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| from dotenv import load_dotenv | |
| load_dotenv() | |
| import streamlit as st | |
| import os | |
| import google.generativeai as genai | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings | |
| from langchain_community.vectorstores import FAISS | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
| def get_text(pdfs): | |
| text = "" | |
| for pdf in pdfs: | |
| pdf_reader = PdfReader(pdf) | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def get_chunks(text): | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000) | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| def get_vectors(text_chunks): | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
| vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) | |
| vector_store.save_local("faiss_index") | |
| def get_conv_chain(): | |
| prompt_template = """ | |
| Answer the question as detailed as possible based on the provided context. If the answer is not in the provided context, simply state, "The answer is not in the context." Do not provide incorrect or misleading information.\n\n | |
| Context:\n {context}?\n | |
| Question: \n{question}\n | |
| Answer: | |
| """ | |
| model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.5) | |
| prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) | |
| chain = load_qa_chain(model, chain_type="stuff", prompt=prompt) | |
| return chain | |
| def user_input(qs): | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
| new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True) | |
| docs = new_db.similarity_search(qs) | |
| chain = get_conv_chain() | |
| res = chain( | |
| {"input_documents": docs, "question": qs}, return_only_outputs=True | |
| ) | |
| print(res) | |
| st.write("Response: ", res["output_text"]) | |
| # Streamlit | |
| def main(): | |
| st.set_page_config(page_title="Chat with Multiple PDF") | |
| st.header("Chat with Multiple PDF using Gemini-Pro") | |
| user_qs = st.text_input("Ask a question from your PDF Files") | |
| if user_qs: | |
| user_input(user_qs) | |
| with st.sidebar: | |
| st.title("Menu: ") | |
| docs = st.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True) | |
| if st.button("Submit & Proceed"): | |
| if docs: | |
| with st.spinner("Please wait a moment..."): | |
| t = get_text(docs) | |
| text_chunks = get_chunks(t) | |
| get_vectors(text_chunks) | |
| st.success("Done!") | |
| else: | |
| st.warning("Please upload at least one PDF file.") | |
| if __name__ == "__main__": | |
| main() |