src/streamlit_app.py

# ==========================================================
# streamlit_app.py — Stable Layout (English Only)
# ==========================================================
import os
import re
import streamlit as st
import torch

# ==========================================================
# ✅ PAGE CONFIGS
# ==========================================================
st.set_page_config(page_title="Enterprise Knowledge Assistant", layout="wide")
print("CUDA available:", torch.cuda.is_available())

# ==========================================================
# ⚙️ CACHE SETUP
# ==========================================================
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
# ==========================================================
from ingestion import extract_text_from_pdf, chunk_text
from vectorstore import build_faiss_index
from qa import retrieve_chunks, generate_answer, cache_embeddings, embed_chunks, genai_generate

# ==========================================================
# 🧠 SMART SUGGESTION GENERATOR (English Only)
# ==========================================================
def generate_dynamic_suggestions_from_toc(toc, chunks, doc_name="Document"):
    """
    Generates 5–7 short, natural English questions based on TOC and document text.
    """
    if not toc or not chunks:
        return ["How do I start using this guide?", "What does this document cover?"]

    titles = []
    for sec, raw_title in toc:
        title = re.sub(r"^\s*[\dA-Za-z.\-]+\s*", "", raw_title)
        title = re.sub(r"\.{2,}\s*\d+$", "", title).strip()
        if 4 < len(title) < 120:
            titles.append(title)

    context_sample = " ".join(chunks[:3])[:4000]
    prompt = f"""
You are a content assistant. Based on the Table of Contents and the sample document text below,
generate 5–7 short, natural user-facing questions.
Each question should be under 18 words, end with a question mark, and sound human.
Document: "{doc_name}"

TABLE OF CONTENTS:
{chr(10).join(['- ' + t for t in titles[:8]])}

SAMPLE TEXT:
{context_sample}

Output: Write each question on a new line. Do not invent facts — base questions only on the document.
"""

    try:
        ai_response = genai_generate(prompt)
        lines = [ln.strip() for ln in ai_response.splitlines() if ln.strip()]
        questions = []
        for ln in lines:
            q = re.sub(r"^[\-\u2022\*\d\.\)\s]+", "", ln).strip()
            if not q.endswith("?") and len(q.split()) < 18 and re.match(r"(?i)^(what|how|why|where|who|when|which|can|does|is|are)\b", q):
                q += "?"
            if 8 <= len(q) <= 140:
                questions.append(q)
        # dedupe
        final = []
        seen = set()
        for q in questions:
            if q.lower() not in seen:
                seen.add(q.lower())
                final.append(q)
        if not final:
            final = [f"What should I know about {t.rstrip('.')}?" for t in titles[:7]]
        return final[:7]
    except Exception:
        return ["How do I start using this guide?", "What does this document cover?"]

# ==========================================================
# 🎨 STYLING — REVERT TO ORIGINAL
# ==========================================================
st.markdown("""
<style>
div.block-container {padding-top: 1.2rem; max-width: 1080px;}
h1, h2, h3 {color: #f3f4f6; font-weight: 600;}
.suggest-chip {
    background: #0f1724;
    border: 1px solid #374151;
    border-radius: 14px;
    color: #e6eef8;
    padding: 8px 12px;
    cursor: pointer;
    font-size: 13px;
    margin: 6px 6px 6px 0;
    display: inline-block;
    transition: background 0.2s, transform 0.1s;
}
.suggest-chip:hover {background: #1e3a8a; transform: translateY(-2px);}
.answer-box {
    background: linear-gradient(180deg,#0b1220,#071027);
    border-left: 4px solid #3b82f6;
    border-radius: 8px;
    padding: 16px 18px;
    color: #e6eef8;
    margin-top: 12px;
    box-shadow: 0 4px 14px rgba(0,0,0,0.35);
}
.stTextInput > div > div > input {
    background-color: #0f172a !important;
    color: #f1f5f9 !important;
    border-radius: 6px !important;
    border: 1px solid #334155 !important;
    padding: 8px 10px !important;
    font-size: 15px !important;
}
.stTextInput > label {font-weight: 500;}
.small-link {
    font-size: 13px;
    color: #60a5fa;
    cursor: pointer;
}
</style>
""", unsafe_allow_html=True)

# ==========================================================
# 🧭 SIDEBAR
# ==========================================================
with st.sidebar:
    st.markdown("### 🧭 Response Style")
    mode = st.radio(
        "",
        ("Strict (Document-only)", "Extended (Document + general)"),
        index=0,
        help="Strict = answers only from the uploaded document. Extended = may include related general info.",
    )

    st.markdown("---")
    show_dev = st.checkbox("Show advanced settings (for developers)", value=False)
    if show_dev:
        st.markdown("### ⚙️ Developer Options")
        chunk_size = st.slider("Chunk Size", 200, 1500, 1000, step=50)
        overlap = st.slider("Chunk Overlap", 50, 200, 120, step=10)
        top_k = st.slider("Top K Results", 1, 10, 7)
    else:
        chunk_size, overlap, top_k = 1000, 120, 5

    st.markdown("---")
    st.caption("✨ Built by Shubham Sharma")

# ==========================================================
# 🧠 SESSION STATE
# ==========================================================
for key, val in {
    "user_query_input": "",
    "show_more": False,
    "selected_suggestion": None,
    "query_suggestions_fixed": None,
    "last_doc": None,
}.items():
    if key not in st.session_state:
        st.session_state[key] = val

def set_user_query(q, idx):
    st.session_state["user_query_input"] = q
    st.session_state["selected_suggestion"] = idx
    st.rerun()

# ==========================================================
# 📄 MAIN SECTION
# ==========================================================
st.title("📄 Enterprise Knowledge Assistant")
st.caption("Query SAP documentation and enterprise PDFs — powered by reasoning and retrieval.")

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

# ==========================================================
# 📂 DOCUMENT HANDLING
# ==========================================================
if doc_choice == "-- Select --":
    st.info("⬅️ Select or upload a document to begin.")
else:
    if doc_choice == "Sample PDF":
        temp_path = os.path.join(os.path.dirname(__file__), "sample.pdf")
        st.success("📘 Sample document loaded successfully — you can start asking your questions below.")
    else:
        uploaded_file = st.file_uploader("", type="pdf", label_visibility="collapsed")
        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("✅ Document processed successfully — you can start asking your questions below.")
        else:
            temp_path = None

    if temp_path:
        with st.spinner("🔍 Processing document..."):
            text, toc, toc_source = extract_text_from_pdf(temp_path)
            chunks = chunk_text(text, chunk_size=chunk_size, overlap=overlap)

        with st.spinner("⚙️ Building search index..."):
            embeddings = cache_embeddings(os.path.basename(temp_path), chunks, embed_chunks)
            index = build_faiss_index(embeddings)

        doc_name = os.path.basename(temp_path)
        if st.session_state["last_doc"] != doc_name:
            query_suggestions = generate_dynamic_suggestions_from_toc(toc, chunks, doc_name)
            st.session_state["query_suggestions_fixed"] = query_suggestions
            st.session_state["last_doc"] = doc_name
            st.session_state["user_query_input"] = ""
            st.session_state["selected_suggestion"] = None
            st.session_state["show_more"] = False
            st.rerun()
        else:
            query_suggestions = st.session_state["query_suggestions_fixed"]

        # ----------------------------------------------------------
        # 💬 ASK SECTION
        # ----------------------------------------------------------
        st.markdown("### 💬 Ask the Assistant")

        if query_suggestions:
            visible = query_suggestions if st.session_state["show_more"] else query_suggestions[:3]
            cols = st.columns(min(3, len(visible)))
            for i, q in enumerate(visible):
                if cols[i % 3].button(f"💬 {q}", key=f"sugg_{i}"):
                    set_user_query(q, i)

            toggle_text = "Show less ▲" if st.session_state["show_more"] else "Show more ▼"
            if st.button(toggle_text, help="Show or hide more suggestions"):
                st.session_state["show_more"] = not st.session_state["show_more"]
                st.rerun()

        user_query = st.text_input("Type your question or click one above:", key="user_query_input")

        # ----------------------------------------------------------
        # 💡 RESPONSE SECTION
        # ----------------------------------------------------------
        if user_query.strip():
            reasoning_mode = mode == "Extended (Document + general)"
            with st.spinner("💭 Generating your answer..."):
                retrieved = retrieve_chunks(user_query, index, chunks, top_k=top_k, embeddings=embeddings)
                answer = generate_answer(user_query, retrieved, reasoning_mode=reasoning_mode)

            st.markdown("### 🤖 Assistant’s Answer")

            if not reasoning_mode and not answer.startswith("⚠️"):
                answer = re.sub(r"\*\*(.*?)\*\*", r"\1", answer)
                answer = re.sub(r"(^|\n)-\s*", r"\1<br>• ", answer)
            st.markdown(f"<div class='answer-box'>{answer}</div>", unsafe_allow_html=True)

            with st.expander("📘 Supporting Context"):
                for i, r in enumerate(retrieved, start=1):
                    st.markdown(f"**Chunk {i}:** {r}")

            if toc:
                with st.expander("📚 Explore Document Sections"):
                    toc_text = "\n".join([f"{sec}. {title}" for sec, title in toc])
                    st.text_area("", toc_text, height=140)

            with st.expander("📄 Document Preview"):
                st.text_area("", text[:1000], height=140)
                st.caption(f"{len(chunks)} chunks processed.")