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# app/main.py
import os, json
import streamlit as st
from ingestion import extract_text_from_pdf, chunk_text
from embeddings import generate_embeddings
from vectorstore import build_faiss_index
from qa import retrieve_chunks, generate_answer
import faiss
# ---------------------------
# App Config
# ---------------------------
st.set_page_config(page_title="Enterprise Knowledge Assistant", layout="wide")
st.title("📄 Enterprise Knowledge Assistant")
st.caption("Select a document from the library or upload your own, then ask questions.")
# ---------------------------
# Sidebar (Settings + Docs + Credits)
# ---------------------------
with st.sidebar:
st.image("app/logo.png", width=150)
# 1. Document Library FIRST
st.header("📚 Document Library")
doc_choice = st.radio(
"Choose a document:",
["-- Select --", "Sample PDF", "Upload Custom PDF"],
index=0
)
st.markdown("---")
# 2. Settings SECOND
st.header("⚙️ Settings")
chunk_size = st.slider("Chunk Size", 200, 1000, 500, step=100)
top_k = st.slider("Top K Results", 1, 5, 3)
st.markdown("---")
# 3. Branding / Credits LAST
st.caption("👨💻 Built by Shubham Sharma")
st.markdown("[📂 GitHub Repo](https://github.com/shubhamsharma170793-cpu/enterprise-knowledge-assistant)")
# ---------------------------
# Document Handling
# ---------------------------
text, chunks, index = None, None, None
if doc_choice == "-- Select --":
st.info("⬅️ Please choose **Sample PDF** or **Upload Custom PDF** from the sidebar to continue.")
elif doc_choice == "Sample PDF":
temp_path = os.path.join("app", "sample.pdf")
st.success("📘 Sample PDF selected")
text = extract_text_from_pdf(temp_path)
chunks = chunk_text(text, chunk_size=chunk_size)
embeddings = generate_embeddings(chunks)
index = build_faiss_index(embeddings)
elif doc_choice == "Upload Custom PDF":
uploaded_file = st.file_uploader("📂 Upload your PDF", type="pdf")
if uploaded_file:
temp_path = "temp.pdf"
with open(temp_path, "wb") as f:
f.write(uploaded_file.getbuffer())
st.success("✅ Document uploaded and processed!")
text = extract_text_from_pdf(temp_path)
chunks = chunk_text(text, chunk_size=chunk_size)
embeddings = generate_embeddings(chunks)
index = build_faiss_index(embeddings)
# ---------------------------
# Document Preview
# ---------------------------
if chunks:
st.subheader("📑 Document Preview")
st.text_area("Extracted text (first 1000 chars)", text[:1000], height=150)
st.caption(f"📦 {len(chunks)} chunks created")
# ---------------------------
# Query Section
# ---------------------------
if index and chunks:
st.markdown("---")
st.subheader("🤖 Ask a Question")
user_query = st.text_input("🔍 Your question about the document:")
if user_query:
retrieved = retrieve_chunks(user_query, index, chunks, top_k=top_k)
answer = generate_answer(user_query, retrieved)
st.markdown("### ✅ Assistant’s Answer")
st.write(answer)
with st.expander("📄 Supporting Chunks"):
for i, r in enumerate(retrieved, start=1):
st.markdown(f"**Chunk {i}:** {r}")
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