| |
| import streamlit as st |
| from PyPDF2 import PdfReader |
| from langchain.text_splitter import RecursiveCharacterTextSplitter |
| import os |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings |
| import google.generativeai as genai |
| from langchain_community.vectorstores import FAISS |
| from langchain_google_genai import ChatGoogleGenerativeAI |
| from langchain.chains.question_answering import load_qa_chain |
| from langchain.prompts import PromptTemplate |
| from dotenv import load_dotenv |
| from htmlTemplates import css, bot_template, user_template |
|
|
| |
| load_dotenv() |
| os.getenv("GOOGLE_API_KEY") |
| genai.configure(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): |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000) |
| 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) |
| FAISS.deserialize_from_bytes(vector_store, embeddings, allow_dangerous_deserialization=True) |
| vector_store.save_local("faiss_index") |
|
|
| |
| def get_conversational_chain(): |
| prompt_template = """ |
| Answer the question concisely, focusing on the most relevant and important details from the PDF context. |
| Refrain from mentioning any mathematical equations, even if they are present in provided context. |
| Focus on the textual information available. Please provide direct quotations or references from PDF |
| to back up your response. If the answer is not found within the PDF, |
| please state "answer is not available in the context."\n\n |
| Context:\n {context}?\n |
| Question: \n{question}\n |
| Example response format: |
| Overview: |
| (brief summary or introduction) |
| Key points: |
| (point 1: paragraph for key details) |
| (point 2: paragraph for key details) |
| ... |
| Use a mix of paragraphs and points to effectively convey the information. |
| """ |
|
|
| |
| |
| model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.2) |
|
|
| 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): |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") |
|
|
| new_db = FAISS.load_local("faiss_index", embeddings) |
| docs = new_db.similarity_search(user_question) |
|
|
| chain = get_conversational_chain() |
|
|
| response = chain.invoke( |
| {"input_documents": docs, "question": user_question}, |
| return_only_outputs=True |
| ) |
|
|
| |
| st.write("question: ", user_question) |
| st.write(bot_template.replace("{{MSG}}", response["output_text"]),unsafe_allow_html=True) |
|
|
| |
| def main(): |
| st.set_page_config(page_title="Chat with PDFs", page_icon="") |
| st.header("Chat with multiple PDFs using AI 💬") |
|
|
| user_question = st.text_input("Ask a Question from PDF file(s)") |
|
|
| 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() |
