# codingo/chatbot/chatbot.py
"""Chatbot module for Codingo …

Default model changed to blenderbot-400M-distill; generation uses max_new_tokens; fallback between causal and seq2seq models."""

import os
import shutil
from typing import List

os.environ.setdefault("HF_HOME", "/tmp/huggingface")
os.environ.setdefault("TRANSFORMERS_CACHE", "/tmp/huggingface/transformers")
os.environ.setdefault("HUGGINGFACE_HUB_CACHE", "/tmp/huggingface/hub")

_hf_model = None
_hf_tokenizer = None
_chatbot_embedder = None
_chatbot_collection = None

_current_dir = os.path.dirname(os.path.abspath(__file__))
_knowledge_base_path = os.path.join(_current_dir, "chatbot.txt")
_chroma_db_dir = "/tmp/chroma_db"

DEFAULT_MODEL_NAME = "facebook/blenderbot-400M-distill"

def _init_hf_model() -> None:
    from transformers import (
        AutoModelForCausalLM,
        AutoModelForSeq2SeqLM,
        AutoTokenizer,
    )
    import torch

    global _hf_model, _hf_tokenizer
    if _hf_model is not None and _hf_tokenizer is not None:
        return

    model_name = os.getenv("HF_CHATBOT_MODEL", DEFAULT_MODEL_NAME)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    try:
        model = AutoModelForCausalLM.from_pretrained(model_name)
    except Exception:
        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    model = model.to(device)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    _hf_model = model
    _hf_tokenizer = tokenizer

def _init_vector_store() -> None:
    global _chatbot_embedder, _chatbot_collection
    if _chatbot_embedder is not None and _chatbot_collection is not None:
        return

    from langchain.text_splitter import RecursiveCharacterTextSplitter
    from sentence_transformers import SentenceTransformer
    import chromadb
    from chromadb.config import Settings

    shutil.rmtree("/app/chatbot/chroma_db", ignore_errors=True)
    os.makedirs(_chroma_db_dir, exist_ok=True)
    try:
        with open(_knowledge_base_path, encoding="utf-8") as f:
            raw_text = f.read()
    except FileNotFoundError:
        raw_text = (
            "Codingo is an AI-powered recruitment platform designed to "
            "streamline job applications, candidate screening, and hiring. "
            "We make hiring smarter, faster, and fairer through automation "
            "and intelligent recommendations."
        )

    splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=100)
    docs: List[str] = [doc.strip() for doc in splitter.split_text(raw_text) if doc.strip()]
    embedder = SentenceTransformer("all-MiniLM-L6-v2")
    embeddings = embedder.encode(docs, show_progress_bar=False, batch_size=32)
    client = chromadb.Client(Settings(
        persist_directory=_chroma_db_dir,
        anonymized_telemetry=False,
        is_persistent=True,
    ))
    collection = client.get_or_create_collection("chatbot")
    try:
        existing = collection.get(limit=1)
        if not existing.get("documents"):
            raise ValueError("Empty Chroma DB")
    except Exception:
        ids = [f"doc_{i}" for i in range(len(docs))]
        collection.add(documents=docs, embeddings=embeddings.tolist(), ids=ids)

    _chatbot_embedder = embedder
    _chatbot_collection = collection

def get_chatbot_response(query: str) -> str:
    if not query or not query.strip():
        return "Please type a question about the Codingo platform."

    _init_vector_store()
    _init_hf_model()
    embedder = _chatbot_embedder
    collection = _chatbot_collection
    model = _hf_model
    tokenizer = _hf_tokenizer

    import torch

    query_embedding = embedder.encode([query])[0]
    results = collection.query(query_embeddings=[query_embedding.tolist()], n_results=3)
    retrieved_docs = results.get("documents", [[]])[0] if results else []
    context = "\n".join(retrieved_docs[:3])

    system_instruction = (
        "You are LUNA AI, a helpful assistant for the Codingo recruitment "
        "platform. Use the provided context to answer questions about "
        "Codingo. If the question is not related to Codingo, politely "
        "redirect the conversation. Keep responses concise and friendly."
    )
    prompt = f"{system_instruction}\n\nContext:\n{context}\n\nUser: {query}\nLUNA AI:"
    inputs = tokenizer.encode(
        prompt, return_tensors="pt", truncation=True, max_length=512, padding=True
    ).to(model.device)

    with torch.no_grad():
        output_ids = model.generate(
            inputs,
            max_new_tokens=150,
            num_beams=3,
            do_sample=True,
            temperature=0.7,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id,
            early_stopping=True,
        )

    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    if "LUNA AI:" in response:
        response = response.split("LUNA AI:")[-1].strip()
    elif prompt in response:
        response = response.replace(prompt, "").strip()

    return (
        response
        if response
        else "I'm here to help you with questions about the Codingo platform. What would you like to know?"
    )