"""
rag_engine.py — Multimodal RAG Engine with Multi-File Support, Reranking & Memory
Supports: PDF, TXT, DOCX, CSV, XLSX, Images (JPG/PNG/WEBP)
Features: Up to 5 simultaneous files, per-file removal, additive indexing
Memory: sliding window of last 6 exchanges

KEY CHANGES (v5 — Cross-Encoder Reranking):
  1. Cross-encoder reranker (ms-marco-MiniLM-L-6-v2) scores every retrieved
     chunk for true semantic relevance to the query — not just embedding distance.
  2. Over-fetches 12+ candidates from the vectorstore, then reranks to pick
     the top-k most relevant chunks for the LLM context.
  3. Graceful fallback — if the reranker fails to load, uses original order.

Previous features preserved:
  - Additive indexing, per-file removal, MAX_FILES=5
  - Multi-file aware generation, cross-doc coverage
  - OCR, color analysis, BLIP raw bytes, VLM descriptions for images
  - Conversation memory (6-exchange sliding window)
"""

import os
import re
import io
import json
import time
import base64
import hashlib
import tempfile
import requests
import logging
from pathlib import Path
from typing import Tuple, List, Optional, Dict
from collections import Counter

from chromadb.config import Settings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.document_loaders import PyPDFLoader
from langchain.schema import Document

import monitor

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ── Constants ────────────────────────────────────────────────────────────────
EMBED_MODEL     = "all-MiniLM-L6-v2"
RERANK_MODEL    = "cross-encoder/ms-marco-MiniLM-L-6-v2"  # ~80MB, CPU-friendly
CHUNK_SIZE      = 600
CHUNK_OVERLAP   = 100
TOP_K           = 4       # final chunks sent to LLM after reranking
RERANK_FETCH_K  = 12      # over-fetch this many candidates for reranking
COLLECTION_NAME = "docmind_multimodal"
HF_API_URL      = "https://router.huggingface.co/v1/chat/completions"
MEMORY_WINDOW   = 6   # number of past Q&A pairs to keep
MAX_FILES        = 5   # maximum simultaneous documents

SUPPORTED_EXTENSIONS = {
    ".pdf", ".txt",
    ".docx", ".doc",
    ".csv", ".xlsx", ".xls",
    ".jpg", ".jpeg", ".png", ".webp",
}

CANDIDATE_MODELS = [
    "meta-llama/Llama-3.1-8B-Instruct:cerebras",
    "meta-llama/Llama-3.3-70B-Instruct:cerebras",
    "mistralai/Mistral-7B-Instruct-v0.3:fireworks-ai",
    "HuggingFaceTB/SmolLM3-3B:hf-inference",
]

# Vision-language models for detailed image descriptions (order matters)
VLM_MODELS = [
    "Qwen/Qwen2.5-VL-7B-Instruct",
    "meta-llama/Llama-3.2-11B-Vision-Instruct",
]


def get_suffix(name: str) -> str:
    return Path(name).suffix.lower() or ".txt"


def _classify_color(r: int, g: int, b: int) -> str:
    """Classify an RGB pixel into a human-readable color name."""
    if r > 220 and g > 220 and b > 220:
        return "white"
    if r < 35 and g < 35 and b < 35:
        return "black"
    if max(r, g, b) - min(r, g, b) < 30:
        if r > 170:
            return "light gray"
        if r > 100:
            return "gray"
        return "dark gray"
    if r > 180 and g > 180 and b < 100:
        return "yellow"
    if r > 180 and g > 100 and g < 180 and b < 80:
        return "orange"
    if r > 150 and g < 80 and b < 80:
        return "red"
    if r > 150 and g < 100 and b > 100:
        return "pink" if r > 200 else "purple"
    if g > 150 and r < 100 and b < 100:
        return "green"
    if g > 120 and r < 80 and b < 80:
        return "dark green"
    if b > 150 and r < 100 and g < 100:
        return "blue"
    if b > 150 and g > 100 and r < 100:
        return "cyan" if g > 150 else "teal"
    if r > 100 and g > 100 and b < 80:
        return "olive"
    if r > 150 and g < 80 and b > 150:
        return "magenta"
    if g >= r and g >= b:
        return "green"
    if r >= g and r >= b:
        return "red"
    return "blue"


class RAGEngine:
    def __init__(self):
        self._embeddings:  Optional[HuggingFaceEmbeddings] = None
        self._reranker = None  # lazy-loaded cross-encoder
        self._vectorstore: Optional[Chroma] = None
        self._splitter = RecursiveCharacterTextSplitter(
            chunk_size=CHUNK_SIZE,
            chunk_overlap=CHUNK_OVERLAP,
            separators=["\n\n", "\n", ". ", " ", ""],
        )
        self._memory: List[dict] = []
        self._documents: Dict[str, dict] = {}  # {filename: {chunk_count, chunk_ids, type}}
        monitor.log_startup()

    @property
    def embeddings(self):
        if self._embeddings is None:
            logger.info("Loading embedding model...")
            self._embeddings = HuggingFaceEmbeddings(
                model_name=EMBED_MODEL,
                model_kwargs={"device": "cpu"},
                encode_kwargs={"normalize_embeddings": True},
            )
        return self._embeddings

    @property
    def reranker(self):
        """Lazy-load the cross-encoder reranker (~80MB, CPU-friendly)."""
        if self._reranker is None:
            try:
                from sentence_transformers import CrossEncoder
                logger.info(f"Loading reranker model: {RERANK_MODEL}...")
                self._reranker = CrossEncoder(RERANK_MODEL, max_length=512)
                logger.info("Reranker loaded successfully.")
            except Exception as e:
                logger.warning(f"Failed to load reranker: {e}. Will skip reranking.")
                self._reranker = False  # sentinel: don't retry
        return self._reranker if self._reranker is not False else None

    def _rerank_documents(self, question: str, docs: List[Document], top_k: int) -> List[Document]:
        """Score and reorder documents using the cross-encoder reranker."""
        if not docs:
            return docs

        ranker = self.reranker
        if ranker is None:
            # Reranker unavailable — fall back to original order
            logger.info("Reranker not available, using original retrieval order.")
            return docs[:top_k]

        # Build query-document pairs for the cross-encoder
        pairs = [(question, doc.page_content) for doc in docs]

        try:
            scores = ranker.predict(pairs)

            # Pair each doc with its rerank score
            scored = list(zip(docs, scores))
            scored.sort(key=lambda x: x[1], reverse=True)

            reranked = [doc for doc, score in scored[:top_k]]

            # Log the reranking effect
            original_sources = [d.metadata.get("source", "?")[:30] for d in docs[:top_k]]
            reranked_sources = [d.metadata.get("source", "?")[:30] for d in reranked]
            top_scores = [f"{s:.3f}" for _, s in scored[:top_k]]
            logger.info(
                f"Reranked {len(docs)} candidates → top {top_k}. "
                f"Scores: {top_scores}. "
                f"Before: {original_sources}, After: {reranked_sources}"
            )

            return reranked
        except Exception as e:
            logger.warning(f"Reranking failed: {e}. Using original order.")
            return docs[:top_k]

    # ── Memory ───────────────────────────────────────────────────────────────

    def clear_memory(self):
        self._memory = []

    def add_to_memory(self, question: str, answer: str):
        self._memory.append({"role": "user",      "content": question})
        self._memory.append({"role": "assistant",  "content": answer})
        max_msgs = MEMORY_WINDOW * 2
        if len(self._memory) > max_msgs:
            self._memory = self._memory[-max_msgs:]

    def get_memory_messages(self) -> List[dict]:
        return self._memory.copy()

    def get_memory_count(self) -> int:
        return len(self._memory) // 2

    # ── Document Management ──────────────────────────────────────────────────

    def get_documents(self) -> List[dict]:
        """Return list of all loaded documents with their info."""
        return [
            {
                "name": name,
                "type": info["type"],
                "chunk_count": info["chunk_count"],
            }
            for name, info in self._documents.items()
        ]

    def get_total_chunks(self) -> int:
        """Total chunks across all loaded files."""
        return sum(info["chunk_count"] for info in self._documents.values())

    def get_file_count(self) -> int:
        return len(self._documents)

    def remove_file(self, filename: str) -> bool:
        """Remove a specific file's chunks from the vectorstore."""
        if filename not in self._documents:
            logger.warning(f"Cannot remove '{filename}' — not found in loaded documents.")
            return False

        chunk_ids = self._documents[filename]["chunk_ids"]

        if self._vectorstore and chunk_ids:
            try:
                self._vectorstore._collection.delete(ids=chunk_ids)
                logger.info(f"Removed {len(chunk_ids)} chunks for '{filename}'")
            except Exception as e:
                logger.warning(f"Failed to delete chunks for '{filename}': {e}")

        del self._documents[filename]

        # If no documents left, clean up the vectorstore entirely
        if not self._documents:
            self._vectorstore = None
            logger.info("All documents removed — vectorstore cleared.")

        return True

    def reset(self):
        """Reset everything — all documents, vectorstore, and memory."""
        self._documents = {}
        if self._vectorstore:
            try:
                self._vectorstore._client.reset()
            except Exception:
                pass
        self._vectorstore = None
        self._memory = []
        logger.info("Full reset: all documents, vectorstore, and memory cleared.")

    # ── Ingestion ────────────────────────────────────────────────────────────

    def ingest_file(self, uploaded_file) -> int:
        """Accept FastAPI UploadFile or Streamlit UploadedFile. Additive indexing."""
        t0 = time.time()
        filename = getattr(uploaded_file, "name", None) or getattr(uploaded_file, "filename", "file")
        suffix   = get_suffix(filename)
        error    = ""
        chunks   = 0

        if suffix not in SUPPORTED_EXTENSIONS:
            raise ValueError(
                f"Unsupported: {suffix}. Supported: {', '.join(sorted(SUPPORTED_EXTENSIONS))}"
            )

        # Enforce file limit (replacement of same name doesn't count as new)
        if filename not in self._documents and len(self._documents) >= MAX_FILES:
            raise ValueError(
                f"Maximum {MAX_FILES} files reached. Remove a file before uploading more."
            )

        # If same filename exists, remove old version first (replacement)
        if filename in self._documents:
            logger.info(f"Replacing existing file: {filename}")
            self.remove_file(filename)

        try:
            if hasattr(uploaded_file, "read"):
                data = uploaded_file.read()
                if hasattr(uploaded_file, "seek"):
                    uploaded_file.seek(0)
            else:
                data = uploaded_file.file.read()

            docs   = self._route(data, filename, suffix)
            chunks = self._index(docs, filename)
        except Exception as e:
            error = str(e)
            logger.error(f"Ingestion error: {e}")
            raise
        finally:
            monitor.log_ingestion(filename, chunks, (time.time() - t0) * 1000, error)
        return chunks

    def ingest_path(self, path: str, name: str = "") -> int:
        """Ingest a file from a local path. Also additive."""
        filename = name or Path(path).name
        suffix   = get_suffix(filename)

        if filename not in self._documents and len(self._documents) >= MAX_FILES:
            raise ValueError(
                f"Maximum {MAX_FILES} files reached. Remove a file before uploading more."
            )

        if filename in self._documents:
            self.remove_file(filename)

        with open(path, "rb") as f:
            data = f.read()
        docs   = self._route(data, filename, suffix)
        chunks = self._index(docs, filename)
        return chunks

    def _route(self, data: bytes, filename: str, suffix: str) -> List[Document]:
        if suffix == ".pdf":
            return self._load_pdf(data, filename)
        elif suffix == ".txt":
            return self._load_text(data, filename)
        elif suffix in {".docx", ".doc"}:
            return self._load_docx(data, filename)
        elif suffix == ".csv":
            return self._load_csv(data, filename)
        elif suffix in {".xlsx", ".xls"}:
            return self._load_excel(data, filename)
        elif suffix in {".jpg", ".jpeg", ".png", ".webp"}:
            return self._load_image(data, filename)
        raise ValueError(f"No loader for {suffix}")

    # ── Loaders ──────────────────────────────────────────────────────────────

    def _load_pdf(self, data: bytes, filename: str) -> List[Document]:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
            tmp.write(data)
            tmp_path = tmp.name
        try:
            docs = PyPDFLoader(tmp_path).load()
            for doc in docs:
                doc.metadata.update({"source": filename, "type": "pdf"})
            return docs
        finally:
            os.unlink(tmp_path)

    def _load_text(self, data: bytes, filename: str) -> List[Document]:
        return [Document(
            page_content=data.decode("utf-8", errors="replace"),
            metadata={"source": filename, "type": "text"}
        )]

    def _load_docx(self, data: bytes, filename: str) -> List[Document]:
        text = ""
        try:
            import docx2txt
            with tempfile.NamedTemporaryFile(delete=False, suffix=".docx") as tmp:
                tmp.write(data)
                tmp_path = tmp.name
            try:
                text = docx2txt.process(tmp_path)
            finally:
                os.unlink(tmp_path)
        except ImportError:
            logger.warning("docx2txt not installed — falling back to raw text extraction")
            text = data.decode("utf-8", errors="replace")
        except Exception as e:
            logger.warning(f"docx2txt failed ({e}) — falling back to raw text extraction")
            text = data.decode("utf-8", errors="replace")

        if not text or not text.strip():
            text = f"[Document: {filename}] — Could not extract text content."

        return [Document(page_content=text, metadata={"source": filename, "type": "docx"})]

    def _load_csv(self, data: bytes, filename: str) -> List[Document]:
        import pandas as pd
        df   = pd.read_csv(io.BytesIO(data))
        docs = []

        summary = (
            f"File: {filename}\n"
            f"Shape: {df.shape[0]} rows × {df.shape[1]} columns\n"
            f"Columns: {', '.join(df.columns.tolist())}\n\n"
            f"First 10 rows:\n{df.head(10).to_string(index=False)}"
        )
        docs.append(Document(page_content=summary, metadata={"source": filename, "type": "csv_summary"}))

        try:
            stats = "Statistical summary:\n" + df.describe(include="all").to_string()
            docs.append(Document(page_content=stats, metadata={"source": filename, "type": "csv_stats"}))
        except Exception as e:
            logger.warning(f"CSV stats failed: {e}")

        try:
            for i in range(0, min(len(df), 500), 50):
                chunk = f"Rows {i}–{i+50}:\n{df.iloc[i:i+50].to_string(index=False)}"
                docs.append(Document(page_content=chunk, metadata={"source": filename, "type": "csv_rows"}))
        except Exception as e:
            logger.warning(f"CSV row chunking failed: {e}")

        return docs

    def _load_excel(self, data: bytes, filename: str) -> List[Document]:
        import pandas as pd
        xl   = pd.ExcelFile(io.BytesIO(data))
        docs = []
        for sheet in xl.sheet_names:
            try:
                df = xl.parse(sheet)
                text = (
                    f"Sheet: {sheet} | {df.shape[0]} rows × {df.shape[1]} cols\n"
                    f"Columns: {', '.join(str(c) for c in df.columns)}\n\n"
                    f"{df.head(10).to_string(index=False)}"
                )
                docs.append(Document(page_content=text, metadata={
                    "source": filename, "type": "excel", "sheet": sheet
                }))
            except Exception as e:
                logger.warning(f"Excel sheet '{sheet}' failed: {e}")
        return docs

    # ══════════════════════════════════════════════════════════════════════════
    # IMAGE LOADING — v3: OCR + Color Analysis + BLIP + VLM
    # ══════════════════════════════════════════════════════════════════════════

    def _load_image(self, data: bytes, filename: str) -> List[Document]:
        logger.info(f"Processing image: {filename}")

        ocr_text        = self._ocr_image(data, filename)
        color_info      = self._analyze_colors(data, filename)
        blip_caption    = self._caption_image_blip(data, filename)
        vlm_description = self._describe_image_with_vlm(data, filename, blip_caption, ocr_text)

        sections = [f"Image file: {filename}", ""]

        if ocr_text:
            sections.append("=== TEXT FOUND IN IMAGE (OCR) ===")
            sections.append(ocr_text)
            sections.append("")

        if color_info:
            sections.append("=== COLOR ANALYSIS ===")
            sections.append(color_info)
            sections.append("")

        sections.append("=== SHORT CAPTION ===")
        sections.append(blip_caption)
        sections.append("")

        sections.append("=== DETAILED VISUAL DESCRIPTION ===")
        sections.append(vlm_description)
        sections.append("")

        summary_parts = [f"This image ({filename})"]
        if ocr_text:
            summary_parts.append(f'contains the text: "{ocr_text}"')
        if color_info:
            summary_parts.append(f"has {color_info.lower()}")
        summary_parts.append(f"and shows: {blip_caption}")

        sections.append("=== SUMMARY ===")
        sections.append(". ".join(summary_parts) + ".")
        sections.append(f"Detailed: {vlm_description}")

        text = "\n".join(sections)
        logger.info(f"Image document length: {len(text)} chars "
                     f"(OCR: {len(ocr_text)} chars, colors: {bool(color_info)}, "
                     f"BLIP: {len(blip_caption)} chars, VLM: {len(vlm_description)} chars)")

        return [Document(
            page_content=text,
            metadata={
                "source": filename,
                "type": "image",
                "ocr_text": ocr_text[:500] if ocr_text else "",
                "caption": blip_caption,
                "colors": color_info,
            }
        )]

    # ── OCR ──────────────────────────────────────────────────────────────────

    def _ocr_image(self, data: bytes, filename: str) -> str:
        try:
            import pytesseract
            from PIL import Image

            img = Image.open(io.BytesIO(data))
            if img.mode not in ("RGB", "L"):
                img = img.convert("RGB")

            text = pytesseract.image_to_string(img).strip()

            if not text or len(text) < 2:
                gray = img.convert("L")
                w, h = gray.size
                if w < 1000 or h < 1000:
                    scale = max(1000 / w, 1000 / h, 1)
                    gray = gray.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
                text = pytesseract.image_to_string(gray).strip()

            if text:
                text = re.sub(r'\n{3,}', '\n\n', text).strip()
                logger.info(f"OCR extracted text from {filename}: '{text[:100]}...'")
                return text
            else:
                logger.info(f"OCR found no text in {filename}")
                return ""

        except ImportError:
            logger.warning("pytesseract not installed — skipping OCR.")
            return self._ocr_image_api(data, filename)
        except Exception as e:
            logger.warning(f"OCR failed for {filename}: {e}")
            return self._ocr_image_api(data, filename)

    def _ocr_image_api(self, data: bytes, filename: str) -> str:
        hf_token = os.environ.get("HF_TOKEN", "")
        if not hf_token:
            return ""

        ocr_models = [
            "microsoft/trocr-large-printed",
            "microsoft/trocr-base-printed",
        ]

        for model_id in ocr_models:
            try:
                resp = requests.post(
                    f"https://api-inference.huggingface.co/models/{model_id}",
                    headers={"Authorization": f"Bearer {hf_token}"},
                    data=data,
                    timeout=30,
                )
                if resp.status_code == 200:
                    result = resp.json()
                    if isinstance(result, list) and result:
                        text = result[0].get("generated_text", "").strip()
                        if text:
                            return text
            except Exception as e:
                logger.warning(f"OCR API failed ({model_id}): {e}")
        return ""

    # ── Color Analysis ───────────────────────────────────────────────────────

    def _analyze_colors(self, data: bytes, filename: str) -> str:
        try:
            from PIL import Image

            img = Image.open(io.BytesIO(data)).convert("RGB")
            img_small = img.resize((80, 80), Image.LANCZOS)
            pixels = list(img_small.getdata())

            color_names = [_classify_color(r, g, b) for r, g, b in pixels]
            counter = Counter(color_names)
            total = len(pixels)

            dominant = [
                (name, count / total * 100)
                for name, count in counter.most_common(5)
                if count / total * 100 >= 3
            ]

            if not dominant:
                return ""

            bg_color = dominant[0][0]
            result = "dominant colors: " + ", ".join(
                f"{name} ({pct:.0f}%)" for name, pct in dominant
            )
            result += f". The background appears to be {bg_color}."
            logger.info(f"Color analysis for {filename}: {result}")
            return result

        except Exception as e:
            logger.warning(f"Color analysis failed for {filename}: {e}")
            return ""

    # ── BLIP Caption ─────────────────────────────────────────────────────────

    def _caption_image_blip(self, data: bytes, filename: str) -> str:
        hf_token = os.environ.get("HF_TOKEN", "")
        if not hf_token:
            return f"[Image: {filename}] — Add HF_TOKEN to enable captioning."

        caption_models = [
            "Salesforce/blip-image-captioning-large",
            "Salesforce/blip-image-captioning-base",
            "nlpconnect/vit-gpt2-image-captioning",
        ]

        for model_id in caption_models:
            try:
                resp = requests.post(
                    f"https://api-inference.huggingface.co/models/{model_id}",
                    headers={"Authorization": f"Bearer {hf_token}"},
                    data=data,  # raw bytes, NOT json
                    timeout=30,
                )
                if resp.status_code == 200:
                    result = resp.json()
                    if isinstance(result, list) and result:
                        caption = result[0].get("generated_text", "")
                        if caption:
                            logger.info(f"BLIP caption ({model_id}): {caption[:80]}")
                            return caption
                elif resp.status_code == 503:
                    logger.info(f"{model_id} is loading, waiting 10s...")
                    time.sleep(10)
                    resp2 = requests.post(
                        f"https://api-inference.huggingface.co/models/{model_id}",
                        headers={"Authorization": f"Bearer {hf_token}"},
                        data=data,
                        timeout=45,
                    )
                    if resp2.status_code == 200:
                        result = resp2.json()
                        if isinstance(result, list) and result:
                            caption = result[0].get("generated_text", "")
                            if caption:
                                return caption
                else:
                    logger.warning(f"BLIP {model_id}: {resp.status_code}: {resp.text[:100]}")
            except Exception as e:
                logger.warning(f"BLIP caption failed ({model_id}): {e}")
                continue

        return f"An image named {filename} was uploaded."

    # ── VLM Detailed Description ─────────────────────────────────────────────

    def _describe_image_with_vlm(self, data: bytes, filename: str,
                                  short_caption: str, ocr_text: str) -> str:
        hf_token = os.environ.get("HF_TOKEN", "")
        if not hf_token:
            return short_caption

        mime = "image/jpeg"
        if data[:8] == b'\x89PNG\r\n\x1a\n':
            mime = "image/png"
        elif data[:4] == b'RIFF' and data[8:12] == b'WEBP':
            mime = "image/webp"

        b64_image = base64.b64encode(data).decode("utf-8")
        image_url = f"data:{mime};base64,{b64_image}"

        headers = {
            "Authorization": f"Bearer {hf_token}",
            "Content-Type": "application/json",
        }

        ocr_hint = ""
        if ocr_text:
            ocr_hint = (
                f"\n\nNote: An OCR system already detected this text in the image: "
                f'"{ocr_text}". Please confirm or correct this text reading.'
            )

        prompt_text = (
            "Analyze this image thoroughly and provide a detailed description. "
            "You MUST address ALL of the following:\n\n"
            "1. TEXT: Read and transcribe ALL text visible in the image, "
            "character by character, word by word. Include any titles, labels, "
            "captions, watermarks, or writing of any kind. If there is text, "
            "quote it exactly.\n\n"
            "2. COLORS: What are the exact colors visible? What is the "
            "background color? What color is the text (if any)? List all "
            "significant colors.\n\n"
            "3. OBJECTS & LAYOUT: What objects, shapes, people, or elements "
            "are in the image? Where are they positioned?\n\n"
            "4. CONTEXT: What type of image is this (photo, screenshot, "
            "diagram, logo, meme, sign, document, etc.)?\n\n"
            "Be specific and factual. Do not guess or make assumptions about "
            "things you cannot see."
            f"{ocr_hint}"
        )

        for model_id in VLM_MODELS:
            try:
                logger.info(f"Trying VLM description with {model_id}...")
                payload = {
                    "model": model_id,
                    "messages": [
                        {
                            "role": "user",
                            "content": [
                                {
                                    "type": "image_url",
                                    "image_url": {"url": image_url},
                                },
                                {
                                    "type": "text",
                                    "text": prompt_text,
                                },
                            ],
                        }
                    ],
                    "max_tokens": 700,
                    "temperature": 0.1,
                    "stream": False,
                }

                resp = requests.post(
                    HF_API_URL,
                    headers=headers,
                    data=json.dumps(payload),
                    timeout=60,
                )

                if resp.status_code == 200:
                    raw = resp.json()["choices"][0]["message"]["content"].strip()
                    description = _strip_thinking(raw)
                    if description and len(description) > 20:
                        logger.info(f"VLM description ({model_id}): {description[:100]}...")
                        return description
                else:
                    logger.warning(f"VLM {model_id}: {resp.status_code}: {resp.text[:150]}")
            except Exception as e:
                logger.warning(f"VLM description failed ({model_id}): {e}")
                continue

        return self._expand_caption_with_llm(short_caption, ocr_text, filename)

    def _expand_caption_with_llm(self, caption: str, ocr_text: str, filename: str) -> str:
        hf_token = os.environ.get("HF_TOKEN", "")
        if not hf_token:
            parts = [caption]
            if ocr_text:
                parts.append(f'Text found in image: "{ocr_text}"')
            return " ".join(parts)

        headers = {
            "Authorization": f"Bearer {hf_token}",
            "Content-Type": "application/json",
        }

        ocr_section = ""
        if ocr_text:
            ocr_section = f'\nOCR text extracted from the image: "{ocr_text}"'

        messages = [
            {
                "role": "system",
                "content": (
                    "You are an image description assistant. You are given information "
                    "extracted from an image (a short AI caption and OCR text). "
                    "Combine this information into a clear, factual description. "
                    "If OCR text was found, make sure to include it prominently. "
                    "Do NOT invent details that aren't supported by the provided info."
                ),
            },
            {
                "role": "user",
                "content": (
                    f"Image file: '{filename}'\n"
                    f"AI caption: \"{caption}\"\n"
                    f"{ocr_section}\n\n"
                    f"Please provide a consolidated description of this image."
                ),
            },
        ]

        for model_id in CANDIDATE_MODELS:
            try:
                resp = requests.post(
                    HF_API_URL,
                    headers=headers,
                    data=json.dumps({
                        "model": model_id,
                        "messages": messages,
                        "max_tokens": 400,
                        "temperature": 0.2,
                        "stream": False,
                    }),
                    timeout=45,
                )
                if resp.status_code == 200:
                    raw = resp.json()["choices"][0]["message"]["content"].strip()
                    expanded = _strip_thinking(raw)
                    if expanded and len(expanded) > 30:
                        return expanded
            except Exception as e:
                logger.warning(f"Caption expansion failed ({model_id}): {e}")
                continue

        parts = [caption]
        if ocr_text:
            parts.append(f'Text visible in image: "{ocr_text}"')
        return " ".join(parts)

    # ══════════════════════════════════════════════════════════════════════════
    # INDEXING — ADDITIVE (does NOT destroy existing data)
    # ══════════════════════════════════════════════════════════════════════════

    def _index(self, docs: List[Document], filename: str) -> int:
        chunks = self._splitter.split_documents(docs)

        if not chunks:
            logger.warning(f"No chunks produced from {filename}")
            return 0

        # Generate unique, stable chunk IDs for this file
        safe_name = re.sub(r'[^a-zA-Z0-9_.-]', '_', filename)
        name_hash = hashlib.md5(filename.encode()).hexdigest()[:8]
        chunk_ids = [f"{safe_name}_{name_hash}__chunk__{i}" for i in range(len(chunks))]

        # Create vectorstore if this is the first file
        if self._vectorstore is None:
            self._vectorstore = Chroma(
                collection_name=COLLECTION_NAME,
                embedding_function=self.embeddings,
                client_settings=Settings(anonymized_telemetry=False),
            )

        # Add chunks to the existing vectorstore (additive!)
        texts     = [c.page_content for c in chunks]
        metadatas = [c.metadata for c in chunks]
        self._vectorstore.add_texts(texts=texts, metadatas=metadatas, ids=chunk_ids)

        # Track this file
        self._documents[filename] = {
            "chunk_count": len(chunks),
            "chunk_ids":   chunk_ids,
            "type":        get_suffix(filename),
        }

        logger.info(
            f"Indexed {len(chunks)} chunks from '{filename}' "
            f"(total files: {len(self._documents)}, total chunks: {self.get_total_chunks()})"
        )
        return len(chunks)

    # ── Query ────────────────────────────────────────────────────────────────

    def query(self, question: str) -> Tuple[str, List[str]]:
        if not self._documents or self._vectorstore is None:
            return "Please upload a document first.", []

        t0 = time.time()
        error = answer = model_used = ""
        sources = []

        try:
            # ── Step 1: Over-fetch candidates ────────────────────────────────
            # Retrieve more candidates than needed so the reranker can pick
            # the truly relevant ones. Scale with number of loaded files.
            num_files = len(self._documents)
            fetch_k   = max(RERANK_FETCH_K, RERANK_FETCH_K + (num_files - 1) * 2)
            initial_k = fetch_k  # MMR will return this many diverse candidates

            retriever = self._vectorstore.as_retriever(
                search_type="mmr",
                search_kwargs={"k": initial_k, "fetch_k": fetch_k * 2},
            )
            candidate_docs = retriever.invoke(question)

            # ── Step 2: Rerank with cross-encoder ────────────────────────────
            # The cross-encoder scores each (query, chunk) pair for true
            # semantic relevance — much more accurate than embedding distance.
            final_k = min(TOP_K + num_files - 1, 6)
            docs = self._rerank_documents(question, candidate_docs, top_k=final_k)

            context = "\n\n---\n\n".join(
                f"[Chunk {i+1} | source: {d.metadata.get('source', '?')} | type: {d.metadata.get('type','text')}]\n{d.page_content}"
                for i, d in enumerate(docs)
            )
            sources = list({d.metadata.get("source", "Document") for d in docs})
            answer, model_used = self._generate(question, context)
            self.add_to_memory(question, answer)

        except Exception as e:
            error  = str(e)
            answer = f"Error: {error}"
            logger.error(f"Query error: {e}")
        finally:
            monitor.log_query(question, answer, sources, (time.time() - t0) * 1000, model_used, TOP_K, error)

        return answer, sources

    # ── LLM ──────────────────────────────────────────────────────────────────

    def _generate(self, question: str, context: str) -> Tuple[str, str]:
        hf_token = os.environ.get("HF_TOKEN", "")
        if not hf_token:
            return (
                "HF_TOKEN not set. Add it as a Secret in Space Settings.\n\n"
                "Best matching excerpt:\n\n" + _extract_best(question, context),
                "none"
            )

        # ── Build doc-type hints from ALL loaded files ────────────────────────
        loaded_types = set(info["type"] for info in self._documents.values())
        all_names    = list(self._documents.keys())

        hints = []
        image_types = {".jpg", ".jpeg", ".png", ".webp"}
        table_types = {".csv", ".xlsx", ".xls"}

        if loaded_types & image_types:
            hints.append(
                "Some documents are IMAGES. Their context contains:\n"
                "  - OCR-extracted text (actual text visible in the image)\n"
                "  - Color analysis (dominant colors detected)\n"
                "  - AI-generated visual descriptions\n"
                "When asked about text in an image, refer to the OCR section. "
                "When asked about colors, refer to the color analysis. "
                "When asked what an image shows, use the descriptions. "
                "Be specific and quote the actual text/colors from the context."
            )
        if loaded_types & table_types:
            hints.append(
                "Some documents are tabular data (spreadsheet/CSV). "
                "Refer to column names and values precisely."
            )
        if loaded_types & {".docx", ".doc"}:
            hints.append("Some documents are Word documents.")

        doc_type_hint = "\n".join(hints)

        # ── File list for the system prompt ───────────────────────────────────
        if len(all_names) == 1:
            files_str = f"You are analyzing: '{all_names[0]}'."
        else:
            files_list = ", ".join(f"'{n}'" for n in all_names)
            files_str = f"You have {len(all_names)} documents loaded: {files_list}."

        system_prompt = (
            f"You are DocMind AI, an expert document analyst built by Ryan Farahani.\n"
            f"{files_str}\n"
            f"{doc_type_hint}\n"
            "Answer using ONLY the provided document context. "
            "When the context contains chunks from multiple files, indicate which file "
            "the information comes from if relevant.\n"
            "Be concise and precise. No preamble. No reasoning out loud. Just answer.\n"
            "If asked a follow-up question, use the conversation history for context."
        )

        messages = [{"role": "system", "content": system_prompt}]
        memory   = self.get_memory_messages()

        if memory:
            messages.append({
                "role":    "system",
                "content": f"Current document context:\n{context}"
            })
            messages.extend(memory)
            messages.append({"role": "user", "content": question})
        else:
            messages.append({
                "role":    "user",
                "content": f"Document context:\n{context}\n\n---\nQuestion: {question}"
            })

        headers    = {"Authorization": f"Bearer {hf_token}", "Content-Type": "application/json"}
        last_error = ""

        for model_id in CANDIDATE_MODELS:
            try:
                resp = requests.post(
                    HF_API_URL,
                    headers=headers,
                    data=json.dumps({
                        "model":       model_id,
                        "messages":    messages,
                        "max_tokens":  500,
                        "temperature": 0.1,
                        "stream":      False,
                    }),
                    timeout=60,
                )
                if resp.status_code == 200:
                    raw    = resp.json()["choices"][0]["message"]["content"].strip()
                    answer = _strip_thinking(raw)
                    if answer:
                        return answer, model_id
                else:
                    last_error = f"{model_id} → {resp.status_code}: {resp.text[:150]}"
                    logger.warning(last_error)
            except Exception as e:
                last_error = str(e)
                logger.warning(f"Exception on {model_id}: {e}")
                continue

        return (
            "AI unavailable. Most relevant excerpt:\n\n"
            + _extract_best(question, context)
            + f"\n\n(Error: {last_error})",
            "fallback"
        )


# ── Helpers ──────────────────────────────────────────────────────────────────

def _strip_thinking(text: str) -> str:
    text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL).strip()
    starters = [
        "okay", "ok,", "alright", "let me", "let's", "i need", "i will",
        "i'll", "first,", "so,", "the user", "looking at", "going through",
        "based on the chunk", "parsing", "to answer", "in order to",
    ]
    lines = text.split("\n")
    clean, found = [], False
    for line in lines:
        lower = line.strip().lower()
        if not found:
            if line.strip() and not any(lower.startswith(p) for p in starters):
                found = True
                clean.append(line)
        else:
            clean.append(line)
    return "\n".join(clean).strip() or text


def _extract_best(question: str, context: str) -> str:
    keywords   = set(re.findall(r'\b\w{4,}\b', question.lower()))
    best, score = "", 0
    for chunk in context.split("---"):
        s = len(keywords & set(re.findall(r'\b\w{4,}\b', chunk.lower())))
        if s > score:
            score, best = s, chunk.strip()
    return (best[:600] + "...") if len(best) > 600 else best or "No relevant content found."