washing space

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import tempfile, os, shutil, zipfile, glob, time
 import os
 from PIL import Image, ImageDraw, ImageFont
+os.environ["GRADIO_TEMP_DIR"] = "/tmp/gradio"
+os.environ["TMPDIR"] = "/tmp"
 os.environ["HF_HOME"] = "/tmp/hfhome"
 os.environ["HF_HUB_CACHE"] = "/tmp/hfhome/hub"
 os.environ["XDG_CACHE_HOME"] = "/tmp/xdgcache"   # au cas où
@@ -27,7 +29,8 @@ for _p in ["/home/user/.cache", "/home/user/.local/share/Trash", "/home/user/app
     _safe_rmtree(_p)
 CACHE_DIR = "/tmp/hfhome/transformers"  # même valeur que ci-dessus
 
-
+for _p in ["/home/user/.cache", "/home/user/.local/share/Trash", "/home/user/app/tmp","/home/user/.gradio", "/tmp/gradio", "/tmp/hfhome", "/tmp/xdgcache", "/tmp/torchhome"]:
+    _safe_rmtree(_p)
 
 # ======== CONFIG ========
 DEVICE = "cpu"                       # Space CPU
@@ -45,6 +48,28 @@ ar_model = SegformerForSemanticSegmentation.from_pretrained(
 ce_model = SegformerForSemanticSegmentation.from_pretrained(
     CE_MODEL_ID, cache_dir=CACHE_DIR).to(DEVICE).eval()
 
+
+# ---- PURGE OPTIONNELLE DU CACHE HF (réduit l'usage disque) ----
+try:
+    from huggingface_hub import scan_cache_dir
+    # CACHE_DIR = "/tmp/hfhome/transformers"  -> on monte d'un cran: "/tmp/hfhome"
+    cache_root = os.path.abspath(os.path.join(CACHE_DIR, ".."))
+    info = scan_cache_dir(cache_root)
+
+    # ne garder que les 2 dépôts utiles à l'app
+    keep = {AR_MODEL_ID, CE_MODEL_ID}
+    for repo in info.repos:
+        if getattr(repo, "repo_id", None) not in keep:
+            # supprime entièrement le repo non utilisé
+            repo_path = getattr(repo, "repo_path", None)
+            if repo_path and os.path.exists(repo_path):
+                _safe_rmtree(repo_path)
+except Exception:
+    # pas bloquant si la structure du cache change
+    pass
+
+
+
 transform = transforms.Compose([
     transforms.Resize(TARGET_SIZE),
     transforms.ToTensor(),
@@ -114,6 +139,19 @@ def overlay_counter(img_np: np.ndarray, idx: int, total: int) -> Image.Image:
     return im
 
 def run_job(files_or_zip, use_demo=False, progress=gr.Progress()):
+    # garde: espace disque minimal requis (ex: 5 Go)
+    MIN_FREE = 5 * (1024**3)
+    try:
+        usage = shutil.disk_usage("/")
+        if usage.free < MIN_FREE:
+            yield None, (
+                "⚠️ Low free disk on Space. Please retry later "
+                "(administrator: increase persistent storage or clean caches)."
+            ), current_preview, None
+            return
+    except Exception:
+        pass
+   
     global BUSY
     first_img_time = None  # durée mesurée sur la 1ère image
     current_preview = PLACEHOLDER.copy()
@@ -353,6 +391,10 @@ def run_job(files_or_zip, use_demo=False, progress=gr.Progress()):
         # ZIP final
         zip_path = os.path.join(workdir, "results.zip")
         shutil.make_archive(zip_path[:-4], "zip", out_root)
+        
+        #  libère l’espace immédiatement : on supprime le dossier des sorties
+        _safe_rmtree(out_root)
+        
         elapsed = time.time() - start
         yield zip_path, f"Done. {processed} image(s) in {elapsed:.1f}s. Download the ZIP below.", current_preview, workdir
         # On ne supprime pas ici pour laisser le téléchargement se faire
@@ -372,6 +414,19 @@ def cleanup_all():
     # purge des caches au cas où
     for _p in ["/tmp/hfhome", "/tmp/xdgcache", "/tmp/torchhome"]:
         _safe_rmtree(_p)
+        
+    for wd in list(ACTIVE_WORKDIRS):
+        try:
+            _safe_rmtree(wd)
+            ACTIVE_WORKDIRS.discard(wd)
+        except Exception:
+            pass
+    # Purge de tous les caches/temp usuels
+    for _p in [
+        "/tmp/gradio", "/tmp/hfhome", "/tmp/xdgcache", "/tmp/torchhome",
+        "/home/user/.cache", "/home/user/.gradio", "/home/user/.local/share/Trash"
+    ]:
+        _safe_rmtree(_p)
 
 with gr.Blocks(theme=gr.themes.Soft(), title="MicroDeepAerenchyma — Segmentation (CPU)") as demo:
     BANNER_URL = "https://huggingface.co/spaces/RomainFernandez/DeepAerenchyma/resolve/main/banner.png"