app.py

import streamlit as st
import requests
import subprocess
import atexit
import os
import signal
import os
import PyPDF2
from docx import Document
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
import pickle
from datetime import datetime
import io
from dotenv import load_dotenv

log_data = [] 


class User:
    def __init__(self, username):
        self.username = username
        self.llm = "gemini-pro"
        self.embedder = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"


def upload_documents(user: User, files) -> tuple[str, int]:
    text = _extract_text_from_document(files)
    chunks = _chunk_text(text)
    status_code = _create_embeddings_and_save(user, chunks)
    if status_code == 200:
        return "Document uploaded successfully.", 200
    else:
        return "Failed to upload document.", 500


def _extract_text_from_document(files) -> str:
    text = ""
    for file in files:
        file_name = file.name
        file_extension = os.path.splitext(file_name)[1]
        if file_extension == '.txt':
            text += file.read().decode('utf-8')
        elif file_extension == '.pdf':
            pdf_reader = PyPDF2.PdfReader(io.BytesIO(file.read()))
            for page_number in range(len(pdf_reader.pages)):
                page = pdf_reader.pages[page_number]
                text += page.extract_text()
        elif file_extension == '.docx':
            doc = Document(io.BytesIO(file.read()))
            for paragraph in doc.paragraphs:
                text += paragraph.text + "\n"
    return text


def _chunk_text(text: str) -> list[str]:
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=512,
        chunk_overlap=10,
        length_function=len
    )
    return text_splitter.split_text(text)


def _create_embeddings_and_save(user: User, chunks: any) -> int:
    embeddings = HuggingFaceEmbeddings(model_name=user.embedder)
    vector_store = FAISS.from_texts(chunks, embeddings, metadatas=[{"source": f"{user.username}:{i}"} for i in range(len(chunks))])
    st.session_state.vector_store = vector_store
    return 200


def ask_question(user: User, question: str, api_key: str, vector_store : FAISS) -> tuple[str, int]:


    if api_key:
        os.environ["GOOGLE_API_KEY"] = api_key
    else:
        is_loaded = load_dotenv()
        if not is_loaded:
            return "API key not found.", 400

    llm = ChatGoogleGenerativeAI(model=user.llm, temperature=0, max_output_tokens=256, top_k=40, top_p=0.8)
    docs = vector_store.similarity_search(question)
    retrieved_chunks = docs[0].page_content + docs[1].page_content + docs[2].page_content
    system_message = "Figure out the answer of the question by the given information pieces. ALWAYS answer with the language of the question."
    prompt = system_message + "Question: " + question + " Context: " + retrieved_chunks
    try:
        response = llm.invoke(prompt)
    except Exception:
        return "Wrong API key.", 400

    answer = response.content + "  **<Most Related Chunk>**  " + retrieved_chunks
    _log(user, question, system_message, retrieved_chunks, response.content)
    return answer, 200


def _log(user: User, question: str, system_message: str, retrieved_chunks: str, answer: str):
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    log_message = (
        f"{timestamp}, Username: {user.username}, Question: {question}, "
        f"LLM: {user.llm}, Embedder: {user.embedder}, System Message: {system_message}, "
        f"Retrieved Texts: {retrieved_chunks}, Answer: {answer}\n"
    )
    log_data.append(log_message)


def main():
    st.title("Free Multilingual RAG")

    tabs = ["Upload Document", "Ask Question"]
    active_tab = st.radio("Upload documents first, ask questions later:", tabs)

    if active_tab == "Upload Document":
        upload_document()
    elif active_tab == "Ask Question":
        ask_question_ui(st.session_state.vector_store)


def upload_document():
    st.write("Several files can be uploaded, each upload crushes the old one. Depending on the number and size of files, the upload process may take a long time.")

    username = st.text_input("Enter a username (just something that represents you):")
    uploaded_files = st.file_uploader("Upload your documents (for now it only works with files that have .txt, .pdf or .docx extension):", accept_multiple_files=True)

    if uploaded_files and username:
        st.write("Number of uploaded files:", len(uploaded_files))

        for uploaded_file in uploaded_files:
            file_details = {"FileName": uploaded_file.name, "FileType": uploaded_file.type, "FileSize": uploaded_file.size}
            st.write(file_details)

        user = User(username=username)
        response, status_code = upload_documents(user, uploaded_files)

        if status_code == 200:
            st.success(response)
        else:
            st.error("Error:", response)


def ask_question_ui(vector_store : FAISS):
    username = st.text_input("Enter a username (just something that represents you):")
    api_key = st.text_input("Add your Google API key. It is free. Key acquisition video: [https://www.youtube.com/watch?v=brCkpzAD0gc]: (If you do not trust you can download and use the app in your local too)", type="password")
    question = st.text_area("Enter the question you want to ask in your document (the more detailed your question, the more accurate an answer you will get):")

    if st.button("Ask"):
        if not question:
            st.warning("Please enter a question.")
        elif not username:
            st.warning("Please enter a username.")
        else:
            user = User(username=username)
            answer, status_code = ask_question(user, question, api_key, vector_store)
            
            if status_code == 200:
                st.success("Answer: " + answer)
            else:
                st.error("Error: " + answer)

if __name__ == "__main__":
    main()