app.py

import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
from deep_translator import GoogleTranslator
import pandas as pd
from langchain_groq import ChatGroq
from openai import OpenAI
from langchain.chat_models import ChatOpenAI
# set this key as an environment variable
os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['Key2']
os.environ["OPENAI_API_KEY"] =st.secrets['Key3']
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

###########################################################################################

def get_pdf_text(pdf_docs : list) -> str:
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text
#######################################################################################
def load_file(): 
    loader = TextLoader('d2.txt')
    documents = loader.load()
    return documents 
########################################################################################
def get_text_chunks(text:str) ->list:
    text_splitter = CharacterTextSplitter(
        separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks


def get_vectorstore(text_chunks : list) -> FAISS:
    #model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
    model="paraphrase-distilroberta-base-v1"
    encode_kwargs = {
        "normalize_embeddings": True
    }  # set True to compute cosine similarity
    embeddings = HuggingFaceBgeEmbeddings(
        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
    )
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore


def get_conversation_chain(vectorstore):
    n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
    n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
    n_ctx=2048
    callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
    # Make sure the model path is correct for your system
    llm = LlamaCpp(
    model_path="mostafaamiri/persian-llama-7b-GGUF-Q4",
    n_gpu_layers=n_gpu_layers, n_batch=n_batch,
    callback_manager=callback_manager,
    verbose=True,
    n_ctx=n_ctx)
    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory,
        # retriever_kwargs={"k": 1},
    )
    return conversation_chain



def handle_userinput(user_question:str):
    response = st.session_state.conversation({"question": user_question})
    st.session_state.chat_history = response["chat_history"]

    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            text2=message.content 
            translator = GoogleTranslator(source='english', target='persian')
            result = translator.translate(text2)
            st.write("سوال کاربر: "+result)
        else:
            text1=message.content 
            translator = GoogleTranslator(source='english', target='persian')
            result = translator.translate(text1)
            st.write("پاسخ ربات: "+result)

#############################################################################################################
def read_pdf_pr_en(pdf_file_path):
  from deep_translator import GoogleTranslator
  import PyPDF2
  # مسیر فایل PDF را تعیین کنید
  #pdf_file_path = '/content/d2en.pdf'
  # باز کردن فایل PDF
  with open(pdf_file_path, 'rb') as pdf_file:
    pdf_reader = PyPDF2.PdfReader(pdf_file)
    # خواندن محتوای صفحه‌ها
    full_text = ''
    for page in pdf_reader.pages:
        page_pdf=page.extract_text()
        translator = GoogleTranslator(source='persian', target='english')
        result = translator.translate(page_pdf)
        full_text +=result
    st.write(full_text)
    return(full_text)
#################################################################################################################
def get_pdf_text(pdf_docs): 
    text = "" 
    for pdf in pdf_docs: 
        pdf_reader = PdfReader(pdf) 
    for page in pdf_reader.pages: 
        txt_page=page.extract_text() 
        text += txt_page
    return text
#######################################################################################################################
def upload_xls():
    st.title("آپلود و نمایش فایل اکسل")
    uploaded_file = st.file_uploader("لطفاً فایل اکسل خود را آپلود کنید", type=["xlsx", "xls"])
    if uploaded_file is not None:
        df = pd.read_excel(uploaded_file)
        st.write("دیتا فریم مربوط به فایل اکسل:")
        st.write(df)
    return df

################################################################################################################
def sentences_f(sentence,df2):
  words = sentence.split()
  df1 = pd.DataFrame(words, columns=['کلمات'])
  df1['معادل'] = ''
  for i, word in df1['کلمات'].items():
    match = df2[df2['کلمات'] == word]
    if not match.empty:
        df1.at[i, 'معادل'] = match['معادل'].values[0]
  df1['معادل'] = df1.apply(lambda row: row['کلمات'] if row['معادل'] == '' else row['معادل'], axis=1)
  translated_sentence = ' '.join(df1['معادل'].tolist())
  return translated_sentence
####################################################################################################################    

####################################################################################################################
def main():
    st.set_page_config(
        page_title="Chat Bot PDFs",
        page_icon=":books:",
    )

    #st.markdown("# Chat with a Bot")
    #st.markdown("This bot tries to answer questions about multiple PDFs. Let the processing of the PDF finish before adding your question. 🙏🏾")

    st.write(css, unsafe_allow_html=True)
    #df2=upload_xls()

    
    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    
    st.header("Chat Bot PDFs :books:")
    user_question = st.text_input("Ask a question about your documents:")
    #user_question2=sentences_f(sentence=user_question1,df2=df2)
    #translator = GoogleTranslator(source='persian', target='english')
    #user_question = translator.translate(user_question2)
    if st.button("Answer"):
            with st.spinner("Answering"):
              handle_userinput(user_question)
          
    if st.button("CLEAR"):
            with st.spinner("CLEARING"):
              st.cache_data.clear()
         
    
    with st.sidebar:
        st.subheader("Your documents")
        pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)

        if st.button("Process"):
            with st.spinner("Processing"):
                # get pdf text
                raw_text = get_pdf_text(pdf_docs)

                # get the text chunks
                text_chunks = get_text_chunks(raw_text)

                # create vector store
                vectorstore = get_vectorstore(text_chunks)

                # create conversation chain
                st.session_state.conversation = get_conversation_chain(vectorstore)
                
                #compelete build model
                st.write("compelete build model")


if __name__ == "__main__":
    main()