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3b2a05d 2af7ec4 202fb2f 3b2a05d 2af7ec4 202fb2f 2af7ec4 202fb2f 2af7ec4 202fb2f 2af7ec4 | 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 | import streamlit as st
import os
from model_processor import LlamaProcessor
st.set_page_config(page_title="Llama PDF Expert", layout="wide")
# --- UI Header ---
st.title("📄 PDF QA with Llama 3.2")
st.markdown("Upload a document and ask questions using a local Llama model.")
# --- Sidebar Configuration ---
with st.sidebar:
st.header("1. Authentication")
token = st.text_input("Hugging Face Token", type="password", help="Enter your HF 'Read' token.")
model_choice = st.selectbox("Select Model", ["meta-llama/Llama-3.2-1B-Instruct", "meta-llama/Llama-3.2-3B-Instruct"])
st.divider()
st.header("2. Document Upload")
uploaded_file = st.file_uploader("Upload PDF", type="pdf")
# --- Session State Initialization ---
if "processor" not in st.session_state:
st.session_state.processor = None
if "vector_db" not in st.session_state:
st.session_state.vector_db = None
if "messages" not in st.session_state:
st.session_state.messages = []
# --- Logic: Model Loading & Processing ---
if uploaded_file and token:
if st.session_state.processor is None:
try:
with st.spinner("Initializing Llama... (this may take a minute)"):
st.session_state.processor = LlamaProcessor(model_choice, token)
# Save and process PDF
with open("temp_upload.pdf", "wb") as f:
f.write(uploaded_file.getbuffer())
with st.spinner("Indexing document..."):
st.session_state.vector_db = st.session_state.processor.process_pdf("temp_upload.pdf")
st.success("Document processed! Ready to chat.")
except Exception as e:
st.error(f"Error initializing: {str(e)}")
# --- Logic: Chat Interface ---
# Display chat history
for msg in st.session_state.messages:
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
# User Input
if prompt := st.chat_input("Ask a question about the PDF..."):
if not st.session_state.vector_db:
st.warning("Please upload a PDF and provide a token first.")
else:
# User message
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Assistant response
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
answer = st.session_state.processor.get_answer(prompt, st.session_state.vector_db)
st.markdown(answer)
st.session_state.messages.append({"role": "assistant", "content": answer}) |