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import os
import streamlit as st
from llm import load_llm, response_generator
from vector_store import load_vector_store, process_pdf
from uuid import uuid4
# repo_id = "Qwen/Qwen2.5-0.5B-Instruct-GGUF"
# filename = "qwen2.5-0.5b-instruct-q8_0.gguf"
repo_id = "Qwen/Qwen2.5-3B-Instruct-GGUF"
filename = "qwen2.5-3b-instruct-q5_k_m.gguf"
llm = load_llm(repo_id, filename)
st.title("PDF QA")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
if message["role"] == "user":
st.markdown(message["content"])
else:
st.code(message["content"])
# Accept user input
if prompt := st.chat_input("What is up?"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
vector_store = load_vector_store()
retriever = vector_store.as_retriever()
docs = retriever.get_relevant_documents(prompt)
response = response_generator(llm, st.session_state.messages, prompt, retriever)
st.markdown(response["answer"])
# Add assistant response to chat history
st.session_state.messages.append(
{"role": "assistant", "content": response["answer"]}
)
with st.sidebar:
st.title("PDFs")
st.write("Upload your pdfs here")
uploaded_files = st.file_uploader(
"Choose a PDF file", accept_multiple_files=True, type="pdf"
)
if uploaded_files is not None:
vector_store = load_vector_store()
for uploaded_file in uploaded_files:
temp_dir = "./temp"
if not os.path.exists(temp_dir):
os.makedirs(temp_dir)
temp_file = f"./temp/{uploaded_file.name}-{uuid4()}.pdf"
with open(temp_file, "wb") as file:
file.write(uploaded_file.getvalue())
st.write("filename:", uploaded_file.name)
process_pdf(temp_file, vector_store)
st.success("PDFs uploaded successfully. ✅")
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