Persist library profiles and download history with weight capacity checks

capacity.py

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+"""Conservative file-weight checks, never a claim that a pipeline can execute."""
+from pathlib import Path
+import re
+
+GIB=1024**3
+
+def assess(size, hardware, filename=''):
+    if not isinstance(size,int) or size<1:return dict(label='Größe unbekannt',scope='unknown',gpus=[])
+    if filename.lower().endswith('.json'):return dict(label='Konfigurationsdatei · kein Modell',scope='configuration',gpus=[])
+    gpus=[]
+    for gpu in hardware.get('gpus',[]):
+        total=gpu.get('total_mib');used=gpu.get('used_mib')
+        reserve=max(512,(total or 0)*.05)
+        capacity=(total-reserve)*1024**2 if total is not None else None
+        free=max(0,total-used-reserve)*1024**2 if total is not None and used is not None else None
+        gpus.append(dict(name=gpu['name'],uuid=gpu.get('uuid'),capacity_bytes=capacity,free_bytes=free,
+                         weights_fit_total=size<=capacity if capacity is not None else None,
+                         weights_fit_now=size<=free if free is not None else None))
+    fits=any(x['weights_fit_total'] for x in gpus);now=any(x['weights_fit_now'] for x in gpus)
+    label='Gewichte passen auf eine GPU; Gesamtbedarf offen' if fits else 'Gewichte benötigen Aufteilung/Auslagerung; Gesamtbedarf offen' if gpus else 'GPU-Speicher nicht ermittelbar'
+    ram=hardware.get('ram',{});total=ram.get('total_bytes');used=ram.get('used_bytes')
+    limit=None
+    try:
+        raw=Path('/sys/fs/cgroup/memory.max').read_text().strip()
+        if raw!='max':limit=int(raw)
+    except (OSError,ValueError):pass
+    return dict(label=label,scope='weights_only',weight_bytes=size,gpus=gpus,weights_fit_now=now if gpus else None,
+                ram_total_bytes=total,ram_available_bytes=total-used if total is not None and used is not None else None,
+                deck_ram_limit_bytes=limit,sampled_at=hardware.get('sampled_at'),
+                explanation='Untergrenze: nur diese Datei, 5 % GPU-Reserve (mindestens 512 MiB). Kontext/KV-Cache, Aktivierungen, Textencoder und VAE kommen hinzu. Kein Startversprechen; GPUs werden nicht addiert.')
+
+def overview(files,hardware):
+    # Do not rank a tiny LoRA, VAE or encoder as the base model. Prefer GGUF
+    # variants when available; otherwise inspect main safetensors weights.
+    weights=[f for f in files if f['name'].lower().endswith(('.gguf','.safetensors')) and not
+             re.search(r'(?:vae|text.encoder|tokenizer|mmproj|lora|adapter|clip|safety.checker|mlx)',f['name'],re.I)]
+    gguf=[f for f in weights if f['name'].lower().endswith('.gguf')]
+    if gguf:weights=gguf
+    groups={};variants=[]
+    for f in weights:
+        match=re.match(r'(.*)-(\d{5})-of-(\d{5})(\.(?:gguf|safetensors))$',f['name'],re.I)
+        if match:
+            prefix,idx,count,suffix=match.groups();key=(prefix,int(count),suffix)
+            groups.setdefault(key,[]).append((int(idx),f))
+        else:variants.append(f)
+    incomplete=False
+    for (prefix,count,suffix),parts in groups.items():
+        if {i for i,_ in parts}==set(range(1,count+1)):
+            variants.append(dict(name=prefix+' (alle '+str(count)+' Teile)',size=sum(f['size'] for _,f in parts)))
+        else:incomplete=True
+    if not variants:return dict(min_bytes=None,max_bytes=None,label='Modellgröße nicht eindeutig ermittelbar; Dateiauswahl öffnen',scope='unknown')
+    smallest=min(variants,key=lambda x:x['size']);largest=max(variants,key=lambda x:x['size'])
+    return dict(min_bytes=smallest['size'],max_bytes=largest['size'],file_count=len(variants),
+                **assess(smallest['size'],hardware,smallest['name']),basis=smallest['name'],incomplete_shards=incomplete,
+                size_note='Hauptgewichte (GGUF bevorzugt), vollständige Shards zusammengefasst; nicht Gesamtpaket. Zusatzmodelle und Laufzeitbedarf fehlen.')