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| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter | |
| import os, getpass | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings | |
| import google.generativeai as genai | |
| from langchain.vectorstores import FAISS | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ConversationBufferMemory | |
| os.environ["GOOGLE_API_KEY"]=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): | |
| #RecursiveCharacterTextSplitter CharacterTextSplitter separator="\n", | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=1000, length_function=len)# | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| def get_vector_store(text_chunks): | |
| embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001") | |
| vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) | |
| return vector_store | |
| def get_conversational_chain(Fvs): | |
| prompt_template = """ | |
| Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in | |
| provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n | |
| Context:\n {context}?\n | |
| Question: \n{question}\n | |
| Answer: | |
| """ | |
| model = ChatGoogleGenerativeAI(model="gemini-2.5-pro",temperature=0.3) | |
| prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"]) | |
| memory = ConversationBufferMemory(memory_key = "chat_history", return_messages=True) | |
| chain = ConversationalRetrievalChain.from_llm(llm=model,retriever=Fvs.as_retriever(), memory=memory) | |
| return 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("👨🔬👨🔬: ", message.content) | |
| else: | |
| st.write("🤖🤖: ", message.content) | |
| ## streamlit app | |
| st.set_page_config("Chat With Multiple PDF") | |
| st.header("Chat with Multiple PDF :books:") | |
| user_question = st.text_input("Ask a Question from the PDF Files") | |
| submit=st.button("Ask the question") | |
| ## If ask button is clicked | |
| if submit: | |
| 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("Menu:") | |
| pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True) | |
| if st.button("Submit & Process"): | |
| with st.spinner("Processing..."): | |
| raw_text = get_pdf_text(pdf_docs) | |
| text_chunks = get_text_chunks(raw_text) | |
| Fvs = get_vector_store(text_chunks) | |
| st.session_state.conversation = get_conversational_chain(Fvs) | |
| st.success("Done") | |
| if st.button("Clear Chat Window", use_container_width=True, type="primary"): | |
| st.session_state.history = [] | |
| st.rerun() | |
| footer = """ | |
| --- | |
| #### Made By [Surat Banerjee](https://www.linkedin.com/in/surat-banerjee/) | |
| """ | |
| st.markdown(footer, unsafe_allow_html=True) | |