Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,98 +1,36 @@
-# app/main.py
-import os, json
+# ----------------------------
+# Hugging Face cache bootstrap
+# ----------------------------
+import os
+
+CACHE_DIR = "/tmp/huggingface"
+os.makedirs(CACHE_DIR, exist_ok=True)
+
+os.environ["HF_HOME"] = CACHE_DIR
+os.environ["TRANSFORMERS_CACHE"] = CACHE_DIR
+os.environ["HF_DATASETS_CACHE"] = CACHE_DIR
+os.environ["HF_MODULES_CACHE"] = CACHE_DIR
+
+# ----------------------------
+# Imports AFTER cache bootstrap
+# ----------------------------
 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)
-# ---------------------------
+st.write("Upload a PDF **or try the sample file** to explore this assistant.")
+
+# ----------------------------
+# Sidebar (Settings + 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}")
+    st.header("⚙️ Se