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import os
import streamlit as st
from rag_utility import process_document_to_chromadb, answer_question
# Set working directory (optional, ensures saving in project dir)
working_dir = os.getcwd()
st.title("🐋 DeepSeek-R1 vs 🦙 Llama-3")
# File uploader
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
# Checkbox flag to track processing status
doc_processed = False
user_question = st.text_area("Ask your question from the document")
if st.button("Answer"):
if uploaded_file is None:
st.error("Please upload a PDF file before asking a question.")
else:
# Process the document only when answering the first time
if not doc_processed:
save_path = os.path.join(working_dir, uploaded_file.name)
with open(save_path, "wb") as f:
f.write(uploaded_file.getbuffer())
process_document_to_chromadb(uploaded_file.name)
doc_processed = True # Mark as processed
st.info("Document Processed Successfully")
# Get answers
answer = answer_question(user_question)
answer_deepseek = answer["answer_deepseek"]
answer_llama3 = answer["answer_llama3"]
# Display side-by-side answers
col1, col2 = st.columns(2)
with col1:
st.markdown("### DeepSeek-r1 Response")
st.markdown(
f"""
<div style="border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9; padding: 10px;">
{answer_deepseek}
</div>
""",
unsafe_allow_html=True
)
with col2:
st.markdown("### Llama-3 Response")
st.markdown(
f"""
<div style="border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9; padding: 10px;">
{answer_llama3}
</div>
""",
unsafe_allow_html=True
)
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