src/streamlit_app.py

import os
import shutil
import streamlit as st

# ==========================================================
# ✅ Page Configuration (must be first Streamlit command)
# ==========================================================
st.set_page_config(
    page_title="Enterprise Knowledge Assistant",
    layout="wide"
)

# ==========================================================
# 🧹 Cache Management (prevents Hugging Face 50GB overflow)
# ==========================================================
def clean_cache(max_size_gb: float = 2.0):
    """
    Cleans large cache folders (> max_size_gb), preserving /tmp/hf_cache if small.
    """
    folders = [
        "/root/.cache/huggingface",
        "/root/.cache/transformers",
        "/root/.cache/torch",
        "/tmp/hf_cache",
    ]
    total_deleted = 0.0

    for folder in folders:
        if os.path.exists(folder):
            # estimate folder size
            size_gb = sum(
                os.path.getsize(os.path.join(dp, f))
                for dp, _, files in os.walk(folder)
                for f in files
            ) / (1024**3)

            # only delete if large
            if size_gb > max_size_gb or "torch" in folder:
                shutil.rmtree(folder, ignore_errors=True)
                total_deleted += size_gb
                print(f"🗑️ Deleted {folder} ({size_gb:.2f} GB)")
            else:
                print(f"✅ Preserved {folder} ({size_gb:.2f} GB)")

    os.makedirs("/tmp/hf_cache", exist_ok=True)
    print(f"🧹 Cache cleanup done. ~{total_deleted:.2f} GB removed.")


def check_disk_usage():
    """Show disk usage info in sidebar."""
    st.sidebar.markdown("### 💾 Disk Usage (Debug)")
    try:
        usage = os.popen("du -sh /root/.cache /tmp 2>/dev/null").read()
        st.sidebar.text(usage if usage else "No cache directories found.")
    except Exception as e:
        st.sidebar.text(f"⚠️ Disk usage check failed: {e}")


# Run cleanup & diagnostics
clean_cache()
check_disk_usage()

# ==========================================================
# ⚙️ Hugging Face Cache Configuration (/tmp for writable path)
# ==========================================================
CACHE_DIR = "/tmp/hf_cache"
os.makedirs(CACHE_DIR, exist_ok=True)
os.environ.update({
    "HF_HOME": CACHE_DIR,
    "TRANSFORMERS_CACHE": CACHE_DIR,
    "HF_DATASETS_CACHE": CACHE_DIR,
    "HF_MODULES_CACHE": CACHE_DIR
})

# ==========================================================
# 📦 Imports AFTER environment setup
# ==========================================================
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

# ==========================================================
# 📁 Paths
# ==========================================================
BASE_DIR = os.path.dirname(__file__)  # /app/src
LOGO_PATH = os.path.join(BASE_DIR, "logo.png")
SAMPLE_PATH = os.path.join(BASE_DIR, "sample.pdf")

# ==========================================================
# 🖥️ UI Header
# ==========================================================
st.title("📄 Enterprise Knowledge Assistant")
st.caption("Upload a PDF or use the sample file to explore intelligent document Q&A.")

# ==========================================================
# 🧭 Sidebar (Document Library + Settings + Diagnostics)
# ==========================================================
with st.sidebar:
    if os.path.exists(LOGO_PATH):
        st.image(LOGO_PATH, width=150)

    st.header("📚 Document Library")
    doc_choice = st.radio(
        "Choose a document:",
        ["-- Select --", "Sample PDF", "Upload Custom PDF"],
        index=0
    )

    st.markdown("---")

    st.header("⚙️ Settings")
    chunk_size = st.slider("Chunk Size (characters)", 300, 1200, 800, step=100)
    top_k = st.slider("Top K Results (retrieved chunks)", 1, 10, 5)

    st.markdown("---")
    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.")

elif doc_choice == "Sample PDF":
    temp_path = SAMPLE_PATH
    st.success("📘 Using built-in Sample PDF")
    with st.spinner("🔍 Extracting and processing document..."):
        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 = os.path.join("/tmp", uploaded_file.name)
        with open(temp_path, "wb") as f:
            f.write(uploaded_file.getbuffer())
        st.success(f"✅ File '{uploaded_file.name}' uploaded successfully")

        with st.spinner("⚙️ Extracting and processing your document..."):
            text = extract_text_from_pdf(temp_path)
            chunks = chunk_text(text, chunk_size=chunk_size)
            embeddings = generate_embeddings(chunks)
            index = build_faiss_index(embeddings)
        st.success("🚀 Document processed successfully!")

# ==========================================================
# 📑 Document Preview
# ==========================================================
if chunks:
    st.subheader("📑 Document Preview")
    st.text_area("Extracted text (first 1000 chars)", text[:1000], height=200)
    avg_len = int(sum(len(c) for c in chunks) / len(chunks))
    st.caption(f"📦 {len(chunks)} chunks created | Avg chunk length: {avg_len} chars")

# ==========================================================
# 💬 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:
        with st.spinner("🧠 Thinking... retrieving context and generating answer..."):
            retrieved = retrieve_chunks(user_query, index, chunks, top_k=top_k)
            answer = generate_answer(user_query, retrieved)

        # ✅ Answer Display
        st.markdown("### ✅ Assistant’s Answer")
        st.markdown(
            f"<div style='background-color:#0E1117;padding:12px;border-radius:10px;color:white;'>{answer}</div>",
            unsafe_allow_html=True
        )

        # 📄 Supporting Chunks
        with st.expander("📄 Supporting Chunks (Context Used)"):
            for i, r in enumerate(retrieved, start=1):
                st.markdown(
                    f"""
                    <div style='background-color:#111827;padding:10px;border-radius:8px;margin-bottom:6px;'>
                    <b>Chunk {i}:</b><br>{r}
                    </div>
                    """,
                    unsafe_allow_html=True,
                )
else:
    st.info("📥 Upload or select a document to start exploring.")