import tempfile
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
import os
import pickle
from datetime import datetime
from backend.generate_metadata import generate_metadata, ingest
MODEL_NAME = "mixtral"
css = '''
<style>
.chat-message {
padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
}
.chat-message.user {
background-color: #2b313e
}
.chat-message.bot {
background-color: #475063
}
.chat-message .avatar {
width: 20%;
}
.chat-message .avatar img {
max-width: 78px;
max-height: 78px;
border-radius: 50%;
object-fit: cover;
}
.chat-message .message {
width: 80%;
padding: 0 1.5rem;
color: #fff;
}
'''
bot_template = '''
<div class="chat-message bot">
<div class="avatar">
<img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png"
style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
</div>
<div class="message">{{MSG}}</div>
</div>
'''
user_template = '''
<div class="chat-message user">
<div class="avatar">
<img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
</div>
<div class="message">{{MSG}}</div>
</div>
'''
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=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
embeddings = OpenAIEmbeddings()
# embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
llm = ChatOpenAI()
# llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
memory = ConversationBufferMemory(
memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
return conversation_chain
def handle_userinput(user_question):
response = st.session_state.conversation({'question': user_question})
st.session_state.chat_history = response['chat_history']
for i, message in enumerate(st.session_state.chat_history):
# Display user message
if i % 2 == 0:
st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
else:
print(message)
# Display AI response
st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
def safe_vec_store():
# USE VECTARA INSTEAD
os.makedirs('vectorstore', exist_ok=True)
filename = 'vectors' + datetime.now().strftime('%Y%m%d%H%M') + '.pkl'
file_path = os.path.join('vectorstore', filename)
vector_store = st.session_state.vectorstore
# Serialize and save the entire FAISS object using pickle
with open(file_path, 'wb') as f:
pickle.dump(vector_store, f)
"""
def main():
st.subheader("Your documents")
if st.session_state.classify:
pdf_doc = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=False)
else:
pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
filenames = [file.name for file in pdf_docs if file is not None]
if st.button("Process"):
with st.spinner("Processing"):
if st.session_state.classify:
# THE CLASSIFICATION APP
st.write("Classifying")
plain_text_doc = ingest(pdf_doc.name)
classification_result = generate_metadata(plain_text_doc)
st.write(classification_result)
else:
# NORMAL RAG
loaded_vec_store = None
for filename in filenames:
if ".pkl" in filename:
file_path = os.path.join('vectorstore', filename)
with open(file_path, 'rb') as f:
loaded_vec_store = pickle.load(f)
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
vec = get_vectorstore(text_chunks)
if loaded_vec_store:
vec.merge_from(loaded_vec_store)
st.warning("loaded vectorstore")
if "vectorstore" in st.session_state:
vec.merge_from(st.session_state.vectorstore)
st.warning("merged to existing")
st.session_state.vectorstore = vec
st.session_state.conversation = get_conversation_chain(vec)
st.success("data loaded")
if "conversation" not in st.session_state:
st.session_state.conversation = None
if "chat_history" not in st.session_state:
st.session_state.chat_history = None
user_question = st.text_input("Ask a question about your documents:")
if user_question:
handle_userinput(user_question)
with st.sidebar:
st.subheader("Classification instructions")
classifier_docs = st.file_uploader("Upload your instructions here and click on 'Process'",
accept_multiple_files=True)
filenames = [file.name for file in classifier_docs if file is not None]
if st.button("Process Classification"):
st.session_state.classify = True
with st.spinner("Processing"):
st.warning("set classify")
time.sleep(3)
if st.button("Save Embeddings"):
if "vectorstore" in st.session_state:
safe_vec_store()
# st.session_state.vectorstore.save_local("faiss_index")
st.sidebar.success("saved")
else:
st.sidebar.warning("No embeddings to save. Please process documents first.")
if st.button("Load Embeddings"):
st.warning("this function is not in use, just upload the vectorstore")
"""
def main():
st.set_page_config(page_title="Doc Verify RAG", page_icon=":mag:")
st.write('Anomaly detection for document metadata', unsafe_allow_html=True)
st.header("Doc Verify RAG :mag:")
def set_pw():
st.session_state.openai_api_key = True
if "openai_api_key" not in st.session_state:
st.session_state.openai_api_key = False
if "openai_org" not in st.session_state:
st.session_state.openai_org = False
if "classify" not in st.session_state:
st.session_state.classify = False
col1, col2 = st.columns(2)
with col1:
uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf", "txt"])
if uploaded_file is not None:
try:
with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
tmp.write(uploaded_file.read())
file_path = tmp.name
st.write(f'Created temporary file {file_path}')
docs = ingest(file_path)
st.write('## Querying Together.ai API')
metadata = generate_metadata(docs)
st.write(f'## Metadata Generated by {MODEL_NAME}')
st.write(metadata)
# Clean up the temporary file
os.remove(file_path)
except Exception as e:
st.error(f'Error: {e}')
with col2:
OPENAI_API_KEY = st.text_input("OPENAI API KEY:", type="password",
disabled=st.session_state.openai_api_key, on_change=set_pw)
classification = st.file_uploader("upload the metadata", type=["csv", "txt"])
if __name__ == '__main__':
main()