create flat folder

Dockerfile

@@ -1,29 +1,36 @@
-# Dockerfile
 FROM python:3.10-slim
 WORKDIR /app
 
-# 1) Install HF tooling + your deps
+# 1) Install HF tooling & your deps
 RUN pip install --no-cache-dir huggingface_hub
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# 2) Create a world-writable cache directory
-RUN mkdir -p /app/model && chmod 777 /app/model
+# 2) Create a cache dir
+RUN mkdir -p /app/model_cache
 
-# 3) Download the entire model into /app/model (build time)
+# 3) Download the model into model_cache (build-time)
 RUN python - <<EOF
 from huggingface_hub import snapshot_download
-snapshot_download(
+# downloads into /app/models--numind--NuExtract-1.5-tiny/snapshots/<hash>/
+tmp = snapshot_download(
     repo_id="numind/NuExtract-1.5-tiny",
-    cache_dir="/app",                # places under /app/models--numind--NuExtract-1.5-tiny/…
-    local_dir="model",               # so final: /app/model/models--…/snapshots/<hash>/
-    local_dir_use_symlinks=False     # ensure actual files, no symlinks needed
+    cache_dir="/app",
+    local_dir="model_cache",
+    local_dir_use_symlinks=False
 )
+# flatten: move from the hash-folder up into /app/model_cache
+import os, shutil, glob
+root = os.path.join("/app/model_cache","models--numind--NuExtract-1.5-tiny","snapshots")
+# there should be exactly one subdir under root
+sub = glob.glob(os.path.join(root, "*"))[0]
+for fname in os.listdir(sub):
+    shutil.move(os.path.join(sub, fname), "/app/model_cache")
+# clean up
+shutil.rmtree(os.path.join("/app/model_cache","models--numind--NuExtract-1.5-tiny"))
 EOF
-# 3b) Make sure perms survive into runtime
-RUN chmod -R 755 /app/model
 
-# 4) Copy your FastAPI code
+# 4) Copy your code
 COPY . .
 
 ENV PORT=7860

app.py

@@ -18,15 +18,15 @@ app = FastAPI()
 # // FOR RUNNING IN SPACES
 model_name = "numind/NuExtract-1.5-tiny"
 # Path inside your container
-MODEL_PATH = "/app/model_cache/models--numind--NuExtract-1.5-tiny/snapshots/df52efb3109d324cd52b30728f9e3fdedf19f742"
+# MODEL_PATH = "/app/model_cache/models--numind--NuExtract-1.5-tiny/snapshots/df52efb3109d324cd52b30728f9e3fdedf19f742"
 # If you used local_dir="model", snapshot_download will still create models--… subfolder.
 # You can also symlink or copy it to /app/model directly in Dockerfile.
 
-# // FOR RUNNING LOCALLY
-# MODEL_CACHE_DIR = "/app/model_cache"
+
+MODEL_PATH = "/app/model_cache"
 # model_cache_path = snapshot_download(
 #     repo_id="numind/NuExtract-1.5-tiny",
-#     cache_dir=MODEL_CACHE_DIR
+#     cache_dir=MODEL_PATH
 # )
 
 print(">>> MODEL CACHE PATH:", MODEL_PATH, os.listdir(MODEL_PATH))
@@ -61,7 +61,7 @@ def load_model():
         local_files_only=True,
         trust_remote_code=True
     )
-    print("Model and tokenizer loaded.", flush=True)
+    print("✅ Model and tokenizer loaded from", MODEL_PATH)
 
 def predict_NuExtract(texts, template, batch_size=10, max_length=5096, max_new_tokens=1024):
     print("Starting NuExtract prediction...", flush=True)