import streamlit as st 
from langchain.vectorstores import faiss
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.embeddings import SentenceTransformerEmbeddings
from tempfile import NamedTemporaryFile
import os
import shutil

try:
    shutil.rmtree("tempDir")
except :
    pass
try:
    os.mkdir("tempDir")
except:
    pass
def save_uploadedfile(uploadedfile):

    with open(os.path.join("tempDir",uploadedfile.name),"wb") as f:
         f.write(uploadedfile.getbuffer())
    return st.success("Saved File:{} to tempDir".format(uploadedfile.name))


def main():
    st.set_page_config(page_title="chet with unipv")
    st.text_input("fai una domanda al tuo professore ")
    with st.sidebar:
        st.subheader("Your_faiss_index")
        documents=st.file_uploader("upload your faiss index here ",accept_multiple_files=True)
        if st.button("Procedi"):
            for document in documents:
                save_uploadedfile(document)
            #with st.spinner("sto processando i tuoi dati"):
            print(documents)
            query="chi è matteo salvini?"
            embeddings= HuggingFaceInstructEmbeddings(model_name="thenlper/gte-base")
            new_db = FAISS.load_local("tempDir", embeddings)
            docs = new_db.similarity_search(query)
            print(docs)
if __name__=="__main__":
    main()