Spaces:
Running
Running
File size: 2,928 Bytes
fb86b93 e58c122 fb86b93 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
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) |