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import streamlit as st | |
from dotenv import load_dotenv | |
from PyPDF2 import PdfReader | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain_community.embeddings import HuggingFaceInstructEmbeddings | |
from langchain_community.vectorstores import FAISS | |
from langchain_community.chat_models import ChatOpenAI | |
from langchain.llms import HuggingFaceHub | |
from langchain import hub | |
from langchain_core.output_parsers import StrOutputParser | |
from langchain_core.runnables import RunnablePassthrough | |
import os | |
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 = CharacterTextSplitter( | |
separator="\n", | |
chunk_size=500, # the character length of the chunck | |
chunk_overlap=100, # the character length of the overlap between chuncks | |
length_function=len # the length function - in this case, character length (aka the python len() fn.) | |
) | |
chunks = text_splitter.split_text(text) | |
return chunks | |
def get_vectorstore(text_chunks): | |
model_name = "hkunlp/instructor-xl" | |
hf = HuggingFaceInstructEmbeddings(model_name=model_name) | |
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=hf) | |
return vectorstore | |
def get_conversation_chain(vectorstore): | |
llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.2",model_kwargs={"Temperature": 0.5, "MaxTokens": 1024}) | |
retriever=vectorstore.as_retriever() | |
prompt = hub.pull("rlm/rag-prompt") | |
# Chain | |
rag_chain = ( | |
{"context": retriever, "question": RunnablePassthrough()} | |
| prompt | |
| llm | |
) | |
response = rag_chain.invoke("A partir de documents PDF, concernant la transition écologique en France, proposer un plan de transition en fonction de la marque").split("\nAnswer:")[-1] | |
return response | |
def rag_pdf(): | |
load_dotenv() | |
st.header("Utiliser l’IA pour générer un plan RSE simplifié") | |
if "conversation" not in st.session_state: | |
st.session_state.conversation = None | |
with st.sidebar: | |
st.subheader("INFOS SUR LA MARQUE") | |
pdf_docs = st.file_uploader("Upload les documents concerant la marque et clique sur process", type="pdf",accept_multiple_files=True) | |
if st.button("Process"): | |
with st.spinner("Processing..."):#loading bar to enhance user experience | |
#get pdf text in raw format | |
raw_text = get_pdf_text(pdf_docs) | |
#get text chunks | |
text_chunks = get_text_chunks(raw_text) | |
#create vectorstore | |
vectorstore = get_vectorstore(text_chunks) | |
#create conversation chain | |
st.session_state.conversation = get_conversation_chain(vectorstore) | |
st.write(st.session_state.conversation) |