final_result.py

from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from load_llm import load_llm
from setup_costume_prompt import set_custom_prompt
from loader import load_faiss_db, load_embeddings
from RetrivalQA_chain import retrieval_qa_chain

embeddings = load_embeddings()
if embeddings:
    db = load_faiss_db(embeddings)     
prompt = set_custom_prompt()
llm = load_llm()
qa_chain = retrieval_qa_chain(llm, prompt, db)

def final_result(query):
    try:
        qa_result = qa_chain  # Calls the qa_bot() to get the QA chain
        print("QA bot initialized successfully!")  # Success message
        
        response = qa_result.invoke({'query': query})
        print(f"Response generated successfully: {response}")  # Success message
        
        return response
    
    except Exception as e:
        print(f"Error: {e}")  # Error message in case of failure