"""
DocMind — Grounded RAG Document Intelligence
=============================================
Streamlit entry point: page layout, sidebar, routing, and main Q&A loop.
"""

import logging
import sys
import time
from typing import Dict, List, Optional

import streamlit as st

# ── Setup logging ───────────────────────────────────────────────────
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s | %(name)s | %(levelname)s | %(message)s",
    stream=sys.stdout,
)
logger = logging.getLogger("docmind")

# ── Page config (must be first st call) ─────────────────────────────
st.set_page_config(
    page_title="DocMind — Grounded RAG",
    page_icon="🧠",
    layout="wide",
    initial_sidebar_state="expanded",
)

# ── Imports (after st.set_page_config) ──────────────────────────────
from config import CONFIG
from pipeline.chunker import (
    ChunkMetadata, parse_document, chunk_text, generate_doc_id,
    extract_section_titles,
)
from pipeline.embedder import load_bge_model, embed_chunks, embed_query
from pipeline.bm25_index import build_bm25_index, BM25Index
from pipeline.qdrant_store import (
    init_qdrant_client, init_collection, clear_collection, upsert_chunks,
)
from pipeline.retriever import hybrid_retrieve
from pipeline.router import route_query, IntentType
from pipeline.llm import generate_answer
from pipeline.attribution import parse_attributed_response, strip_unattributed
from pipeline.grounding import grounding_gate, ConfidenceLevel
from summarizer.quick_summary import generate_quick_summary
from summarizer.structured_summary import generate_structured_summary
from summarizer.key_points import extract_key_points
from ui.styles import get_custom_css
from ui.components import (
    render_chat_message, render_empty_state, render_document_status,
    render_pipeline_progress, render_retrieval_stats, render_confidence_badge,
    render_grounding_bar, render_comparison_table, render_dashboard_metrics,
    render_keypoint_card,
)


# ── Inject custom CSS ──────────────────────────────────────────────
st.markdown(get_custom_css(), unsafe_allow_html=True)


# ── Session state initialization ───────────────────────────────────

def init_session_state():
    """Initialize all session state variables."""
    defaults = {
        "chat_history": [],          # List of {role, content, grounding, sources, stats}
        "chunk_store": {},           # chunk_id → ChunkMetadata
        "bm25_index": None,          # BM25Index instance
        "doc_info": [],              # List of {name, doc_id, chunk_count, page_count, color_idx}
        "all_chunks": [],            # All chunks across all documents
        "doc_index_map": {},         # doc_id → color index
        "show_debug": False,         # Toggle retrieval debug panel
    }
    for key, value in defaults.items():
        if key not in st.session_state:
            st.session_state[key] = value


init_session_state()


# ── Document ingestion ─────────────────────────────────────────────

def ingest_document(uploaded_file, doc_color_index: int) -> Optional[dict]:
    """
    Full ingestion pipeline for a single document:
    Parse → Chunk → Embed → Index.

    Returns doc info dict or None on failure.
    """
    filename = uploaded_file.name
    file_bytes = uploaded_file.read()

    # Validate file size
    max_bytes = CONFIG.ui.max_upload_mb * 1024 * 1024
    if len(file_bytes) > max_bytes:
        st.error(f"❌ File too large: {len(file_bytes) / 1e6:.1f}MB (max {CONFIG.ui.max_upload_mb}MB)")
        return None

    doc_id = generate_doc_id(filename, file_bytes)

    # Check if already ingested
    if doc_id in st.session_state.doc_index_map:
        st.info(f"📄 '{filename}' is already loaded.")
        return None

    progress = st.progress(0, text="📄 Parsing document...")

    try:
        # Stage 1: Parse
        pages = parse_document(filename, file_bytes)
        progress.progress(25, text="✂️ Chunking text...")

        # Stage 2: Chunk
        chunks = chunk_text(
            pages, doc_id, filename,
            max_tokens=CONFIG.chunking.max_tokens,
            overlap_tokens=CONFIG.chunking.overlap_tokens,
        )
        if not chunks:
            st.error("❌ No text content found in document.")
            progress.empty()
            return None
        progress.progress(50, text="🔢 Generating embeddings...")

        # Stage 3: Embed
        embed_model = load_bge_model()
        texts = [c.text for c in chunks]
        vectors = embed_chunks(embed_model, texts, batch_size=CONFIG.embedding.batch_size)
        progress.progress(75, text="📊 Indexing vectors...")

        # Stage 4: Index in Qdrant
        qdrant_client = init_qdrant_client()
        init_collection(qdrant_client)
        metadata = [
            {"doc_id": c.doc_id, "doc_name": c.doc_name, "page_num": c.page_num}
            for c in chunks
        ]
        upsert_chunks(
            qdrant_client,
            chunk_ids=[c.chunk_id for c in chunks],
            vectors=vectors,
            metadata=metadata,
        )

        # Store chunks in session state
        for chunk in chunks:
            st.session_state.chunk_store[chunk.chunk_id] = chunk
        st.session_state.all_chunks.extend(chunks)

        # Update BM25 index (rebuilt with all documents)
        all_chunk_ids = list(st.session_state.chunk_store.keys())
        all_texts = [st.session_state.chunk_store[cid].text for cid in all_chunk_ids]
        st.session_state.bm25_index = build_bm25_index(all_chunk_ids, all_texts)

        # Track document
        st.session_state.doc_index_map[doc_id] = doc_color_index
        page_count = len(set(c.page_num for c in chunks))
        doc_info = {
            "name": filename,
            "doc_id": doc_id,
            "chunk_count": len(chunks),
            "page_count": page_count,
            "color_idx": doc_color_index,
        }
        st.session_state.doc_info.append(doc_info)

        progress.progress(100, text="✅ Done!")
        time.sleep(0.5)
        progress.empty()

        st.success(f"✅ **{len(chunks)} chunks** indexed from **{page_count} pages** — {filename}")
        logger.info("Ingested '%s': %d chunks, %d pages", filename, len(chunks), page_count)
        return doc_info

    except Exception as e:
        progress.empty()
        st.error(f"❌ Ingestion failed: {e}")
        logger.exception("Ingestion error for '%s'", filename)
        return None


# ── Q&A Pipeline ───────────────────────────────────────────────────

def run_qa_pipeline(query: str) -> dict:
    """
    Execute the full 4-layer RAG pipeline:
    1. Intent Router
    2. Hybrid Retrieval
    3. Attributed Generation
    4. Grounding Gate

    Returns a dict with the answer and all metadata.
    """
    result = {
        "answer": "",
        "grounding": None,
        "sources": [],
        "stats": None,
        "is_handoff": False,
        "is_refused": False,
    }

    # ── Layer 1: Intent Router ──
    intent = route_query(query)
    
    if intent.intent_type == IntentType.SENSITIVE:
        result["answer"] = intent.handoff_message
        result["is_handoff"] = True
        return result
        
    if intent.intent_type == IntentType.SUMMARY:
        st.info("Generating comprehensive summary...", icon="📊")
        result["answer"] = generate_structured_summary(st.session_state.all_chunks)
        result["is_handoff"] = True  # Treat as handoff to bypass grounding UI
        return result
        
    if intent.intent_type == IntentType.KEY_POINTS:
        st.info("Extracting key points...", icon="🎯")
        kps = extract_key_points(st.session_state.all_chunks)
        md_bullets = "\n".join(f"- {kp.text} 📄 {kp.page_ref.replace('[PAGE ', 'p.').replace(']', '')}" for kp in kps)
        result["answer"] = md_bullets
        result["is_handoff"] = True
        return result

    # ── Layer 2: Hybrid Retrieval ──
    embed_model = load_bge_model()
    qdrant_client = init_qdrant_client()

    chunks, stats = hybrid_retrieve(
        query=query,
        embed_model=embed_model,
        bm25_index=st.session_state.bm25_index,
        qdrant_client=qdrant_client,
        chunk_store=st.session_state.chunk_store,
    )
    result["stats"] = stats
    result["sources"] = chunks

    if not chunks:
        result["answer"] = (
            "I couldn't find any relevant information in the uploaded documents "
            "for this query. Please try rephrasing or upload a relevant document."
        )
        result["is_refused"] = True
        return result

    # ── Layer 3: Attributed Generation ──
    raw_answer = generate_answer(chunks, query)

    if raw_answer.strip() == "INSUFFICIENT_CONTEXT":
        result["answer"] = (
            "The uploaded documents don't contain enough relevant information "
            "to answer this question confidently."
        )
        result["is_refused"] = True
        return result

    # Parse attribution tags
    sentences = parse_attributed_response(raw_answer)
    valid_ids = set(st.session_state.chunk_store.keys())
    sentences = strip_unattributed(sentences, valid_ids)

    if not sentences:
        result["answer"] = (
            "I couldn't generate a verifiable answer from the documents. "
            "Try asking something more specific."
        )
        result["is_refused"] = True
        return result

    # ── Layer 4: Grounding Gate ──
    grounding_result = grounding_gate(sentences, st.session_state.chunk_store)
    result["grounding"] = grounding_result

    if grounding_result.is_refused:
        result["answer"] = grounding_result.refusal_message
        result["is_refused"] = True
    else:
        # Build final answer from grounded sentences
        answer_parts = []
        for sent in grounding_result.grounded_sentences:
            if sent.chunk_id in st.session_state.chunk_store:
                chunk = st.session_state.chunk_store[sent.chunk_id]
                doc_name = chunk.doc_name
                page = chunk.page_num
                citation = f'<span class="citation-badge" title="{doc_name} (Page {page})">📄 p.{page}</span>'
                answer_parts.append(f"{sent.text} {citation}")
            else:
                answer_parts.append(sent.text)
        result["answer"] = " ".join(answer_parts)

    return result


# ── Chat export ────────────────────────────────────────────────────

def export_chat_as_text() -> str:
    """Export chat history as plain text."""
    lines = ["DocMind — Chat Export", "=" * 40, ""]
    for msg in st.session_state.chat_history:
        role = "You" if msg["role"] == "user" else "DocMind"
        lines.append(f"{role}: {msg['content']}")
        if msg.get("grounding") and not msg["grounding"].is_refused:
            lines.append(f"  [Grounding Score: {msg['grounding'].overall_score:.1%}]")
        lines.append("")
    return "\n".join(lines)


# ── Sidebar ────────────────────────────────────────────────────────

with st.sidebar:
    # Branding
    st.markdown("""
    <div class="sidebar-logo">
        <h1>🧠 DocMind</h1>
    </div>
    <div class="sidebar-tagline">Grounded RAG Document Intelligence</div>
    """, unsafe_allow_html=True)

    st.markdown("---")

    # File upload
    st.markdown("### 📁 Upload Documents")
    uploaded_files = st.file_uploader(
        "Drop PDF, DOCX, or TXT files",
        type=["pdf", "docx", "txt"],
        accept_multiple_files=True,
        key="file_uploader",
        help=f"Max {CONFIG.ui.max_documents} documents, {CONFIG.ui.max_upload_mb}MB each",
    )

    if uploaded_files:
        if len(uploaded_files) > CONFIG.ui.max_documents:
            st.warning(f"⚠️ Maximum {CONFIG.ui.max_documents} documents allowed.")
            uploaded_files = uploaded_files[:CONFIG.ui.max_documents]

        for idx, file in enumerate(uploaded_files):
            doc_id = generate_doc_id(file.name, file.read())
            file.seek(0)  # Reset after reading
            if doc_id not in st.session_state.doc_index_map:
                ingest_document(file, doc_color_index=idx)

    # Show loaded documents
    if st.session_state.doc_info:
        st.markdown("### 📄 Loaded Documents")
        for info in st.session_state.doc_info:
            render_document_status(
                info["name"], info["chunk_count"],
                info["page_count"], info["color_idx"],
            )

    st.markdown("---")

    # Settings
    st.markdown("### ⚙️ Settings")
    st.session_state.show_debug = st.toggle(
        "Show retrieval debug", value=st.session_state.show_debug
    )

    # Actions
    st.markdown("---")
    col1, col2 = st.columns(2)
    with col1:
        if st.button("🗑️ Clear Chat", use_container_width=True):
            st.session_state.chat_history = []
            st.rerun()
    with col2:
        if st.session_state.chat_history:
            chat_text = export_chat_as_text()
            st.download_button(
                "💾 Export",
                data=chat_text,
                file_name="docmind_chat.txt",
                mime="text/plain",
                use_container_width=True,
            )

    # Reset all
    if st.button("🔄 Reset Everything", use_container_width=True, type="secondary"):
        for key in list(st.session_state.keys()):
            del st.session_state[key]
        try:
            qdrant_client = init_qdrant_client()
            clear_collection(qdrant_client)
        except Exception:
            pass
        st.rerun()


# ── Main area ──────────────────────────────────────────────────────

# Main branding header
st.markdown("""
<div style="text-align: center; margin-bottom: 2rem; padding-top: 1rem;">
    <h1 style="font-size: 2.8rem; font-weight: 700; margin-bottom: 0.2rem; background: linear-gradient(135deg, #818CF8, #C084FC); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">🧠 DocMind</h1>
    <p style="color: #94A3B8; font-size: 1.05rem; letter-spacing: 0.02em;">Enterprise-Grade Document Intelligence with Grounded RAG</p>
</div>
""", unsafe_allow_html=True)

# Tabs
if st.session_state.doc_info:
    # Render top-level metrics dashboard
    total_docs = len(st.session_state.doc_info)
    total_chunks = len(st.session_state.all_chunks)
    render_dashboard_metrics(total_docs, total_chunks)

    tab_qa, tab_summary, tab_compare = st.tabs([
        "💬 Q&A", "📊 Summaries", "📑 Compare Documents"
    ])
else:
    tab_qa = st.container()
    tab_summary = None
    tab_compare = None


# ── Q&A Tab ────────────────────────────────────────────────────────

with tab_qa:
    if not st.session_state.doc_info:
        render_empty_state()
    else:
        # Display chat history
        for msg in st.session_state.chat_history:
            render_chat_message(
                role=msg["role"],
                content=msg["content"],
                grounding_result=msg.get("grounding"),
                sources=msg.get("sources"),
                doc_index_map=st.session_state.doc_index_map,
            )
            if msg["role"] == "bot" and msg.get("stats") and st.session_state.show_debug:
                render_retrieval_stats(msg["stats"])

        # Query input
        query = st.chat_input("Ask a question about your documents...")

        if query:
            # Add user message to history
            st.session_state.chat_history.append({
                "role": "user",
                "content": query,
            })
            render_chat_message("user", query)

            # Run pipeline
            with st.spinner("🔍 Searching documents & verifying claims..."):
                try:
                    result = run_qa_pipeline(query)
                except Exception as e:
                    result = {
                        "answer": f"🚨 **System Error:** {str(e)}",
                        "is_refused": True,
                        "grounding": None,
                        "sources": [],
                        "stats": None
                    }

            # Add bot response to history
            bot_msg = {
                "role": "bot",
                "content": result["answer"],
                "grounding": result.get("grounding"),
                "sources": result.get("sources", []),
                "stats": result.get("stats"),
            }
            st.session_state.chat_history.append(bot_msg)

            # Render bot response
            render_chat_message(
                role="bot",
                content=result["answer"],
                grounding_result=result.get("grounding"),
                sources=result.get("sources"),
                doc_index_map=st.session_state.doc_index_map,
            )

            if result.get("stats") and st.session_state.show_debug:
                render_retrieval_stats(result["stats"])

            # Trim history
            max_history = CONFIG.ui.max_chat_history * 2  # pairs
            if len(st.session_state.chat_history) > max_history:
                st.session_state.chat_history = st.session_state.chat_history[-max_history:]


# ── Summary Tab ────────────────────────────────────────────────────

if tab_summary:
    with tab_summary:
        if not st.session_state.doc_info:
            st.info("Upload a document first to generate summaries.")
        else:
            sum_tab1, sum_tab2, sum_tab3 = st.tabs([
                "📝 Quick Summary", "📑 Structured Summary", "🎯 Key Points"
            ])

            with sum_tab1:
                if st.button("Generate Quick Summary", key="btn_quick", type="primary"):
                    with st.spinner("Generating summary..."):
                        summary = generate_quick_summary(st.session_state.all_chunks)
                    st.markdown(summary)

            with sum_tab2:
                if st.button("Generate Structured Summary", key="btn_structured", type="primary"):
                    with st.spinner("Generating structured summary..."):
                        summary = generate_structured_summary(st.session_state.all_chunks)
                    st.markdown(summary)

            with sum_tab3:
                if st.button("Extract Key Points", key="btn_keypoints", type="primary"):
                    with st.spinner("Extracting key points..."):
                        embed_model = load_bge_model()
                        points = extract_key_points(
                            st.session_state.all_chunks,
                            embed_model=embed_model,
                        )
                    if points:
                        for pt in points:
                            page_ref = f"PAGE: {pt.page_ref}" if pt.page_ref else ""
                            render_keypoint_card(pt.text, page_ref)
                    else:
                        st.info("No key points could be extracted.")


# ── Compare Tab ────────────────────────────────────────────────────

if tab_compare:
    with tab_compare:
        if len(st.session_state.doc_info) < 2:
            st.info("Upload at least 2 documents to enable comparison.")
        else:
            st.markdown("### 📑 Document Comparison")
            doc_names = [d["name"] for d in st.session_state.doc_info]
            st.markdown(f"Comparing: **{' vs '.join(doc_names)}**")

            if st.button("🔄 Generate Comparison", key="btn_compare", type="primary"):
                with st.spinner("Comparing documents..."):
                    # Build comparison prompt
                    doc_summaries = []
                    for info in st.session_state.doc_info:
                        doc_chunks = [
                            c for c in st.session_state.all_chunks
                            if c.doc_id == info["doc_id"]
                        ]
                        # Use first 3 chunks from each doc
                        excerpt = "\n".join(c.text for c in doc_chunks[:3])
                        doc_summaries.append(f"**{info['name']}**:\n{excerpt}")

                    combined = "\n\n---\n\n".join(doc_summaries)
                    prompt = (
                        "Compare these documents in a structured table format. "
                        "Include columns for: Aspect, "
                        + ", ".join(doc_names)
                        + ". Cover the main topics, scope, key differences, and similarities."
                    )

                    from pipeline.llm import generate_summary
                    comparison = generate_summary(combined, prompt)

                st.markdown(comparison)