Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,98 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# 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
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# ---------------------------
+# 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}")