"""Data access layer for the web dashboard.

Thin wrapper around ietf_analyzer.db.Database that returns plain dicts
ready for JSON serialization or Jinja2 template rendering.
"""

from __future__ import annotations

import json
import sys
from collections import Counter, defaultdict
from pathlib import Path

# Add project root to path so we can import ietf_analyzer
_project_root = Path(__file__).resolve().parent.parent.parent
if str(_project_root) not in sys.path:
    sys.path.insert(0, str(_project_root / "src"))

from ietf_analyzer.config import Config
from ietf_analyzer.db import Database


def get_db() -> Database:
    """Get a Database instance using default config."""
    config = Config.load()
    return Database(config)


def get_overview_stats(db: Database) -> dict:
    """Return high-level stats for the dashboard home page."""
    total_drafts = db.count_drafts()
    rated_pairs = db.drafts_with_ratings(limit=1000)
    rated_count = len(rated_pairs)
    author_count = db.author_count()
    idea_count = db.idea_count()
    gaps = db.all_gaps()
    input_tok, output_tok = db.total_tokens_used()

    return {
        "total_drafts": total_drafts,
        "rated_count": rated_count,
        "author_count": author_count,
        "idea_count": idea_count,
        "gap_count": len(gaps),
        "input_tokens": input_tok,
        "output_tokens": output_tok,
    }


def get_category_counts(db: Database) -> dict[str, int]:
    """Return {category: draft_count} for all categories."""
    pairs = db.drafts_with_ratings(limit=1000)
    counts: dict[str, int] = Counter()
    for _, rating in pairs:
        for cat in rating.categories:
            counts[cat] += 1
    return dict(counts.most_common())


def get_drafts_page(
    db: Database,
    page: int = 1,
    per_page: int = 50,
    search: str = "",
    category: str = "",
    min_score: float = 0.0,
    sort: str = "score",
    sort_dir: str = "desc",
) -> dict:
    """Return a paginated, filtered list of drafts with ratings.

    Returns dict with keys: drafts, total, page, per_page, pages.
    """
    pairs = db.drafts_with_ratings(limit=1000)

    # Filter
    filtered = []
    for draft, rating in pairs:
        if min_score > 0 and rating.composite_score < min_score:
            continue
        if category and category not in rating.categories:
            continue
        if search:
            haystack = f"{draft.name} {draft.title} {rating.summary}".lower()
            if not all(w in haystack for w in search.lower().split()):
                continue
        filtered.append((draft, rating))

    # Sort
    sort_keys = {
        "score": lambda p: p[1].composite_score,
        "name": lambda p: p[0].name,
        "date": lambda p: p[0].time or "",
        "novelty": lambda p: p[1].novelty,
        "maturity": lambda p: p[1].maturity,
        "relevance": lambda p: p[1].relevance,
        "overlap": lambda p: p[1].overlap,
        "momentum": lambda p: p[1].momentum,
    }
    key_fn = sort_keys.get(sort, sort_keys["score"])
    reverse = sort_dir == "desc"
    filtered.sort(key=key_fn, reverse=reverse)

    total = len(filtered)
    pages = max(1, (total + per_page - 1) // per_page)
    page = max(1, min(page, pages))
    start = (page - 1) * per_page
    page_items = filtered[start : start + per_page]

    drafts = []
    for draft, rating in page_items:
        drafts.append({
            "name": draft.name,
            "title": draft.title,
            "date": draft.date,
            "url": draft.datatracker_url,
            "pages": draft.pages or 0,
            "group": draft.group or "individual",
            "score": round(rating.composite_score, 2),
            "novelty": rating.novelty,
            "maturity": rating.maturity,
            "overlap": rating.overlap,
            "momentum": rating.momentum,
            "relevance": rating.relevance,
            "categories": rating.categories,
            "summary": rating.summary,
        })

    return {
        "drafts": drafts,
        "total": total,
        "page": page,
        "per_page": per_page,
        "pages": pages,
    }


def get_draft_detail(db: Database, name: str) -> dict | None:
    """Return full detail for a single draft."""
    draft = db.get_draft(name)
    if not draft:
        return None

    rating = db.get_rating(name)
    authors = db.get_authors_for_draft(name)
    ideas = db.get_ideas_for_draft(name)
    refs = db.get_refs_for_draft(name)

    result = {
        "name": draft.name,
        "title": draft.title,
        "rev": draft.rev,
        "abstract": draft.abstract,
        "date": draft.date,
        "time": draft.time,
        "url": draft.datatracker_url,
        "text_url": draft.text_url,
        "pages": draft.pages,
        "words": draft.words,
        "group": draft.group or "individual",
        "categories": draft.categories,
        "tags": draft.tags,
        "authors": [
            {"name": a.name, "affiliation": a.affiliation, "person_id": a.person_id}
            for a in authors
        ],
        "ideas": ideas,
        "refs": [{"type": t, "id": rid} for t, rid in refs],
    }

    if rating:
        result["rating"] = {
            "score": round(rating.composite_score, 2),
            "novelty": rating.novelty,
            "maturity": rating.maturity,
            "overlap": rating.overlap,
            "momentum": rating.momentum,
            "relevance": rating.relevance,
            "summary": rating.summary,
            "novelty_note": rating.novelty_note,
            "maturity_note": rating.maturity_note,
            "overlap_note": rating.overlap_note,
            "momentum_note": rating.momentum_note,
            "relevance_note": rating.relevance_note,
            "categories": rating.categories,
        }

    return result


def get_rating_distributions(db: Database) -> dict:
    """Return arrays for each rating dimension, suitable for Plotly."""
    pairs = db.drafts_with_ratings(limit=1000)
    dims = {
        "novelty": [],
        "maturity": [],
        "overlap": [],
        "momentum": [],
        "relevance": [],
        "scores": [],
        "categories": [],
        "names": [],
    }
    for draft, rating in pairs:
        dims["novelty"].append(rating.novelty)
        dims["maturity"].append(rating.maturity)
        dims["overlap"].append(rating.overlap)
        dims["momentum"].append(rating.momentum)
        dims["relevance"].append(rating.relevance)
        dims["scores"].append(round(rating.composite_score, 2))
        dims["categories"].append(rating.categories[0] if rating.categories else "Other")
        dims["names"].append(draft.name)
    return dims


def get_timeline_data(db: Database) -> dict:
    """Return monthly counts by category for timeline chart."""
    pairs = db.drafts_with_ratings(limit=1000)
    all_drafts = db.list_drafts(limit=1000, order_by="time ASC")
    rating_map = {d.name: r for d, r in pairs}

    month_cat: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
    for d in all_drafts:
        month = d.time[:7] if d.time else "unknown"
        r = rating_map.get(d.name)
        if r:
            cat = r.categories[0] if r.categories else "Other"
            month_cat[month][cat] += 1

    months = sorted(month_cat.keys())
    cat_totals: Counter = Counter()
    for mc in month_cat.values():
        for c, cnt in mc.items():
            cat_totals[c] += cnt
    top_cats = [c for c, _ in cat_totals.most_common(10)]

    series = {}
    for cat in top_cats:
        series[cat] = [month_cat[m].get(cat, 0) for m in months]

    return {"months": months, "series": series, "categories": top_cats}


def get_ideas_by_type(db: Database) -> dict:
    """Return ideas grouped by type with counts."""
    all_ideas = db.all_ideas()
    type_counts = Counter(i.get("type", "other") or "other" for i in all_ideas)
    return {
        "total": len(all_ideas),
        "by_type": dict(type_counts.most_common()),
        "ideas": all_ideas,
    }


def get_all_gaps(db: Database) -> list[dict]:
    """Return all gap analysis results."""
    return db.all_gaps()


def get_gap_detail(db: Database, gap_id: int) -> dict | None:
    """Return a single gap by ID, or None if not found."""
    gaps = db.all_gaps()
    for g in gaps:
        if g["id"] == gap_id:
            return g
    return None


def get_generated_drafts() -> list[dict]:
    """Return list of pre-generated draft files in data/reports/generated-drafts/."""
    drafts_dir = _project_root / "data" / "reports" / "generated-drafts"
    if not drafts_dir.exists():
        return []
    results = []
    for f in sorted(drafts_dir.glob("draft-*.txt")):
        # Extract title from first non-empty content line after header
        title = f.stem
        text = f.read_text(errors="replace")
        for line in text.splitlines():
            stripped = line.strip()
            if stripped and not stripped.startswith("Internet-Draft") and \
               not stripped.startswith("Intended status") and \
               not stripped.startswith("Expires:") and stripped != "":
                title = stripped
                break
        results.append({
            "filename": f.name,
            "stem": f.stem,
            "title": title,
            "size": f.stat().st_size,
            "path": str(f),
        })
    return results


def read_generated_draft(filename: str) -> str | None:
    """Read a generated draft file by filename. Returns text or None."""
    drafts_dir = _project_root / "data" / "reports" / "generated-drafts"
    path = drafts_dir / filename
    if not path.exists() or not path.is_file():
        return None
    # Safety: ensure we're not reading outside the directory
    if not str(path.resolve()).startswith(str(drafts_dir.resolve())):
        return None
    return path.read_text(errors="replace")


def get_top_authors(db: Database, limit: int = 30) -> list[dict]:
    """Return top authors by draft count."""
    rows = db.top_authors(limit=limit)
    return [
        {"name": name, "affiliation": aff, "draft_count": cnt, "drafts": drafts}
        for name, aff, cnt, drafts in rows
    ]


def get_org_data(db: Database, limit: int = 20) -> list[dict]:
    """Return organization contribution data."""
    rows = db.top_orgs(limit=limit)
    return [
        {"org": org, "author_count": authors, "draft_count": drafts}
        for org, authors, drafts in rows
    ]


def get_category_radar_data(db: Database) -> dict:
    """Return average rating profiles per category for radar chart."""
    pairs = db.drafts_with_ratings(limit=1000)
    cat_ratings: dict[str, list] = defaultdict(list)
    for _, r in pairs:
        for c in r.categories:
            cat_ratings[c].append(r)

    top_cats = sorted(cat_ratings.keys(), key=lambda c: len(cat_ratings[c]), reverse=True)[:8]
    result = {}
    for cat in top_cats:
        ratings = cat_ratings[cat]
        n = len(ratings)
        result[cat] = {
            "count": n,
            "novelty": round(sum(r.novelty for r in ratings) / n, 2),
            "maturity": round(sum(r.maturity for r in ratings) / n, 2),
            "relevance": round(sum(r.relevance for r in ratings) / n, 2),
            "momentum": round(sum(r.momentum for r in ratings) / n, 2),
            "low_overlap": round(sum(6 - r.overlap for r in ratings) / n, 2),
        }
    return result


def get_score_histogram(db: Database) -> list[float]:
    """Return list of composite scores for histogram."""
    pairs = db.drafts_with_ratings(limit=1000)
    return [round(r.composite_score, 2) for _, r in pairs]


def get_coauthor_network(db: Database, min_shared: int = 1) -> dict:
    """Return co-authorship network data for force-directed graph.

    Returns {nodes: [{id, name, org, draft_count}], edges: [{source, target, weight}]}
    """
    pairs = db.coauthor_pairs()
    top = db.top_authors(limit=100)

    # Build node set from authors who have co-authorships
    author_info = {name: {"org": aff, "draft_count": cnt} for name, aff, cnt, _ in top}
    node_set = set()
    edges = []
    for a, b, shared in pairs:
        if shared >= min_shared:
            node_set.add(a)
            node_set.add(b)
            edges.append({"source": a, "target": b, "weight": shared})

    nodes = []
    for name in node_set:
        info = author_info.get(name, {"org": "", "draft_count": 1})
        nodes.append({
            "id": name,
            "name": name,
            "org": info["org"],
            "draft_count": info["draft_count"],
        })

    return {"nodes": nodes, "edges": edges}


def get_similarity_graph(db: Database, threshold: float = 0.75) -> dict:
    """Return draft similarity network for force-directed graph.

    Returns {nodes: [{name, title, category, score}],
             edges: [{source, target, similarity}],
             stats: {node_count, edge_count, avg_similarity}}
    """
    import numpy as np

    embeddings = db.all_embeddings()
    if len(embeddings) < 2:
        return {"nodes": [], "edges": [], "stats": {"node_count": 0, "edge_count": 0, "avg_similarity": 0}}

    pairs = db.drafts_with_ratings(limit=1000)
    rating_map = {d.name: r for d, r in pairs}
    draft_map = {d.name: d for d, _ in pairs}

    # Filter to drafts with both embeddings and ratings
    names = [n for n in embeddings if n in rating_map]
    if len(names) < 2:
        return {"nodes": [], "edges": [], "stats": {"node_count": 0, "edge_count": 0, "avg_similarity": 0}}

    matrix = np.array([embeddings[n] for n in names])

    # L2-normalize and compute cosine similarity
    norms = np.linalg.norm(matrix, axis=1, keepdims=True)
    norms[norms == 0] = 1.0
    normalized = matrix / norms
    sim_matrix = normalized @ normalized.T

    # Find pairs above threshold (upper triangle only)
    edges = []
    node_set = set()
    for i in range(len(names)):
        for j in range(i + 1, len(names)):
            sim = float(sim_matrix[i, j])
            if sim >= threshold:
                edges.append({"source": names[i], "target": names[j], "similarity": round(sim, 4)})
                node_set.add(names[i])
                node_set.add(names[j])

    # Build nodes from connected drafts only
    nodes = []
    for name in names:
        if name not in node_set:
            continue
        r = rating_map[name]
        d = draft_map.get(name)
        nodes.append({
            "name": name,
            "title": d.title if d else name,
            "category": r.categories[0] if r.categories else "Other",
            "score": round(r.composite_score, 2),
        })

    avg_sim = round(sum(e["similarity"] for e in edges) / max(len(edges), 1), 4)

    return {
        "nodes": nodes,
        "edges": edges,
        "stats": {"node_count": len(nodes), "edge_count": len(edges), "avg_similarity": avg_sim},
    }


def get_cross_org_data(db: Database, limit: int = 20) -> list[dict]:
    """Return cross-org collaboration pairs."""
    rows = db.cross_org_collaborations(limit=limit)
    return [
        {"org_a": a, "org_b": b, "shared_drafts": cnt}
        for a, b, cnt in rows
    ]


def get_author_network_full(db: Database) -> dict:
    """Return enriched co-authorship network with avg scores and cluster info.

    Returns {
        nodes: [{id, name, org, draft_count, avg_score, drafts: [name,...]}],
        edges: [{source, target, weight}],
        clusters: [{id, members: [name,...], org_mix: {org: count}, size}],
    }
    """
    pairs = db.coauthor_pairs()
    top = db.top_authors(limit=500)

    # Build rating lookup for avg scores
    rated = db.drafts_with_ratings(limit=2000)
    draft_score = {d.name: r.composite_score for d, r in rated}

    # Author info map
    author_info = {}
    for name, aff, cnt, drafts in top:
        scores = [draft_score[dn] for dn in drafts if dn in draft_score]
        avg = round(sum(scores) / len(scores), 2) if scores else 0
        author_info[name] = {
            "org": aff, "draft_count": cnt, "drafts": drafts, "avg_score": avg
        }

    # Build node set: authors with 2+ drafts OR 1+ co-authorship
    node_set = set()
    edges = []
    for a, b, shared in pairs:
        if shared >= 1:
            node_set.add(a)
            node_set.add(b)
            edges.append({"source": a, "target": b, "weight": shared})

    # Also include authors with 2+ drafts even if no co-authorships
    for name, info in author_info.items():
        if info["draft_count"] >= 2:
            node_set.add(name)

    nodes = []
    for name in node_set:
        info = author_info.get(name, {"org": "", "draft_count": 1, "drafts": [], "avg_score": 0})
        nodes.append({
            "id": name,
            "name": name,
            "org": info["org"],
            "draft_count": info["draft_count"],
            "avg_score": info["avg_score"],
            "drafts": info["drafts"][:8],  # cap for JSON size
        })

    # Cluster detection via connected components (BFS)
    adjacency: dict[str, set[str]] = defaultdict(set)
    for e in edges:
        adjacency[e["source"]].add(e["target"])
        adjacency[e["target"]].add(e["source"])

    visited: set[str] = set()
    clusters = []

    for node in sorted(node_set):
        if node in visited:
            continue
        component: list[str] = []
        queue = [node]
        while queue:
            current = queue.pop(0)
            if current in visited:
                continue
            visited.add(current)
            component.append(current)
            for neighbor in adjacency.get(current, []):
                if neighbor not in visited:
                    queue.append(neighbor)

        if len(component) >= 2:
            org_mix: dict[str, int] = Counter()
            for m in component:
                org = author_info.get(m, {}).get("org", "")
                if org:
                    org_mix[org] += 1
            clusters.append({
                "id": len(clusters),
                "members": component,
                "org_mix": dict(org_mix.most_common()),
                "size": len(component),
            })

    clusters.sort(key=lambda c: c["size"], reverse=True)

    return {"nodes": nodes, "edges": edges, "clusters": clusters}


def get_idea_clusters(db: Database) -> dict:
    """Cluster ideas by embedding similarity, return clusters + t-SNE scatter."""
    import numpy as np

    embeddings = db.all_idea_embeddings()
    if not embeddings:
        return {"clusters": [], "scatter": [], "stats": {"total": 0, "clustered": 0, "num_clusters": 0}, "empty": True}

    # Fetch ideas with IDs for metadata lookup
    rows = db.conn.execute("SELECT id, title, description, idea_type, draft_name FROM ideas").fetchall()
    idea_map = {r["id"]: {"title": r["title"], "description": r["description"],
                           "type": r["idea_type"], "draft_name": r["draft_name"]} for r in rows}

    # Build matrix from embeddings that have matching ideas
    idea_ids = [iid for iid in embeddings if iid in idea_map]
    if len(idea_ids) < 5:
        return {"clusters": [], "scatter": [], "stats": {"total": len(idea_ids), "clustered": 0, "num_clusters": 0}, "empty": True}

    matrix = np.array([embeddings[iid] for iid in idea_ids])

    # Agglomerative clustering with cosine distance
    try:
        from sklearn.cluster import AgglomerativeClustering
        clustering = AgglomerativeClustering(
            n_clusters=None, distance_threshold=0.5,
            metric='cosine', linkage='average',
        )
        labels = clustering.fit_predict(matrix)
    except Exception:
        return {"clusters": [], "scatter": [], "stats": {"total": len(idea_ids), "clustered": 0, "num_clusters": 0}, "empty": True}

    # Build cluster data
    cluster_ideas: dict[int, list] = defaultdict(list)
    for idx, iid in enumerate(idea_ids):
        cluster_ideas[labels[idx]].append(iid)

    # Filter to clusters with 2+ ideas
    stop = {"a", "an", "the", "of", "for", "in", "to", "and", "or", "with", "on", "by", "is", "as", "at", "from", "that", "this", "it"}
    clusters = []
    for cid in sorted(cluster_ideas.keys()):
        members = cluster_ideas[cid]
        if len(members) < 2:
            continue
        ideas_in_cluster = [idea_map[iid] for iid in members if iid in idea_map]
        # Theme: most common significant words in titles
        words = Counter()
        for idea in ideas_in_cluster:
            for w in idea["title"].lower().split():
                w_clean = w.strip("()[].,;:-\"'")
                if len(w_clean) > 2 and w_clean not in stop:
                    words[w_clean] += 1
        top_words = [w for w, _ in words.most_common(4)]
        theme = " ".join(top_words).title() if top_words else f"Cluster {cid}"

        drafts = list({idea["draft_name"] for idea in ideas_in_cluster})
        clusters.append({
            "id": len(clusters),
            "theme": theme,
            "size": len(ideas_in_cluster),
            "ideas": ideas_in_cluster[:20],
            "drafts": drafts,
        })

    # t-SNE for scatter
    scatter = []
    try:
        from sklearn.manifold import TSNE
        perp = min(30, len(idea_ids) - 1)
        tsne = TSNE(n_components=2, perplexity=perp, random_state=42, max_iter=500)
        coords = tsne.fit_transform(matrix)
        for idx, iid in enumerate(idea_ids):
            info = idea_map.get(iid, {})
            scatter.append({
                "x": round(float(coords[idx, 0]), 3),
                "y": round(float(coords[idx, 1]), 3),
                "cluster_id": int(labels[idx]),
                "title": info.get("title", ""),
                "draft_name": info.get("draft_name", ""),
            })
    except Exception:
        pass

    total = len(idea_ids)
    clustered = sum(c["size"] for c in clusters)
    return {
        "clusters": clusters,
        "scatter": scatter,
        "stats": {"total": total, "clustered": clustered, "num_clusters": len(clusters)},
        "empty": False,
    }


def get_timeline_animation_data(db: Database) -> dict:
    """Compute t-SNE on all drafts, return points with month info + category_monthly.

    t-SNE is computed once on ALL drafts so coordinates are stable across
    animation frames.  Each point carries a ``month`` field (YYYY-MM) so the
    front-end can build cumulative animation frames.
    """
    import numpy as np

    embeddings = db.all_embeddings()
    if len(embeddings) < 5:
        return {"points": [], "months": [], "category_monthly": {}}

    pairs = db.drafts_with_ratings(limit=1000)
    rating_map = {d.name: r for d, r in pairs}
    draft_map = {d.name: d for d, _ in pairs}

    # Filter to drafts that have both embeddings and ratings
    names = [n for n in embeddings if n in rating_map]
    if len(names) < 5:
        return {"points": [], "months": [], "category_monthly": {}}

    matrix = np.array([embeddings[n] for n in names])

    try:
        from sklearn.manifold import TSNE
        tsne = TSNE(n_components=2, perplexity=min(30, len(names) - 1),
                     random_state=42, max_iter=500)
        coords = tsne.fit_transform(matrix)
    except Exception:
        return {"points": [], "months": [], "category_monthly": {}}

    # Build points with month
    points = []
    month_set: set[str] = set()
    category_monthly: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))

    for i, name in enumerate(names):
        r = rating_map[name]
        d = draft_map.get(name)
        month = (d.time[:7] if d and d.time else "unknown")
        cat = r.categories[0] if r.categories else "Other"
        month_set.add(month)
        category_monthly[month][cat] += 1
        points.append({
            "name": name,
            "title": d.title if d else name,
            "x": round(float(coords[i, 0]), 3),
            "y": round(float(coords[i, 1]), 3),
            "category": cat,
            "score": round(r.composite_score, 2),
            "month": month,
        })

    months = sorted(month_set)
    # Convert defaultdict to plain dict for JSON
    cat_monthly_plain = {m: dict(cats) for m, cats in category_monthly.items()}

    return {
        "points": points,
        "months": months,
        "category_monthly": cat_monthly_plain,
    }


def get_monitor_status(db: Database) -> dict:
    """Return monitoring status data for dashboard."""
    runs = db.get_monitor_runs(limit=20)
    last = runs[0] if runs else None
    unrated = len(db.unrated_drafts(limit=9999))
    unembedded = len(db.drafts_without_embeddings(limit=9999))
    no_ideas = len(db.drafts_without_ideas(limit=9999))
    return {
        "last_run": last,
        "runs": runs,
        "unprocessed": {"unrated": unrated, "unembedded": unembedded, "no_ideas": no_ideas},
        "total_runs": len(runs),
    }


def get_landscape_tsne(db: Database) -> list[dict]:
    """Compute t-SNE from embeddings, return [{name, title, x, y, category, score}].

    Uses cached coordinates if available, otherwise computes fresh.
    """
    import numpy as np

    embeddings = db.all_embeddings()
    if len(embeddings) < 5:
        return []

    pairs = db.drafts_with_ratings(limit=1000)
    rating_map = {d.name: r for d, r in pairs}
    draft_map = {d.name: d for d, _ in pairs}

    # Filter to drafts that have both embeddings and ratings
    names = [n for n in embeddings if n in rating_map]
    if len(names) < 5:
        return []

    matrix = np.array([embeddings[n] for n in names])

    try:
        from sklearn.manifold import TSNE
        tsne = TSNE(n_components=2, perplexity=min(30, len(names) - 1),
                     random_state=42, max_iter=500)
        coords = tsne.fit_transform(matrix)
    except Exception:
        return []

    result = []
    for i, name in enumerate(names):
        r = rating_map[name]
        d = draft_map.get(name)
        result.append({
            "name": name,
            "title": d.title if d else name,
            "x": round(float(coords[i, 0]), 3),
            "y": round(float(coords[i, 1]), 3),
            "category": r.categories[0] if r.categories else "Other",
            "score": round(r.composite_score, 2),
        })
    return result