relevant info: {query}"
    response = manager_agent.run(search_query)
    return response

# 8) Orchestrator: run_with_chain
def run_with_chain(query: str) -> str:
    print("DEBUG: Starting run_with_chain...")
    
    # 1) Classify the query
    class_result = classification_chain.invoke({"query": query})
    classification = class_result.get("text", "").strip()
    print("DEBUG: Classification =>", classification)

    # If OutOfScope => refusal => tailor => return
    if classification == "OutOfScope":
        # Extract the main topic for the refusal message
        topic = extract_main_topic(query)
        print("DEBUG: Extracted Topic =>", topic)
        
        # Pass the extracted topic to the refusal chain
        refusal_text = refusal_chain.run({"topic": topic})
        final_refusal = tailor_chain.run({"response": refusal_text})
        return final_refusal.strip()

    # If Wellness => wellness RAG => if insufficient => web => unify => tailor
    if classification == "Wellness":
        rag_result = wellness_rag_chain({"query": query})
        csv_answer = rag_result["result"].strip()
        if not csv_answer:
            web_answer = do_web_search(query)
        else:
            lower_ans = csv_answer.lower()
            if any(phrase in lower_ans for phrase in ["i do not know", "not sure", "no context", "cannot answer"]):
                web_answer = do_web_search(query)
            else:
                web_answer = ""
        final_merged = cleaner_chain.merge(kb=csv_answer, web=web_answer)
        final_answer = tailor_chain.run({"response": final_merged})
        return final_answer.strip()

    # If Brand => brand RAG => tailor => return
    if classification == "Brand":
        rag_result = brand_rag_chain({"query": query})
        csv_answer = rag_result["result"].strip()
        final_merged = cleaner_chain.merge(kb=csv_answer, web="")
        final_answer = tailor_chain.run({"response": final_merged})
        return final_answer.strip()

    # fallback
    refusal_text = refusal_chain.run({"topic": "this topic"})
    final_refusal = tailor_chain.run({"response": refusal_text})
    return final_refusal.strip()