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| import streamlit as st | |
| from dotenv import load_dotenv | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.llms import HuggingFaceHub | |
| from html_template import css, bot_template, user_template | |
| def get_pdf_text(pdf_docs): | |
| text = '' | |
| for pdf in pdf_docs: | |
| reader = PdfReader(pdf) | |
| for page in reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def get_text_chuks(raw_text): | |
| text_splitter = CharacterTextSplitter( | |
| separator = '\n', | |
| chunk_size = 1000, | |
| chunk_overlap = 200, | |
| length_function = len | |
| ) | |
| chunks = text_splitter.split_text(raw_text) | |
| return chunks | |
| def get_vector_store(text_chunks): | |
| embeddings = OpenAIEmbeddings() | |
| # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") | |
| vector_store = FAISS.from_texts(text_chunks, embeddings) | |
| return vector_store | |
| def get_conversation_chain(vectorstore): | |
| llm = ChatOpenAI(temperature=0.2) | |
| # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.2, "max_length":512}) | |
| 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_user_question(user_question): | |
| 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: | |
| 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 main(): | |
| load_dotenv() | |
| st.set_page_config(page_title='Chat with your PDFs', page_icon='📂', layout='wide') | |
| st.header('Chat with multiple PDFs :books:') | |
| # st.write(bot_template.replace('{{MSG}}', 'hello user'), unsafe_allow_html=True) | |
| # st.write(user_template.replace('{{MSG}}', 'hello bot'), unsafe_allow_html=True) | |
| 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 | |
| with st.sidebar: | |
| st.subheader('Document') | |
| 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_chuks(raw_text) | |
| # create vector store | |
| vectorstore = get_vector_store(text_chunks) | |
| # create conversation chain | |
| st.session_state.conversation = get_conversation_chain(vectorstore) | |
| user_question = st.text_input('Ask a question about your pdf') | |
| if user_question: | |
| handle_user_question(user_question) | |
| if __name__ == '__main__': | |
| main() | |