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| 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 | |
| # set this key as an environment variable | |
| os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token'] | |
| ########################################################################################### | |
| 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" | |
| 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:FAISS) -> ConversationalRetrievalChain: | |
| # llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") | |
| llm = HuggingFaceHub( | |
| repo_id="mistralai/Mistral-7B-Instruct-v0.2", | |
| #repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| #repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF" | |
| model_kwargs={"temperature": 0.9, "max_length": 2048}, | |
| ) | |
| memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| conversation_chain = ConversationalRetrievalChain.from_llm( | |
| llm=llm, retriever=vectorstore.as_retriever(), memory=memory | |
| ) | |
| 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 | |
| ################################33333333333333333333333333333333333333333333333333333333 | |
| 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) | |
| 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_question1 = st.text_input("Ask a question about your documents:") | |
| translator = GoogleTranslator(source='persian', target='english') | |
| user_question = translator.translate(user_question1) | |
| 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() | |