app.py

import streamlit as st
import pickle
import os
import torch
from tqdm.auto import tqdm
from langchain.text_splitter import RecursiveCharacterTextSplitter


# from langchain.vectorstores import Chroma
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceInstructEmbeddings


from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA



st.set_page_config(
    page_title = 'aitGPT',
    page_icon = '✅')


st.markdown("# Hello")


@st.cache_data
def load_scraped_web_info():
    with open("ait-web-document", "rb") as fp:
        ait_web_documents = pickle.load(fp)
        
        
    text_splitter = RecursiveCharacterTextSplitter(
        # Set a really small chunk size, just to show.
        chunk_size = 500,
        chunk_overlap  = 100,
        length_function = len,
    )

    chunked_text = text_splitter.create_documents([doc for doc in tqdm(ait_web_documents)])


    st.markdown(f"Number of Documents: {len(ait_web_documents)}")
    st.markdown(f"Number of chunked texts: {len(chunked_text)}")





@st.cache_resource
def load_embedding_model():
    embedding_model = HuggingFaceInstructEmbeddings(model_name='hkunlp/instructor-base',
                                                model_kwargs = {'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu')})
    return embedding_model

@st.cache_data
def load_faiss_index():
    vector_database = FAISS.load_local("faiss_index", embedding_model)
    return vector_database

@st.cache_resource
def load_llm_model():
    # llm = HuggingFacePipeline.from_model_id(model_id= 'lmsys/fastchat-t5-3b-v1.0', 
    #                                         task= 'text2text-generation',
    #                                         model_kwargs={ "device_map": "auto",
    #                                                     "load_in_8bit": True,"max_length": 256, "temperature": 0,
    #                                                     "repetition_penalty": 1.5})
    
    
    llm = HuggingFacePipeline.from_model_id(model_id= 'lmsys/fastchat-t5-3b-v1.0', 
                                        task= 'text2text-generation',
                                        
                                        model_kwargs={ "max_length": 256, "temperature": 0,
                                                      "torch_dtype":torch.float32,
                                                    "repetition_penalty": 1.3})


    return llm


def load_retriever(llm, db):
    qa_retriever = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff",
                            retriever=db.as_retriever())

    return qa_retriever

#--------------



load_scraped_web_info()
embedding_model = load_embedding_model()
vector_database = load_faiss_index()
llm_model = load_llm_model()
qa_retriever = load_retriever(llm= llm_model, db= vector_database)


print("all load done")



query_input = st.text_input(label= 'your question')


def retrieve_document(query_input):
    related_doc = vector_database.similarity_search(query_input)
    return related_doc

def retrieve_answer(query_input):
    answer = qa_retriever.run(query_input)
    return answer

output_1 = st.text_area(label = "Here is the relevant documents",
                      value = retrieve_document(query_input))


output_2 = st.text_area(label = "Here is the answer",
                      value = retrieve_answer(query_input))



# faiss_retriever = vector_database.as_retriever()
# print("Succesfully had FAISS as retriever")