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| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| import os | |
| from langchain.vectorstores import FAISS | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ConversationBufferMemory | |
| # import google.generativeai as palm | |
| # from langchain.embeddings import GooglePalmEmbeddings | |
| # from langchain.llms import GooglePalm | |
| # from langchain_google_genai import GoogleGenerativeAI | |
| from langchain.embeddings import HuggingFaceInstructEmbeddings | |
| # from langchain.llms import HuggingFaceHub | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| os.getenv('HUGGINGFACEHUB_API_TOKEN') | |
| # os.getenv('GOOGLE_API_KEY') | |
| def get_pdf_text(pdf_docs): | |
| text="" | |
| for pdf in pdf_docs: | |
| pdf_reader= PdfReader(pdf) | |
| for page in pdf_reader.pages: | |
| text+= page.extract_text() | |
| return text | |
| def get_text_chunks(text): | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20) | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| def get_vector_store(text_chunks): | |
| embeddings = HuggingFaceInstructEmbeddings(model_name="BAAI/bge-large-zh-v1.5") | |
| # embeddings = GooglePalmEmbeddings() | |
| vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) | |
| return vector_store | |
| def get_conversational_chain(vector_store): | |
| # llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.5, "max_length":512}) | |
| # llm = GoogleGenerativeAI(model="models/text-bison-001", temperature=0.5) | |
| llm = HuggingFaceEndpoint( | |
| repo_id="HuggingFaceH4/zephyr-7b-beta", | |
| task="text-generation", | |
| max_new_tokens=512) | |
| memory = ConversationBufferMemory(memory_key = "chat_history", return_messages=True) | |
| conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vector_store.as_retriever(), memory=memory) | |
| return conversation_chain | |
| def user_input(user_question): | |
| response = st.session_state.conversation({'question': user_question}) | |
| st.session_state.chatHistory = response['chat_history'] | |
| for i, message in enumerate(st.session_state.chatHistory): | |
| if i%2 == 0: | |
| st.write("Human: ", message.content) | |
| else: | |
| st.write("Bot: ", message.content) | |
| def main(): | |
| st.set_page_config("Chat with Multiple PDFs") | |
| st.header("Chat with Multiple PDF 💬") | |
| user_question = st.text_input("Ask a Question from the PDF Files") | |
| if "conversation" not in st.session_state: | |
| st.session_state.conversation = None | |
| if "chatHistory" not in st.session_state: | |
| st.session_state.chatHistory = None | |
| if user_question: | |
| user_input(user_question) | |
| with st.sidebar: | |
| st.title("Settings") | |
| st.subheader("Upload your Documents") | |
| pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Process Button", accept_multiple_files=True) | |
| if st.button("Process"): | |
| with st.spinner("Processing"): | |
| raw_text = get_pdf_text(pdf_docs) | |
| text_chunks = get_text_chunks(raw_text) | |
| vector_store = get_vector_store(text_chunks) | |
| st.session_state.conversation = get_conversational_chain(vector_store) | |
| st.success("Done") | |
| if __name__ == "__main__": | |
| main() | |