from dotenv import load_dotenv
load_dotenv()
import streamlit as st
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
import os



def main():
    load_dotenv()
    st.set_page_config(page_title="Chat Text")
    st.header("Chat Text 💬")
    
    # Dosyayı oku
    file_path = 'shipping.txt'
    with open(file_path, 'r', encoding='utf-8') as file:
        text = file.read()
        
    # Metni parçalara ayır
    char_text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000,
                                               chunk_overlap=200, length_function=len)
    text_chunks = char_text_splitter.split_text(text)
    
    embeddings = OpenAIEmbeddings(
        openai_api_key=os.getenv('OPENAI_API_KEY')
    )
    docsearch = FAISS.from_texts(text_chunks, embeddings) 
    llm = OpenAI() 
    chain = load_qa_chain(llm, chain_type="stuff")
    
    # Kullanıcıdan soru al
    query = st.text_input("Type your question:")
    if query:
        docs = docsearch.similarity_search(query)
        response = chain.run(input_documents=docs, question=query)
           
        st.write(response)

if __name__ == '__main__':
    main()