try to fix storage

app.py

@@ -1,20 +1,80 @@
+# ---- BOOTSTRAP: keep storage under control on Spaces ----
+import os, shutil, subprocess
+from huggingface_hub import scan_cache_dir, snapshot_download
+
+# 1) Put ALL caches in /data so they’re manageable & persistent
+os.makedirs("/data/.cache", exist_ok=True)
+os.environ.setdefault("XDG_CACHE_HOME", "/data/.cache")
+os.environ.setdefault("HF_HOME", "/data/.cache/huggingface")
+os.environ.setdefault("HF_HUB_CACHE", "/data/.cache/huggingface/hub")
+os.environ.setdefault("TRANSFORMERS_CACHE", "/data/.cache/huggingface/transformers")
+os.environ.setdefault("DATASETS_CACHE", "/data/.cache/huggingface/datasets")
+
+# 2) Prune old HF cache revisions (keeps current blobs, deletes stale revs)
+try:
+    cache = scan_cache_dir(os.environ["HF_HUB_CACHE"])
+    cache.delete_revisions([rev for rev in cache.revisions])
+except Exception as e:
+    print(f"[cache prune] skipped: {e}")
+
+# (Optional) light guard: trim pip wheel cache
+try:
+    subprocess.run(["pip", "cache", "purge"], check=False)
+except Exception:
+    pass
+# ---- END BOOTSTRAP ----
+
 import gradio as gr
 import sys
 import pandas as pd
 from transformers import AutoTokenizer, AutoModel, AutoConfig
-# Add the current directory to the system path
+
+# If you want fully reproducible rebuilds, set these in Space → Settings → Variables
+# (or leave blank to use latest)
+MODEL_ID = "ChatterjeeLab/MetaLATTE"
+TOKENIZER_ID = "facebook/esm2_t33_650M_UR50D"
+MODEL_REV = os.getenv("MODEL_REV", "")         # e.g. "a1b2c3d"
+TOKENIZER_REV = os.getenv("TOKENIZER_REV", "") # e.g. "9f8e7d6"
+
+# Prefer downloading *exactly* what you need to /data and load locally.
+# This avoids multiple revision copies over time.
+def maybe_snapshot(repo_id, revision, allow_patterns):
+    kw = dict(repo_id=repo_id, local_dir=None, ignore_regex=None)
+    if revision:
+        kw["revision"] = revision
+    # Download to HF cache in /data; return the resolved local dir
+    return snapshot_download(allow_patterns=allow_patterns, **kw)
+
+# Download tokenizer files only (small)
+esm_local = maybe_snapshot(
+    TOKENIZER_ID, TOKENIZER_REV,
+    allow_patterns=[
+        "tokenizer.json","tokenizer_config.json","vocab.*","merges.*",
+        "special_tokens_map.json","*.model","tokenizer*.txt","spiece.*","*.tiktoken"
+    ]
+)
+
+# Download MetaLATTE (weights + config only)
+metalatte_local = maybe_snapshot(
+    MODEL_ID, MODEL_REV,
+    allow_patterns=["*.json","*.safetensors","*.bin","*.model","*.txt"]  # keep it tight
+)
+
+# Add the current directory to the system path for your custom code
 metalatte_path = '.'
 sys.path.insert(0, metalatte_path)
 
 # Import the custom configuration and model
 from configuration import MetaLATTEConfig
-from modeling_metalatte  import MultitaskProteinModel
+from modeling_metalatte import MultitaskProteinModel
 AutoConfig.register("metalatte", MetaLATTEConfig)
 AutoModel.register(MetaLATTEConfig, MultitaskProteinModel)
 
-tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
-config = AutoConfig.from_pretrained("ChatterjeeLab/MetaLATTE")
-model = AutoModel.from_pretrained("ChatterjeeLab/MetaLATTE", config=config)
+# Load from the local snapshot dirs (avoids re-downloading on rebuilds)
+tokenizer = AutoTokenizer.from_pretrained(esm_local, local_files_only=True)
+config = AutoConfig.from_pretrained(metalatte_local, local_files_only=True)
+model = AutoModel.from_pretrained(metalatte_local, config=config, local_files_only=True)
+
 
 def predict(sequence):
     inputs = tokenizer(sequence, return_tensors="pt")