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import os
import streamlit as st
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.document_loaders import PyPDFLoader
# Initialize session state variables
if "messages" not in st.session_state:
st.session_state.messages = []
if "chain" not in st.session_state:
st.session_state.chain = None
if "processed_pdfs" not in st.session_state:
st.session_state.processed_pdfs = False
def create_sidebar():
with st.sidebar:
st.title("PDF Chat")
st.markdown("### Quick Demo of RAG")
api_key = st.text_input("OpenAI API Key:", type="password")
st.markdown("""
### Tools Used
- OpenAI
- LangChain
- ChromaDB
### Steps
1. Add API key
2. Upload PDF
3. Chat!
""")
return api_key
def process_pdfs(papers, api_key):
if papers and not st.session_state.processed_pdfs:
with st.spinner("Processing PDFs..."):
texts = []
for paper in papers:
try:
file_path = os.path.join('./uploads', paper.name)
os.makedirs('./uploads', exist_ok=True)
with open(file_path, "wb") as f:
f.write(paper.getbuffer())
loader = PyPDFLoader(file_path)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len,
is_separator_regex=False,
)
texts.extend(text_splitter.split_documents(documents))
os.remove(file_path)
except Exception as e:
st.error(f"Error processing {paper.name}: {str(e)}")
if texts:
embedding = OpenAIEmbeddings(openai_api_key=api_key)
vectorstore = Chroma(embedding_function=embedding, persist_directory="db")
vectorstore.add_documents(texts)
st.session_state.chain = ConversationalRetrievalChain.from_llm(
ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_key=api_key),
vectorstore.as_retriever(),
memory=ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
)
st.session_state.processed_pdfs = True
st.success("PDFs processed successfully!")
return texts
return []
def main():
st.set_page_config(page_title="PDF Chat")
# Sidebar with API key input
api_key = create_sidebar()
if not api_key:
st.warning("Please enter your OpenAI API key")
return
st.title("Chat with PDF")
# File uploader
papers = st.file_uploader("Upload PDFs", type=["pdf"], accept_multiple_files=True)
# Process PDFs if needed
texts = process_pdfs(papers, api_key)
# Display chat messages from history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input
if prompt := st.chat_input("Ask about your PDFs"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message
with st.chat_message("user"):
st.markdown(prompt)
# Generate and display assistant response
with st.chat_message("assistant"):
if not st.session_state.processed_pdfs:
response = "Please upload a PDF first."
else:
with st.spinner("Thinking..."):
result = st.session_state.chain({"question": prompt})
response = result["answer"]
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
if __name__ == "__main__":
main() |