Spaces:
Running
Running
| 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 | |
| import shutil | |
| import argparse | |
| PDF_PATH=os.path.join(os.path.dirname(__file__), "docs") | |
| def load_pdfs(): | |
| faiss_index_path = os.path.join(os.path.dirname(__file__), "faiss_index") | |
| if os.path.exists(faiss_index_path): | |
| return | |
| pdfs = [f for f in os.listdir(PDF_PATH) if os.path.isfile(os.path.join(PDF_PATH, f))] | |
| text="" | |
| for pdf in pdfs: | |
| print("process PDF: %s..." % pdf) | |
| pdf_reader= PdfReader(os.path.join(PDF_PATH, pdf)) | |
| for page in pdf_reader.pages: | |
| text+= page.extract_text() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000) | |
| text_chunks = text_splitter.split_text(text) | |
| embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001") | |
| vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) | |
| vector_store.save_local("faiss_index") | |
| return text | |
| def get_conversational_chain(): | |
| 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", | |
| temperature=0.3) | |
| 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( | |
| {"input_documents":docs, "question": user_question} | |
| , return_only_outputs=True) | |
| print(response) | |
| st.write("Reply: ", response["output_text"]) | |
| def main(): | |
| load_pdfs() | |
| st.set_page_config("TDX Doctor") | |
| st.header("Please ask questions related to TDX or UEFI") | |
| st.markdown("Ask a question like following styles:") | |
| st.markdown("- please describe EFI PEI Core in 200 words.") | |
| st.markdown("- please describe intel tdx in 200 words.") | |
| st.markdown("- please explain SEAMCALL in 200 words.") | |
| user_question = st.text_input("input", label_visibility="hidden") | |
| if user_question: | |
| user_input(user_question) | |
| if __name__ == "__main__": | |
| main() |