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import streamlit as st
import os
from src.rag_engine import ProjectRAGEngine
# -------------------------------
# Page configuration
# -------------------------------
st.set_page_config(
page_title="IIR Project Analyzer",
layout="wide"
)
st.title("📂 Industrial Project Report Analyzer")
# -------------------------------
# Session persistence
# -------------------------------
if "engine" not in st.session_state:
st.session_state.engine = ProjectRAGEngine()
if "processed_files" not in st.session_state:
st.session_state.processed_files = []
if "messages" not in st.session_state:
st.session_state.messages = []
# -------------------------------
# File upload
# -------------------------------
uploaded_files = st.sidebar.file_uploader(
"Upload Project PDFs",
type="pdf",
accept_multiple_files=True
)
if uploaded_files:
uploaded_names = {f.name for f in uploaded_files}
if set(st.session_state.processed_files) != uploaded_names:
with st.spinner("Analyzing project reports..."):
temp_dir = "temp"
os.makedirs(temp_dir, exist_ok=True)
paths = []
for f in uploaded_files:
path = os.path.join(temp_dir, f.name)
with open(path, "wb") as out:
out.write(f.getbuffer())
paths.append(path)
st.session_state.engine.process_documents(paths)
st.session_state.processed_files = list(uploaded_names)
st.success("Reports indexed. Ready for queries.")
# -------------------------------
# Display previous chat messages
# -------------------------------
for msg in st.session_state.messages:
with st.chat_message(msg["role"]):
st.write(msg["content"])
# -------------------------------
# Chat interface
# -------------------------------
query = st.chat_input("Ex: 'Compare the budgets of these projects'")
if query:
# Store user message
st.session_state.messages.append(
{"role": "user", "content": query}
)
with st.chat_message("user"):
st.write(query)
with st.chat_message("assistant"):
answer, sources = st.session_state.engine.get_answer(query)
st.markdown("### Response")
st.write(answer)
# Store assistant message
st.session_state.messages.append(
{"role": "assistant", "content": answer}
)
if sources:
with st.expander("📌 Source Attribution & Quotes"):
for idx, s in enumerate(sources):
doc_name = os.path.basename(s["metadata"]["source"])
page_num = s["metadata"]["page"] + 1
st.markdown(
f"**Source {idx + 1}:** {doc_name} (Page {page_num})"
)
st.caption(f"\"{s['content'][:300]}...\"")
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