import os
import torch
from qdrant_client import QdrantClient, models
from sentence_transformers import SentenceTransformer, CrossEncoder
from pymongo import MongoClient
from bson import ObjectId
from typing import List, Dict
import google.generativeai as genai
from groq import Groq 

def build_content(doc: dict, entity_type: str) -> str:
    """Convert MongoDB document into natural text for embeddings."""
    parts = [f"{entity_type} ID: {doc.get('id', str(doc.get('_id', '')))}"]
    for k, v in doc.items():
        if k in ["_id"]:  # skip ObjectId
            continue
        if isinstance(v, list):
            parts.append(f"{k}: {', '.join(map(str, v))}")
        elif isinstance(v, dict):
            nested = "; ".join([f"{nk}: {nv}" for nk, nv in v.items() if nv])
            parts.append(f"{k}: {nested}")
        else:
            if v:
                parts.append(f"{k}: {v}")
    return "\n".join(parts)


class ErrorBot:
    """Chatbot using RAG (Qdrant + Gemini API)."""

    def __init__(self, embedding_model_name: str, llm_model_name: str, google_api_key: str = None, groq_api_key: str = None, llm_provider: str = "gemini"):
        print("🚀 Initializing ErrorBot...")

        # --- Embedding model
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Using device: {self.device}")
        self.embedding_model = SentenceTransformer(embedding_model_name, device=self.device)
        self.embedding_dim = self.embedding_model.get_sentence_embedding_dimension()

        # --- Qdrant client
        print("Connecting to Qdrant...")
        self.qdrant = QdrantClient(
            url=os.getenv("QDRANT_URL"),
            api_key=os.getenv("QDRANT_API_KEY"),
        )
        self.collection_name = "technical_errors"
        self._setup_collection()

        # --- LLM setup
        self.llm_provider = llm_provider.lower()
        self.llm_model_name = llm_model_name

        if self.llm_provider == "gemini":
            genai.configure(api_key=google_api_key)
            self.llm = genai.GenerativeModel(llm_model_name)

        elif self.llm_provider == "groq":
            self.llm = Groq(api_key=groq_api_key)

        else:
            raise ValueError(f"Unsupported LLM provider: {self.llm_provider}")

        # --- Cross encoder reranker
        self.reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
        print(f"✅ ErrorBot ready with {self.llm_provider.upper()}")

    def _setup_collection(self):
        if not self.qdrant.collection_exists(self.collection_name):
            self.qdrant.create_collection(
                collection_name=self.collection_name,
                vectors_config=models.VectorParams(
                    size=self.embedding_dim,
                    distance=models.Distance.COSINE,
                ),
            )

    def ingest_from_mongodb(self, mongo_uri: str, db_name: str, batch_size: int = 32):
        client = MongoClient(mongo_uri)
        db = client[db_name]

        collections = {
            "ProblemReport": db["problemReports"],
            "FaultAnalysis": db["faultanalysis"],
            "Correction": db["corrections"],
        }

        docs = []
        for entity_type, coll in collections.items():
            for doc in coll.find():
                if "_id" in doc and isinstance(doc["_id"], ObjectId):
                    doc["_id"] = str(doc["_id"])
                docs.append({"entity_type": entity_type, "data": doc})

        contents = [build_content(d["data"], d["entity_type"]) for d in docs]

        all_embeddings = []
        for i in range(0, len(contents), batch_size):
            batch_contents = contents[i:i + batch_size]
            embeddings = self.embedding_model.encode(batch_contents, show_progress_bar=True).tolist()
            all_embeddings.extend(embeddings)

        self.qdrant.upsert(
            collection_name=self.collection_name,
            points=[
                models.PointStruct(
                    id=i,
                    vector=emb,
                    payload={
                        "id": d["data"].get("id", str(d["data"].get("_id", i))),
                        "entity_type": d["entity_type"],
                        "raw": d["data"],
                        "content": c,
                    },
                )
                for i, (d, emb, c) in enumerate(zip(docs, all_embeddings, contents))
            ],
            wait=True,
        )
        print(f"✅ Ingested {len(docs)} documents into '{self.collection_name}'")

    def retrieve(self, query: str, top_k: int = 5, score_threshold: float = 0.3, rerank: bool = True):
        query_embedding = self.embedding_model.encode(query).tolist()
        hits = self.qdrant.query_points(
            collection_name=self.collection_name,
            query=query_embedding,
            limit=top_k * 3 if rerank else top_k,
            with_payload=True,
            score_threshold=score_threshold,
        ).points

        candidates = [
            {
                "id": hit.payload.get("id"),
                "entity_type": hit.payload.get("entity_type", ""),
                "content": hit.payload.get("content", ""),
                "score": hit.score,
            }
            for hit in hits
        ]

        if rerank and candidates:
            pairs = [(query, c["content"]) for c in candidates]
            scores = self.reranker.predict(pairs)
            for i, score in enumerate(scores):
                candidates[i]["rerank_score"] = float(score)
            candidates = sorted(candidates, key=lambda x: x["rerank_score"], reverse=True)

        return candidates[:top_k]

    def generate_answer(self, query: str, context: List[Dict], history: list = None):
        context_str = "\n---\n".join(
            [f"{c['entity_type']} (Score: {c['score']:.2f}):\n{c['content']}" for c in context]
        )

        # --- System prompt
        system_prompt = f"""
    You are a technical assistant. You have access to Problem Reports (PR), Fault Analyses (FA), and Corrections (CR).
    Use the provided context and conversation history to answer the question clearly and concisely.
    If context is not relevant, say you do not have enough information.

    ### Context
    {context_str}
    """

        # --- Conversation history in list-of-dicts format
        convo = []
        if history:
            for msg in history:
                convo.append({
                    "role": "user" if msg["role"] == "user" else "assistant",
                    "content": msg["content"],
                })

        convo.append({"role": "user", "content": query})

        # --- Gemini flow
        if self.llm_provider == "gemini":
            convo_str = "\n".join([f"{m['role'].capitalize()}: {m['content']}" for m in convo])
            prompt = system_prompt + "\n\n" + convo_str + "\nAssistant:"
            response = self.llm.generate_content(prompt)
            return response.text.strip()

        # --- Groq flow
        elif self.llm_provider == "groq":
            completion = self.llm.chat.completions.create(
                model=self.llm_model_name,
                messages=[{"role": "system", "content": system_prompt}] + convo
            )
            return completion.choices[0].message.content.strip()


    def ask(self, query: str, history: list = None):
        print(f"\n❓ Query: {query}")
        retrieved_context = self.retrieve(query)

        if not retrieved_context:
            print("💬 No relevant context found.")
            return "I could not find any relevant information."

        print(f"✅ Retrieved {len(retrieved_context)} documents.")
        for i, doc in enumerate(retrieved_context):
            print(f"  - Context {i+1} ({doc['entity_type']}, ID: {doc['id']}, Score: {doc['score']:.2f})")

        answer = self.generate_answer(query, retrieved_context, history)
        print(f"\n🤖 Answer: {answer}")
        return answer