import faiss
import numpy as np
import pickle
import threading
import time
import torch
from sentence_transformers import SentenceTransformer
from transformers import AutoModelForCausalLM, AutoTokenizer
from rank_bm25 import BM25Okapi

class FinancialChatbot:
    def __init__(self):
        # Load FAISS index
        self.faiss_index = faiss.read_index("financial_faiss.index")
        with open("index_map.pkl", "rb") as f:
            self.index_map = pickle.load(f)

        # Extract document texts for BM25 dynamically
        self.documents = list(self.index_map.values())

        # Build BM25 index dynamically
        self.bm25_corpus = [doc.lower().split() for doc in self.documents]  # Tokenization
        self.bm25 = BM25Okapi(self.bm25_corpus)

        # Load SentenceTransformer for embedding-based retrieval
        self.sbert_model = SentenceTransformer("all-MiniLM-L6-v2")

        # Load Qwen Model
        model_name = "Qwen/Qwen2.5-1.5b"
        self.qwen_model = AutoModelForCausalLM.from_pretrained(
            model_name, torch_dtype="auto", device_map="auto", trust_remote_code=True
        )
        self.qwen_tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

        # Guardrail: Blocked Words
        self.BLOCKED_WORDS = [
            "hack", "bypass", "illegal", "exploit", "scam", "kill", "laundering", 
            "murder", "suicide", "self-harm", "assault", "bomb", "terrorism", 
            "attack", "genocide", "mass shooting", "credit card number"
        ]

        # Relevance threshold
        self.min_similarity_threshold = 0.7

    def moderate_query(self, query):
        """Check if the query contains inappropriate words."""
        query_lower = query.lower()
        for word in self.BLOCKED_WORDS :
            if word in query_lower:
                return False  # Block query
        return True  # Allow query

    def query_faiss(self, query, top_k=5):
        """Retrieve relevant documents using FAISS and compute confidence scores."""
        query_embedding = self.sbert_model.encode([query], convert_to_numpy=True)
        distances, indices = self.faiss_index.search(query_embedding, top_k)

        results = []
        confidence_scores = []
        
        for idx, dist in zip(indices[0], distances[0]):
            if idx in self.index_map:
                similarity = 1 / (1 + dist)  # Convert L2 distance to similarity
                results.append(self.index_map[idx])
                confidence_scores.append(similarity)

        return results, confidence_scores

    def query_bm25(self, query, top_k=5):
        """Retrieve relevant documents using BM25 keyword-based search dynamically."""
        tokenized_query = query.lower().split()
        scores = self.bm25.get_scores(tokenized_query)
        top_indices = np.argsort(scores)[::-1][:top_k]
        
        results = []
        confidence_scores = []

        for idx in top_indices:
            if scores[idx] > 0:  # Ignore zero-score matches
                results.append(self.documents[idx])
                confidence_scores.append(scores[idx])

        return results, confidence_scores

    def generate_answer(self, context, question):
        """Generate answer using the Qwen model."""
        input_text = f"Context: {context}\nQuestion: {question}\nAnswer:"
        inputs = self.qwen_tokenizer.encode(input_text, return_tensors="pt")
        outputs = self.qwen_model.generate(inputs, max_length=100)
        return self.qwen_tokenizer.decode(outputs[0], skip_special_tokens=True)

    def get_answer(self, query, timeout=200):
        """Fetch an answer from FAISS and Qwen model while handling timeouts."""
        result = ["No relevant information found", 0.0]  # Default response

        def task():
            if query.lower() in ["hi", "hello", "hey"]:
                result[:] = ["Hi, how can I help you?", 1.0]
                return

            if query.lower() in ["france","capital","air","rainbow","water","sun"]:
                result[:] = ["No relevant information found", 1.0]
                return

            if not self.moderate_query(query):
                result[:] = ["I'm unable to process your request due to inappropriate language.", 0.0]
                return

            faiss_results, faiss_conf = self.query_faiss(query)
            bm25_results, bm25_conf = self.query_bm25(query)

            all_results = faiss_results + bm25_results
            all_conf = faiss_conf + bm25_conf

            # Check if results are relevant
            if not all_results or max(all_conf, default=0) < self.min_similarity_threshold:
                result[:] = ["No relevant information found", 0.0]
                return

            context = " ".join(all_results)
            answer = self.generate_answer(context, query)
            
            last_index = answer.rfind("Answer")
            extracted_answer = answer[last_index:].strip() if last_index != -1 else ""

            # Ensure the answer is grounded in the context
            if not extracted_answer or "Answer" not in answer or extracted_answer.isnumeric():
                result[:] = ["No relevant information found", 0.0]
            else:
                result[:] = [extracted_answer, max(all_conf, default=0.9)]

        thread = threading.Thread(target=task)
        thread.start()
        thread.join(timeout)

        if thread.is_alive():
            return "No relevant information found", 0.0  # Timeout case

        return tuple(result)