Add Hermes Ollama Models dashboard plugin

README.md

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+# Hermes Ollama Models
+
+Native-like Hermes dashboard plugin for inspecting and managing a local Ollama installation.
+
+## Included
+
+- Installed and currently loaded Ollama model inventory
+- Model size, loaded memory, estimated RAM, quantization, context, capabilities, and strengths
+- Dense versus MoE classification
+- Search, popular models, family variants, downloads, updates, and removal actions
+- MLX model exclusion
+- RAM-aware Popular view for the current 30 GiB host
+
+## RAM-aware Popular policy
+
+The Popular view only displays models with known size and known estimated baseline RAM at or below 30 GiB. When an oversized popular family has a known smaller fitting variant, the smaller variant is shown instead. Equivalent model footprints are deduplicated.
+
+The estimate is a baseline and actual usage varies with context length, KV cache, GPU offload, batching, and runtime overhead.
+
+## Layout
+
+- `plugin.yaml` — Hermes plugin metadata
+- `dashboard/manifest.json` — native dashboard registration
+- `dashboard/plugin_api.py` — Ollama API and catalog backend
+- `dashboard/dist/index.js` — dashboard UI bundle
+- `dashboard/dist/style.css` — dashboard styles
+
+Runtime catalog data is intentionally stored in Hermes state rather than committed here.

dashboard/dist/index.js

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+(function () {
+  "use strict";
+  var SDK = window.__HERMES_PLUGIN_SDK__;
+  var registry = window.__HERMES_PLUGINS__;
+  if (!SDK || !registry) return;
+  var React = SDK.React;
+  var h = React.createElement;
+  var fetchJSON = SDK.fetchJSON;
+  var API = "/api/plugins/ollama-manager";
+
+  function fmtBytes(bytes) {
+    if (!bytes) return "Unknown";
+    var units = ["B", "GiB", "TiB"], value = Number(bytes), index = 0;
+    while (value >= 1024 && index < units.length - 1) { value /= 1024; index += 1; }
+    return value.toFixed(index ? 2 : 0) + " " + units[index];
+  }
+  function fmtDate(value) {
+    if (!value) return "Unknown";
+    try { return new Date(value).toLocaleString(); } catch (_) { return value; }
+  }
+  function Badge(props) { return h("span", { className: "ollama-badge " + (props.tone || "") }, props.children); }
+  function Button(props) { return h("button", Object.assign({ className: "ollama-button" }, props), props.children); }
+  function CapabilityList(props) {
+    var model = props.model;
+    return h("div", { className: "ollama-capabilities" },
+      (model.capabilities || []).map(function (cap) {
+        return h("div", { className: "ollama-capability", key: cap },
+          h(Badge, null, cap),
+          h("span", null, (model.capability_breakdown || {})[cap] || "Advertised by model metadata")
+        );
+      })
+    );
+  }
+  function VariantTable(props) {
+    var model = props.model, variants = model.variants || [], action = props.action, busy = props.busy;
+    if (!variants.length) return null;
+    return h("div", { className: "ollama-variants" },
+      h("div", { className: "ollama-variants-heading" },
+        h("h4", null, "Available ", model.name.split(":")[0], " sizes"),
+        h("span", null, "MLX variants excluded")
+      ),
+      h("div", { className: "ollama-variant-table-wrap" },
+        h("table", { className: "ollama-variant-table" },
+          h("thead", null, h("tr", null,
+            h("th", null, "Name"), h("th", null, "Size / RAM"), h("th", null, "Context"), h("th", null, "Input"), h("th", null, "Action")
+          )),
+          h("tbody", null, variants.map(function (variant) {
+            var current = variant.name === model.name;
+            var installed = variant.installed;
+            return h("tr", { key: variant.name, className: current ? "current" : "" },
+              h("td", null, h("strong", null, variant.name), current && h(Badge, { tone: "current" }, "current"), installed && !current && h(Badge, { tone: "installed" }, "installed")),
+              h("td", null, h("strong", null, variant.size_label || "Unknown"), h("small", null, variant.expected_ram_label || "RAM unknown")),
+              h("td", null, variant.context_length ? Math.round(Number(variant.context_length) / 1024) + "K" : "Unknown"),
+              h("td", null, (variant.input_modalities || ["Text"]).join(", ")),
+              h("td", null, current ? h("span", { className: "ollama-current-label" }, "Current") : installed ? h("span", { className: "ollama-current-label" }, "Installed") : h(Button, { disabled: !!busy, onClick: function () { action("pull", variant.name); } }, busy === variant.name + ":pull" ? "Downloading…" : "Download"))
+            );
+          }))
+        )
+      )
+    );
+  }
+
+  function ModelCard(props) {
+    var model = props.model, installed = props.installed, busy = props.busy;
+    var openState = React.useState(false), open = openState[0], setOpen = openState[1];
+    var action = props.action;
+    return h("article", { className: "ollama-model-card" },
+      h("div", { className: "ollama-card-top" },
+        h("div", { className: "ollama-model-title" },
+          h("h3", null, model.name),
+          h("div", { className: "ollama-badge-row" },
+            installed && model.loaded && h(Badge, { tone: "live" }, "loaded"),
+            installed && h(Badge, { tone: "installed" }, "installed"),
+            !installed && h(Badge, { tone: "download" }, "available"),
+            model.popularity_rank && h(Badge, { tone: "popular" }, "#" + model.popularity_rank + " popular"),
+            model.is_moe && h(Badge, { tone: "moe" }, "MoE")
+          )
+        ),
+        h("div", { className: "ollama-card-actions" },
+          installed && h(Button, { disabled: !!busy, onClick: function () { action("redownload", model.name); } }, busy === model.name + ":redownload" ? "Updating…" : "Update / re-download"),
+          installed && h(Button, { disabled: !!busy, className: "ollama-button danger", onClick: function () { action("delete", model.name); } }, busy === model.name + ":delete" ? "Removing…" : "Remove"),
+          !installed && h(Button, { disabled: !!busy, onClick: function () { action("pull", model.name); } }, busy === model.name + ":pull" ? "Downloading…" : "Download")
+        )
+      ),
+      h("div", { className: "ollama-model-summary" },
+        h("div", null, h("small", null, "Size"), h("strong", null, model.size_gb ? model.size_gb + " GiB" : "Unknown")),
+        h("div", null, h("small", null, "Expected RAM"), h("strong", null, model.expected_ram_label || "Unknown")),
+        h("div", null, h("small", null, "Type"), h("strong", null, model.architecture || "Unknown")),
+        h("div", null, h("small", null, "Parameters"), h("strong", null, model.parameter_size || "Unknown"))
+      ),
+      h("div", { className: "ollama-card-meta" },
+        h("span", null, (model.quantization || "Unknown") + " · " + (model.format || "Unknown")),
+        model.context_length && h("span", null, "Context " + Number(model.context_length).toLocaleString()),
+        installed && model.modified_at && h("span", null, "Updated " + fmtDate(model.modified_at))
+      ),
+      h("div", { className: "ollama-strengths" }, h("strong", null, "Excels at: "), (model.strengths || []).join(" · ")),
+      installed && h(VariantTable, { model: model, action: action, busy: busy }),
+      h(Button, { className: "ollama-details-toggle", onClick: function () { setOpen(!open); } }, open ? "Hide capability breakdown" : "Show capability breakdown"),
+      open && h("div", { className: "ollama-details" },
+        h("h4", null, "Capabilities"), h(CapabilityList, { model: model }),
+        h("h4", null, "Runtime estimate"),
+        h("p", null, model.expected_ram_basis || "No estimate basis available.", " Actual memory varies with context length, KV cache, GPU offload, and concurrent requests."),
+        h("div", { className: "ollama-detail-grid" },
+          h("span", null, "Family: ", h("strong", null, model.family || "unknown")),
+          h("span", null, "Digest: ", h("strong", null, model.digest ? model.digest.slice(0, 16) + "…" : "unknown")),
+          h("span", null, "Embedding: ", h("strong", null, model.embedding_length || "unknown")),
+          h("span", null, "Loaded VRAM: ", h("strong", null, fmtBytes(model.loaded_vram_bytes)))
+        )
+      )
+    );
+  }
+  function Empty(props) { return h("div", { className: "ollama-empty" }, props.children); }
+
+  function Page() {
+    var dataState = React.useState(null), data = dataState[0], setData = dataState[1];
+    var tabState = React.useState("installed"), tab = tabState[0], setTab = tabState[1];
+    var queryState = React.useState(""), query = queryState[0], setQuery = queryState[1];
+    var busyState = React.useState(""), busy = busyState[0], setBusy = busyState[1];
+    var noticeState = React.useState(null), notice = noticeState[0], setNotice = noticeState[1];
+    var loadingState = React.useState(true), loading = loadingState[0], setLoading = loadingState[1];
+
+    function load() {
+      return fetchJSON(API + "/status").then(function (value) {
+        setData(value); setLoading(false); return value;
+      }).catch(function (err) { setNotice({ error: err.message || String(err) }); setLoading(false); });
+    }
+    React.useEffect(function () {
+      load();
+      var timer = setInterval(load, 5000);
+      return function () { clearInterval(timer); };
+    }, []);
+
+    function action(kind, name) {
+      if (kind === "delete" && !window.confirm("Remove " + name + " from Ollama?")) return;
+      var key = name + ":" + kind;
+      setBusy(key); setNotice(null);
+      var method = kind === "delete" ? "DELETE" : "POST";
+      var path = kind === "delete" ? "/model" : "/" + kind;
+      fetchJSON(API + path, { method: method, headers: { "Content-Type": "application/json" }, body: JSON.stringify({ name: name }) })
+        .then(function (result) { setNotice({ ok: result.message || "Action started." }); load(); })
+        .catch(function (err) { setNotice({ error: err.message || String(err) }); })
+        .finally(function () { setBusy(""); });
+    }
+    function refreshCatalog() {
+      setBusy("catalog"); setNotice(null);
+      fetchJSON(API + "/catalog/refresh", { method: "POST" })
+        .then(function (result) { setNotice({ ok: "Catalog refreshed: " + result.count + " models." }); load(); })
+        .catch(function (err) { setNotice({ error: err.message || String(err) }); })
+        .finally(function () { setBusy(""); });
+    }
+
+    var models = data ? (tab === "installed" ? data.models || [] : tab === "popular" ? data.popular || [] : data.catalog || []) : [];
+    var needle = query.toLowerCase().trim();
+    if (needle) models = models.filter(function (model) { return (model.name + " " + model.family + " " + (model.strengths || []).join(" ") + " " + (model.capabilities || []).join(" ")).toLowerCase().indexOf(needle) >= 0; });
+    var jobs = data && data.jobs ? data.jobs.filter(function (job) { return job.state === "running"; }) : [];
+
+    return h("main", { className: "ollama-page" },
+      h("header", { className: "ollama-hero" },
+        h("div", null, h("div", { className: "ollama-eyebrow" }, "LOCAL MODEL OPERATIONS"), h("h1", null, "Ollama Models"), h("p", null, "Inspect, download, update, and remove models from the local Ollama runtime.")),
+        h("div", { className: "ollama-health" },
+          h(Badge, { tone: data && data.ollama && data.ollama.available ? "live" : "danger" }, data && data.ollama && data.ollama.available ? "Ollama online" : "Ollama unavailable"),
+          data && data.ollama && h("span", null, "v" + (data.ollama.version || "unknown")),
+          h(Button, { disabled: busy === "catalog", onClick: refreshCatalog }, busy === "catalog" ? "Refreshing…" : "Refresh catalog")
+        )
+      ),
+      notice && h("div", { className: "ollama-notice " + (notice.error ? "error" : "ok") }, notice.error || notice.ok),
+      h("section", { className: "ollama-toolbar" },
+        h("div", { className: "ollama-tabs" },
+          h(Button, { className: tab === "installed" ? "selected" : "", onClick: function () { setTab("installed"); } }, "Installed (" + ((data && data.models) || []).length + ")"),
+          h(Button, { className: tab === "popular" ? "selected" : "", onClick: function () { setTab("popular"); } }, "Top 20 popular (" + ((data && data.popular) || []).length + ")"),
+          h(Button, { className: tab === "catalog" ? "selected" : "", onClick: function () { setTab("catalog"); } }, "Available downloads (" + ((data && data.catalog) || []).length + ")")
+        ),
+        h("input", { className: "ollama-search", value: query, placeholder: "Search models, capabilities, or strengths…", onChange: function (event) { setQuery(event.target.value); } })
+      ),
+      h("div", { className: "ollama-info-strip" },
+        h("span", null, data && data.models ? data.models.filter(function (m) { return m.loaded; }).length + " currently loaded" : "Loading runtime state…"),
+        h("span", null, "Catalog checked " + (data && data.catalog_updated_at ? fmtDate(data.catalog_updated_at) : "not yet")),
+        h("span", null, "Next daily check " + (data && data.next_catalog_refresh ? fmtDate(data.next_catalog_refresh) : "01:00 Melbourne time") + " (1:00 AM Melbourne time)")
+      ),
+      tab === "popular" && h("p", { className: "ollama-popular-note" }, "Popular is limited to models with known size and RAM estimates at or below 30 GiB. Oversized families are represented by a smaller fitting variant when available."),
+      jobs.length > 0 && h("section", { className: "ollama-jobs" }, jobs.map(function (job) { return h("div", { key: job.id }, h("strong", null, job.action + " · " + job.name), h("span", null, job.percent == null ? job.status : job.percent + "%")); })),
+      loading && h(Empty, null, "Loading local Ollama inventory…"),
+      !loading && !models.length && h(Empty, null, tab === "installed" ? "No local models found." : tab === "popular" ? "No popular catalog entries available." : "No catalog entries available. Try Refresh catalog."),
+      h("section", { className: "ollama-grid" }, models.map(function (model) { return h(ModelCard, { key: model.name, model: model, installed: tab === "installed" || !!model.installed, busy: busy, action: action }); }))
+    );
+  }
+  registry.register("ollama-manager", Page);
+})();

dashboard/manifest.json

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+{
+  "name": "ollama-manager",
+  "label": "Ollama Models",
+  "description": "Inspect, download, update, and remove local Ollama models.",
+  "icon": "Cpu",
+  "version": "1.2.0",
+  "tab": {"path": "/ollama-manager", "position": "after:models"},
+  "entry": "dist/index.js",
+  "css": "dist/style.css",
+  "api": "plugin_api.py"
+}

dashboard/plugin_api.py

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+"""Native Hermes dashboard API for managing a local Ollama instance."""
+from __future__ import annotations
+
+import json
+import re
+import threading
+import time
+import uuid
+from datetime import datetime, timedelta, timezone
+from html import unescape
+from html.parser import HTMLParser
+from pathlib import Path
+from typing import Any
+from urllib.error import HTTPError, URLError
+from urllib.parse import unquote, urlencode
+from urllib.request import Request, urlopen
+from zoneinfo import ZoneInfo
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from hermes_constants import get_hermes_home
+
+router = APIRouter()
+LOCAL_OLLAMA = "http://127.0.0.1:11434"
+REMOTE_OLLAMA = "https://ollama.com"
+CATALOG_FILE = "catalog.json"
+MODEL_RE = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._:/-]{0,190}$")
+MELBOURNE = ZoneInfo("Australia/Melbourne")
+POPULAR_RAM_LIMIT_GIB = 30.0
+
+_jobs: dict[str, dict[str, Any]] = {}
+_jobs_lock = threading.Lock()
+_catalog_lock = threading.Lock()
+
+CAPABILITY_INFO = {
+    "completion": "Text generation and chat completion.",
+    "tools": "Tool/function calling for agent workflows.",
+    "thinking": "Explicit reasoning/thinking output support.",
+    "vision": "Image input and visual understanding.",
+    "audio": "Audio input or audio-aware inference.",
+    "video": "Video input or video-aware inference.",
+}
+
+FAMILY_STRENGTHS = {
+    "qwen": ["general reasoning", "coding", "multilingual work", "tool use"],
+    "qwen35": ["general reasoning", "coding", "long-context work", "tool use"],
+    "gemma": ["general assistance", "reasoning", "tool use", "efficient local inference"],
+    "nemotron": ["reasoning", "agent workflows", "long-context work", "technical tasks"],
+    "deepseek": ["coding", "mathematical reasoning", "technical analysis"],
+    "gpt-oss": ["general reasoning", "coding", "agent workflows"],
+    "mistral": ["general assistance", "multilingual work", "coding"],
+    "kimi": ["long-context work", "reasoning", "coding"],
+    "minimax": ["agent workflows", "reasoning", "long-context work"],
+    "glm": ["reasoning", "coding", "multilingual work"],
+    "lfm2": ["fast local assistants", "low-resource inference", "general chat"],
+}
+
+
+def _home() -> Path:
+    path = get_hermes_home() / "ollama-manager"
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def _json_request(url: str, method: str = "GET", payload: Any = None, timeout: int = 30) -> dict[str, Any]:
+    data = None if payload is None else json.dumps(payload).encode("utf-8")
+    headers = {"Accept": "application/json"}
+    if data is not None:
+        headers["Content-Type"] = "application/json"
+    request = Request(url, data=data, headers=headers, method=method)
+    with urlopen(request, timeout=timeout) as response:
+        raw = response.read()
+    value = json.loads(raw.decode("utf-8")) if raw else {}
+    return value if isinstance(value, dict) else {}
+
+
+def _valid_name(name: str) -> str:
+    name = str(name or "").strip()
+    if not MODEL_RE.fullmatch(name):
+        raise HTTPException(400, "Invalid Ollama model name")
+    return name
+
+
+def _local_tags() -> list[dict[str, Any]]:
+    try:
+        payload = _json_request(LOCAL_OLLAMA + "/api/tags", timeout=15)
+        models = payload.get("models", [])
+        return [item for item in models if isinstance(item, dict)]
+    except (HTTPError, URLError, OSError, ValueError):
+        return []
+
+
+def _local_ps() -> list[dict[str, Any]]:
+    try:
+        payload = _json_request(LOCAL_OLLAMA + "/api/ps", timeout=10)
+        models = payload.get("models", [])
+        return [item for item in models if isinstance(item, dict)]
+    except (HTTPError, URLError, OSError, ValueError):
+        return []
+
+
+def _is_mlx(raw: dict[str, Any] | str) -> bool:
+    text = str(raw if isinstance(raw, str) else {
+        "name": raw.get("name") or raw.get("model"),
+        "format": (raw.get("details") or {}).get("format"),
+        "capabilities": raw.get("capabilities"),
+    }).lower()
+    return "mlx" in text or bool(re.search(r"(?:^|[-:])mlx(?:$|[-:])", text))
+
+
+def _family_key(name: str) -> str:
+    return str(name or "").split(":", 1)[0].strip()
+
+
+def _known_ram_fit(model: dict[str, Any]) -> bool:
+    """Return True only when both size and RAM are known and fit this host."""
+    size_gb = model.get("size_gb")
+    ram_gb = model.get("expected_ram_gb")
+    return (
+        isinstance(size_gb, (int, float))
+        and isinstance(ram_gb, (int, float))
+        and float(ram_gb) <= POPULAR_RAM_LIMIT_GIB
+    )
+
+
+def _popular_fit_models(
+    raw_rows: list[dict[str, Any]],
+    family_rows: dict[str, list[dict[str, Any]]],
+    catalog_view,
+    installed_names: set[str],
+    loaded: dict[str, Any],
+) -> list[dict[str, Any]]:
+    """Select popular models that are usable within this host's RAM budget.
+
+    Oversized popular entries may be represented by the largest known smaller
+    family variant that fits. Unknown entries are never shown.
+    """
+    result: list[dict[str, Any]] = []
+    seen: set[str] = set()
+    seen_footprints: set[tuple[str, float, float]] = set()
+    seen_families: set[str] = set()
+    for raw in raw_rows:
+        if _is_mlx(raw):
+            continue
+        original = catalog_view(raw, "popular")
+        if _known_ram_fit(original):
+            candidate = original
+        else:
+            original_size = original.get("size_gb")
+            if not isinstance(original_size, (int, float)):
+                continue
+            family = _family_key(original["name"])
+            variants: list[dict[str, Any]] = []
+            for variant_raw in family_rows.get(family, []):
+                if _is_mlx(variant_raw):
+                    continue
+                variant = _model_view(variant_raw, loaded.get(str(variant_raw.get("name") or variant_raw.get("model"))), source="popular")
+                variant["installed"] = variant["name"] in installed_names
+                variant["loaded"] = variant["name"] in loaded
+                if _known_ram_fit(variant) and float(variant.get("size_gb", 0)) < float(original_size):
+                    variants.append(variant)
+            if not variants:
+                continue
+            candidate = max(variants, key=lambda item: (float(item.get("size_gb", 0)), item["name"]))
+            candidate["popular_origin"] = original["name"]
+            candidate["popular_note"] = f"Smaller fit variant for {original['name']}"
+        if candidate["name"] in seen:
+            continue
+        candidate_family = _family_key(candidate["name"])
+        if candidate.get("popular_origin") and candidate_family in seen_families:
+            continue
+        footprint = (
+            candidate_family,
+            round(float(candidate.get("size_gb", 0)), 2),
+            round(float(candidate.get("expected_ram_gb", 0)), 1),
+        )
+        if footprint in seen_footprints:
+            continue
+        seen.add(candidate["name"])
+        seen_families.add(candidate_family)
+        seen_footprints.add(footprint)
+        result.append(candidate)
+    for rank, row in enumerate(result, 1):
+        row["popularity_rank"] = rank
+    return result
+
+
+def _parse_number(text: Any) -> float | None:
+    match = re.search(r"([0-9]+(?:\.[0-9]+)?)", str(text or ""))
+    return float(match.group(1)) if match else None
+
+
+def _ram_estimate(size_bytes: Any, parameter_size: Any, quantization: Any, context_length: Any) -> tuple[float | None, str]:
+    size = float(size_bytes or 0)
+    if size > 0:
+        base = size / (1024 ** 3)
+        # Ollama's runtime needs allocator/graph overhead beyond the GGUF blob.
+        estimate = base * 1.15 + 0.5
+        basis = "disk size × 1.15 + 0.5 GiB runtime overhead"
+    else:
+        params = _parse_number(parameter_size)
+        if params is None:
+            return None, "Unavailable: source did not publish a model size"
+        bits = 4.5 if "Q4" in str(quantization).upper() else 8.0
+        estimate = params * (bits / 8.0) + 0.8
+        basis = "parameter/quantization estimate; source size unavailable"
+    context = int(context_length or 0)
+    if context >= 524288:
+        estimate += 2.0
+        basis += "; includes large-context allowance"
+    elif context >= 131072:
+        estimate += 1.0
+        basis += "; includes long-context allowance"
+    return round(estimate, 1), basis
+
+
+def _infer_capabilities(name: str, family: str, details: dict[str, Any], advertised: Any) -> list[str]:
+    caps = [str(item).lower() for item in advertised or [] if item]
+    text = f"{name} {family}".lower()
+    if not caps or "completion" not in caps:
+        caps.append("completion")
+    if any(token in text for token in ("vision", "vl", "gemma4", "muse-glimmer", "qwen3.8")) and "vision" not in caps:
+        caps.append("vision")
+    if any(token in text for token in ("audio", "omni")) and "audio" not in caps:
+        caps.append("audio")
+    if "video" in text and "video" not in caps:
+        caps.append("video")
+    if any(token in text for token in ("thinking", "reasoning", "nemotron", "deepseek", "qwen", "kimi")) and "thinking" not in caps:
+        caps.append("thinking")
+    if any(token in text for token in ("tool", "agent", "qwen", "gemma", "nemotron", "deepseek", "gpt-oss")) and "tools" not in caps:
+        caps.append("tools")
+    order = ["completion", "tools", "thinking", "vision", "audio", "video"]
+    return [cap for cap in order if cap in set(caps)]
+
+
+def _architecture(name: str, family: str, details: dict[str, Any]) -> tuple[str, bool]:
+    text = f"{name} {family} {details.get('parent_model', '')}".lower()
+    moe = any(token in text for token in ("moe", "mixture", "_h_", "a3b"))
+    label = "Mixture of Experts (MoE)" if moe else "Dense / single-expert"
+    if family:
+        label += f" · {family}"
+    return label, moe
+
+
+def _strengths(name: str, family: str, capabilities: list[str], context_length: Any) -> list[str]:
+    text = f"{name} {family}".lower()
+    result: list[str] = []
+    for key, values in FAMILY_STRENGTHS.items():
+        if key in text:
+            result.extend(values)
+            break
+    if "tools" in capabilities:
+        result.append("tool-enabled automation")
+    if "vision" in capabilities:
+        result.append("image-aware tasks")
+    if int(context_length or 0) >= 131072:
+        result.append("long documents and large codebases")
+    if not result:
+        result = ["general local inference"]
+    return list(dict.fromkeys(result))
+
+
+def _model_view(raw: dict[str, Any], loaded: dict[str, Any] | None = None, source: str = "local") -> dict[str, Any]:
+    details = raw.get("details") if isinstance(raw.get("details"), dict) else {}
+    name = str(raw.get("name") or raw.get("model") or "")
+    family = str(details.get("family") or (details.get("families") or [""])[0] or "")
+    capabilities = _infer_capabilities(name, family, details, raw.get("capabilities"))
+    context_length = details.get("context_length") or raw.get("context_length")
+    architecture, is_moe = _architecture(name, family, details)
+    size_bytes = raw.get("size") or 0
+    ram_gb, ram_basis = _ram_estimate(size_bytes, details.get("parameter_size"), details.get("quantization_level"), context_length)
+    loaded = loaded or {}
+    return {
+        "name": name,
+        "source": source,
+        "downloadable": source == "catalog",
+        "installed": source == "local",
+        "loaded": bool(loaded),
+        "size_bytes": int(size_bytes or 0),
+        "size_gb": round(float(size_bytes or 0) / (1024 ** 3), 2) if size_bytes else None,
+        "size_label": raw.get("size_label") or (f"{round(float(size_bytes or 0) / (1024 ** 3), 2)} GiB" if size_bytes else "Unknown"),
+        "loaded_bytes": int(loaded.get("size") or 0),
+        "loaded_vram_bytes": int(loaded.get("size_vram") or 0),
+        "digest": raw.get("digest", ""),
+        "modified_at": raw.get("modified_at"),
+        "family": family or "unknown",
+        "architecture": architecture,
+        "is_moe": is_moe,
+        "parameter_size": details.get("parameter_size") or "unknown",
+        "quantization": details.get("quantization_level") or "unknown",
+        "format": details.get("format") or "unknown",
+        "context_length": context_length,
+        "input_modalities": raw.get("input_modalities") or (["Text", "Image"] if "vision" in capabilities else ["Text"]),
+        "embedding_length": details.get("embedding_length"),
+        "capabilities": capabilities,
+        "capability_breakdown": {cap: CAPABILITY_INFO[cap] for cap in capabilities if cap in CAPABILITY_INFO},
+        "strengths": _strengths(name, family, capabilities, context_length),
+        "expected_ram_gb": ram_gb,
+        "expected_ram_label": f"{ram_gb:.1f} GiB baseline" if ram_gb is not None else "Unknown",
+        "expected_ram_basis": ram_basis,
+    }
+
+
+class _VariantPageParser(HTMLParser):
+    """Extract the public Ollama tag rows without depending on third-party HTML packages."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._depth = 0
+        self._parts: list[str] = []
+        self._href = ""
+        self.rows: list[dict[str, Any]] = []
+
+    def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None:
+        attrs_map = dict(attrs)
+        classes = attrs_map.get("class") or ""
+        if tag == "div" and "group" in classes.split() and "px-4" in classes.split():
+            self._depth = 1
+            self._parts = []
+            self._href = ""
+            return
+        if self._depth:
+            if tag == "div":
+                self._depth += 1
+            if tag == "a" and (attrs_map.get("href") or "").startswith("/library/") and not self._href:
+                self._href = attrs_map["href"] or ""
+
+    def handle_data(self, data: str) -> None:
+        if self._depth:
+            self._parts.append(data)
+
+    def handle_endtag(self, tag: str) -> None:
+        if not self._depth or tag != "div":
+            return
+        self._depth -= 1
+        if self._depth == 0 and self._href:
+            self.rows.append({"href": self._href, "text": " ".join("".join(self._parts).split())})
+            self._parts = []
+            self._href = ""
+
+
+def _text_request(url: str, timeout: int = 30) -> str:
+    request = Request(url, headers={"Accept": "text/html"})
+    with urlopen(request, timeout=timeout) as response:
+        return response.read().decode("utf-8", errors="replace")
+
+
+def _size_bytes(label: str) -> int:
+    match = re.search(r"([0-9]+(?:\.[0-9]+)?)\s*(KB|MB|GB|TB)", label.upper())
+    if not match:
+        return 0
+    multipliers = {"KB": 10**3, "MB": 10**6, "GB": 10**9, "TB": 10**12}
+    return int(float(match.group(1)) * multipliers[match.group(2)])
+
+
+def _parse_variant_page(html: str, family: str) -> list[dict[str, Any]]:
+    parser = _VariantPageParser()
+    parser.feed(html)
+    rows: list[dict[str, Any]] = []
+    seen: set[str] = set()
+    for item in parser.rows:
+        href = unescape(item["href"])
+        name = unquote(href.rsplit("/", 1)[-1])
+        if not name or name in seen or not name.startswith(family + ":"):
+            continue
+        seen.add(name)
+        text = item["text"]
+        size_match = re.search(r"•\s*([0-9]+(?:\.[0-9]+)?(?:KB|MB|GB|TB))\s*•", text, re.I)
+        context_match = re.search(r"([0-9]+)K\s+context window", text, re.I)
+        input_match = re.search(r"([^•]+?)\s+input\s+•", text, re.I)
+        size_label = size_match.group(1).upper() if size_match else ("cloud" if ("-cloud" in name or ":cloud" in name) else "Unknown")
+        modalities = [part.strip() for part in (input_match.group(1).split(",") if input_match else []) if part.strip()]
+        caps = ["vision"] if any(part.lower() == "image" for part in modalities) else []
+        rows.append({
+            "name": name,
+            "model": name,
+            "size": _size_bytes(size_label),
+            "size_label": size_label,
+            "modified_at": None,
+            "digest": "",
+            "details": {"family": family, "context_length": int(context_match.group(1)) * 1024 if context_match else None, "format": "gguf"},
+            "capabilities": caps,
+            "input_modalities": modalities or ["Text"],
+            "is_mlx": _is_mlx(name) or bool(re.search(r"\bMLX\b", text, re.I)),
+        })
+    return [row for row in rows if not row["is_mlx"]]
+
+
+def _fetch_family_variants(family: str) -> list[dict[str, Any]]:
+    try:
+        html = _text_request(f"{REMOTE_OLLAMA}/library/{family}/tags", timeout=30)
+        return _parse_variant_page(html, family)
+    except (HTTPError, URLError, OSError, ValueError):
+        return []
+
+
+def _catalog_path() -> Path:
+    return _home() / CATALOG_FILE
+
+
+def _read_catalog() -> dict[str, Any]:
+    try:
+        value = json.loads(_catalog_path().read_text(encoding="utf-8"))
+        return value if isinstance(value, dict) else {}
+    except (OSError, ValueError):
+        return {}
+
+
+def _write_catalog(value: dict[str, Any]) -> None:
+    path = _catalog_path()
+    temp = path.with_suffix(".tmp")
+    temp.write_text(json.dumps(value, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+    temp.replace(path)
+
+
+def _refresh_family_variants(rows: list[dict[str, Any]]) -> dict[str, list[dict[str, Any]]]:
+    families = sorted({_family_key(str(row.get("name") or row.get("model"))) for row in rows if not _is_mlx(row)})
+    return {family: _fetch_family_variants(family) for family in families if family}
+
+
+def refresh_catalog(force: bool = True) -> dict[str, Any]:
+    with _catalog_lock:
+        try:
+            query = urlencode({"limit": 100, "sort": "popular"})
+            payload = _json_request(f"{REMOTE_OLLAMA}/api/tags?{query}", timeout=30)
+            rows = [item for item in payload.get("models", []) if isinstance(item, dict) and not _is_mlx(item)]
+            catalog = {
+                "fetched_at": datetime.now(timezone.utc).isoformat(),
+                "source": f"{REMOTE_OLLAMA}/api/tags",
+                "models": rows,
+                "families": _refresh_family_variants(_local_tags()),
+            }
+            _write_catalog(catalog)
+            return catalog
+        except (HTTPError, URLError, OSError, ValueError) as exc:
+            cached = _read_catalog()
+            if cached:
+                cached["last_error"] = str(exc)
+                return cached
+            return {"fetched_at": None, "source": f"{REMOTE_OLLAMA}/api/tags", "models": [], "last_error": str(exc)}
+
+
+def _catalog_stale(catalog: dict[str, Any]) -> bool:
+    raw = catalog.get("fetched_at")
+    if not raw:
+        return True
+    try:
+        fetched = datetime.fromisoformat(str(raw).replace("Z", "+00:00"))
+        return datetime.now(timezone.utc) - fetched > timedelta(hours=20)
+    except ValueError:
+        return True
+
+
+def _ensure_catalog() -> dict[str, Any]:
+    catalog = _read_catalog()
+    if not catalog.get("models"):
+        return refresh_catalog()
+    if _catalog_stale(catalog) and not _catalog_lock.locked():
+        threading.Thread(target=refresh_catalog, kwargs={"force": True}, daemon=True, name="ollama-catalog-refresh").start()
+    return catalog
+
+
+def _next_refresh() -> str:
+    now = datetime.now(MELBOURNE)
+    target = now.replace(hour=1, minute=0, second=0, microsecond=0)
+    if now >= target:
+        target += timedelta(days=1)
+    return target.isoformat()
+
+
+def _job_snapshot() -> list[dict[str, Any]]:
+    with _jobs_lock:
+        return [dict(item) for item in _jobs.values()]
+
+
+def _set_job(job_id: str, **values: Any) -> None:
+    with _jobs_lock:
+        if job_id in _jobs:
+            _jobs[job_id].update(values, updated_at=time.time())
+
+
+def _run_pull(job_id: str, name: str, action: str) -> None:
+    try:
+        payload = json.dumps({"name": name, "stream": True}).encode("utf-8")
+        request = Request(LOCAL_OLLAMA + "/api/pull", data=payload, headers={"Content-Type": "application/json"}, method="POST")
+        with urlopen(request, timeout=3600) as response:
+            for raw_line in response:
+                try:
+                    event = json.loads(raw_line.decode("utf-8"))
+                except ValueError:
+                    continue
+                status = str(event.get("status") or "working")
+                completed = int(event.get("completed") or 0)
+                total = int(event.get("total") or 0)
+                percent = round(completed * 100 / total, 1) if total else None
+                _set_job(job_id, status=status, completed=completed, total=total, percent=percent, digest=event.get("digest"))
+                if event.get("error"):
+                    raise RuntimeError(str(event["error"]))
+        _set_job(job_id, state="completed", status="success", percent=100)
+    except Exception as exc:
+        _set_job(job_id, state="failed", status="error", error=str(exc))
+
+
+def _run_delete(job_id: str, name: str) -> None:
+    try:
+        _json_request(LOCAL_OLLAMA + "/api/delete", method="DELETE", payload={"name": name}, timeout=120)
+        _set_job(job_id, state="completed", status="deleted", percent=100)
+    except Exception as exc:
+        _set_job(job_id, state="failed", status="error", error=str(exc))
+
+
+def _new_job(name: str, action: str) -> str:
+    job_id = uuid.uuid4().hex
+    with _jobs_lock:
+        _jobs[job_id] = {"id": job_id, "name": name, "action": action, "state": "running", "status": "starting", "percent": 0, "created_at": time.time(), "updated_at": time.time()}
+    target = _run_delete if action == "delete" else _run_pull
+    args = (job_id, name) if action == "delete" else (job_id, name, action)
+    threading.Thread(target=target, args=args, daemon=True, name=f"ollama-{action}-{job_id[:8]}").start()
+    return job_id
+
+
+class ModelRequest(BaseModel):
+    name: str
+
+
+@router.get("/status")
+def status() -> dict[str, Any]:
+    tags = _local_tags()
+    ps_rows = _local_ps()
+    loaded = {str(row.get("name") or row.get("model")): row for row in ps_rows}
+    local = [
+        _model_view(row, loaded.get(str(row.get("name") or row.get("model"))))
+        for row in tags
+        if not _is_mlx(row)
+    ]
+    catalog = _ensure_catalog()
+    installed_names = {row["name"] for row in local}
+
+    def catalog_view(raw: dict[str, Any], source: str) -> dict[str, Any]:
+        name = str(raw.get("name") or raw.get("model"))
+        view = _model_view(raw, loaded.get(name), source=source)
+        view["installed"] = name in installed_names
+        view["loaded"] = name in loaded
+        return view
+
+    downloadable = [
+        catalog_view(row, "catalog")
+        for row in catalog.get("models", [])
+        if not _is_mlx(row) and str(row.get("name") or row.get("model")) not in installed_names
+    ]
+
+    family_rows = catalog.get("families") if isinstance(catalog.get("families"), dict) else {}
+    popular = _popular_fit_models(
+        [row for row in catalog.get("models", []) if not _is_mlx(row)],
+        family_rows,
+        catalog_view,
+        installed_names,
+        loaded,
+    )
+    for row in local:
+        family = _family_key(row["name"])
+        variants = []
+        for raw in family_rows.get(family, []):
+            if _is_mlx(raw):
+                continue
+            variant = _model_view(raw, loaded.get(raw["name"]), source="variant")
+            variant["installed"] = variant["name"] in installed_names
+            variant["current"] = variant["name"] == row["name"]
+            variants.append(variant)
+        row["variants"] = sorted(variants, key=lambda item: (item.get("size_bytes") or 0, item["name"]))
+
+    return {
+        "ollama": {"available": bool(tags or ps_rows), "version": _ollama_version(), "endpoint": LOCAL_OLLAMA},
+        "models": local,
+        "popular": popular,
+        "popular_filter": {
+            "max_expected_ram_gib": POPULAR_RAM_LIMIT_GIB,
+            "requires_known_size": True,
+            "requires_known_ram": True,
+            "smaller_fit_variants_substituted": True,
+        },
+        "catalog": downloadable,
+        "catalog_updated_at": catalog.get("fetched_at"),
+        "catalog_source": catalog.get("source"),
+        "catalog_error": catalog.get("last_error"),
+        "next_catalog_refresh": _next_refresh(),
+        "jobs": _job_snapshot(),
+        "generated_at": time.time(),
+    }
+
+
+def _ollama_version() -> str | None:
+    try:
+        return str(_json_request(LOCAL_OLLAMA + "/api/version", timeout=5).get("version") or "unknown")
+    except Exception:
+        return None
+
+
+@router.post("/catalog/refresh")
+def catalog_refresh() -> dict[str, Any]:
+    catalog = refresh_catalog()
+    return {"ok": bool(catalog.get("models")), "updated_at": catalog.get("fetched_at"), "count": len(catalog.get("models", [])), "error": catalog.get("last_error")}
+
+
+@router.post("/pull")
+def pull_model(body: ModelRequest) -> dict[str, Any]:
+    name = _valid_name(body.name)
+    return {"ok": True, "job_id": _new_job(name, "download"), "message": f"Downloading or updating {name}"}
+
+
+@router.post("/redownload")
+def redownload_model(body: ModelRequest) -> dict[str, Any]:
+    name = _valid_name(body.name)
+    return {"ok": True, "job_id": _new_job(name, "redownload"), "message": f"Re-downloading or updating {name}"}
+
+
+@router.delete("/model")
+def delete_model(body: ModelRequest) -> dict[str, Any]:
+    name = _valid_name(body.name)
+    return {"ok": True, "job_id": _new_job(name, "delete"), "message": f"Removing {name}"}
+
+
+def create_ollama_routes(app) -> None:
+    app.include_router(router, prefix="/api/plugins/ollama-manager")

plugin.yaml

@@ -0,0 +1,3 @@
+name: ollama-manager
+version: 1.2.0
+description: Native dashboard manager for local Ollama models and catalog discovery.