add permissions to Dockerfile

Dockerfile

@@ -2,12 +2,26 @@ FROM ubuntu:latest
 LABEL authors="Carla"
 FROM python:3.13
 
-WORKDIR /code
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/code
+#WORKDIR /code
 
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-COPY . .
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/code
+#COPY . .
 
 # Expose FastAPI on port 7860 (required for Spaces)
 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

app/services/ai_service.py

@@ -1,25 +1,24 @@
 import glob
 import os
 
-from huggingface_hub import try_to_load_from_cache
 from transformers import  AutoTokenizer, AutoModelForCausalLM
 
 # Load GPT-2 small (text generation)
 # -----------------------------
 GPT2_MODEL_NAME = "distilgpt2"
-# CACHE_DIR = "./data/hf_cache"
-# os.makedirs(CACHE_DIR, exist_ok=True)
+CACHE_DIR = "./data/hf_cache"
+os.makedirs(CACHE_DIR, exist_ok=True)
 
 
-for lock_file in glob.glob(os.path.join(try_to_load_from_cache('funny-meme-generator', 'cache_dir'), "*.lock")):
+for lock_file in glob.glob(os.path.join(CACHE_DIR, "*.lock")):
     try:
         os.remove(lock_file)
         print(f"Removed lock file: {lock_file}")
     except Exception as e:
         print(f"Could not remove {lock_file}: {e}")
 
-gpt2_tokenizer = AutoTokenizer.from_pretrained(GPT2_MODEL_NAME, cache_dir=try_to_load_from_cache('funny-meme-generator', 'cache_dir'))
-gpt2_model = AutoModelForCausalLM.from_pretrained(GPT2_MODEL_NAME, cache_dir=try_to_load_from_cache('funny-meme-generator', 'cache_dir'))
+gpt2_tokenizer = AutoTokenizer.from_pretrained(GPT2_MODEL_NAME, cache_dir=CACHE_DIR)
+gpt2_model = AutoModelForCausalLM.from_pretrained(GPT2_MODEL_NAME, cache_dir=CACHE_DIR)
 
 # tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 # model = AutoModelWithLMHead.from_pretrained(MODEL_NAME)