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()