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| import streamlit as st | |
| from PyPDF2 import PdfReader #library to read pdf files | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter#library to split pdf files | |
| import os | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings #to embed the text | |
| import google.generativeai as genai | |
| from langchain.vectorstores import FAISS #for vector embeddings | |
| from langchain_google_genai import ChatGoogleGenerativeAI # | |
| from langchain.chains.question_answering import load_qa_chain #to chain the prompts | |
| from langchain.prompts import PromptTemplate #to create prompt templates | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| genai.configure(api_key = os.getenv("GOOGLE_API_KEY")) | |
| def get_pdf_text(pdf_docs): | |
| text = "" | |
| # iterate over all pdf files uploaded | |
| for pdf in pdf_docs: | |
| pdf_reader = PdfReader(pdf) | |
| # iterate over all pages in a pdf | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def get_text_chunks(text): | |
| # create an object of RecursiveCharacterTextSplitter with specific chunk size and overlap size | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size = 10000, chunk_overlap = 1000) | |
| # now split the text we have using object created | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| def get_vector_store(text_chunks): | |
| embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001") # google embeddings | |
| vector_store = FAISS.from_texts(text_chunks,embeddings) # use the embedding object on the splitted text of pdf docs | |
| vector_store.save_local("faiss_index") # save the embeddings in local | |
| def get_conversation_chain(): | |
| # define the prompt | |
| 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-pro", temperatue = 0.3) # create object of gemini-pro | |
| prompt = PromptTemplate(template = prompt_template, input_variables= ["context","question"]) | |
| chain = load_qa_chain(model,chain_type="stuff",prompt = prompt) | |
| return chain | |
| def user_input(user_question): | |
| # user_question is the input question | |
| embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001") | |
| # load the local faiss db | |
| new_db = FAISS.load_local("faiss_index", embeddings) | |
| # using similarity search, get the answer based on the input | |
| docs = new_db.similarity_search(user_question) | |
| chain = get_conversation_chain() | |
| response = chain( | |
| {"input_documents":docs, "question": user_question} | |
| , return_only_outputs=True) | |
| print(response) | |
| st.write("Reply: ", response["output_text"]) | |
| def main(): | |
| st.set_page_config("Chat PDF") | |
| st.header("Chat with PDF using Gemini") | |
| user_question = st.text_input("Ask a Question:") | |
| if user_question: | |
| user_input(user_question) | |
| with st.sidebar: | |
| st.title("Menu:") | |
| pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", 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) | |
| get_vector_store(text_chunks) | |
| st.success("Done") | |
| if __name__ == "__main__": | |
| main() |