import streamlit as st from dotenv import load_dotenv from PyPDF2 import PdfReader from langchain.text_splitter import CharacterTextSplitter from langchain.llms import CTransformers # For loading transformer models. from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings from langchain.vectorstores import FAISS from langchain.embeddings import HuggingFaceEmbeddings # General embeddings from HuggingFace models. 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 def get_pdf_text(pdf_docs): text = "" pdf_reader = PdfReader(pdf_docs) for page in pdf_reader.pages: text += page.extract_text() return text def get_text_chunks(text): text_splitter = CharacterTextSplitter( separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len ) chunks = text_splitter.split_text(text) return chunks def get_vectorstore(text_chunks): # Load the desired embeddings model. embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'}) # embeddings = OpenAIEmbeddings() # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) return vectorstore def get_conversation_chain(vectorstore): # llm = ChatOpenAI() # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512}) llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q2_K.bin", model_type="llama") 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): response = st.session_state.conversation({'query': user_question}) st.session_state.chat_history = response['chat_history'] for i, message in enumerate(st.session_state.chat_history): if i % 2 == 0: st.write(user_template.replace( "{{MSG}}", message.content), unsafe_allow_html=True) else: st.write(bot_template.replace( "{{MSG}}", message.content), unsafe_allow_html=True) def get_text_file(docs): text = f.read() return text def get_csv_file(docs): import pandas as pd text = '' data = pd.read_csv(docs) for index, row in data.iterrows(): item_name = row[0] row_text = item_name for col_name in data.columns[1:]: row_text += '{} is {} '.format(col_name, row[col_name]) text += row_text + '\n' return text def get_json_file(docs): import json text = '' with open(docs, 'r') as f: json_data = json.load(f) for f_key, f_value in json_data.items(): for s_value in f_value: text += str(f_key) + str(s_value) text += '\n' #print(text) return text def get_hwp_file(docs): pass def get_docs_file(docs): pass def main(): load_dotenv() st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:") 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 with multiple PDFs :books:") user_question = st.text_input("Ask a question about your documents:") if user_question: handle_userinput(user_question) with st.sidebar: st.subheader("Your documents") 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 = "" for file in docs: if file.type == 'text/plain': #file is .txt raw_text += get_text_file(file) elif file.type == 'application/octet-stream': #file is .pdf raw_text += get_pdf_text(file) elif file.type == 'text/csv': #file is .csv raw_text += get_csv_file(file) elif file.type == 'application/json': # file is .json raw_text += get_json_file(file) elif file.type == 'application/x-hwp': # file is .hwp raw_text += get_hwp_file(file) elif file.type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document': # file is .docs raw_text += get_docs_file(file) # 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) if __name__ == '__main__': main()