Update main.py

main.py

@@ -8,32 +8,38 @@ from app.api.routes import router
 from app.core.config import settings
 
 # ====== Step 1: Safe Hugging Face cache location ======
-os.environ["HF_HOME"] = "./hf_cache"
-os.environ["TRANSFORMERS_CACHE"] = "./hf_cache"
-os.environ["HF_HUB_CACHE"] = "./hf_cache"
-os.environ["HF_DATASETS_CACHE"] = "./hf_cache"
-os.environ["HF_METRICS_CACHE"] = "./hf_cache"
+# For Hugging Face Spaces, use /tmp to ensure write access
+HF_CACHE_DIR = "/tmp/hf_cache"
+os.makedirs(HF_CACHE_DIR, exist_ok=True)
+
+os.environ["HF_HOME"] = HF_CACHE_DIR
+os.environ["TRANSFORMERS_CACHE"] = HF_CACHE_DIR
+os.environ["HF_HUB_CACHE"] = HF_CACHE_DIR
+os.environ["HF_DATASETS_CACHE"] = HF_CACHE_DIR
+os.environ["HF_METRICS_CACHE"] = HF_CACHE_DIR
 
 # ====== Step 2: Logging setup ======
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 logger.info("Downloading model and dataset from Hugging Face Hub...")
 
-# ====== Step 3: Load your Hugging Face token ======
+# ====== Step 3: Load your Hugging Face token from env ======
 HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    raise RuntimeError("HF_TOKEN environment variable not set")
 
-# ====== Step 4: Download model and FAISS index ======
+# ====== Step 4: Download model and FAISS index into /tmp ======
 MODEL_PATH = snapshot_download(
     repo_id="negi2725/legalBert",
     token=HF_TOKEN,
-    local_dir="./models/legalbert_model",
+    local_dir="/tmp/legalbert_model",
     local_dir_use_symlinks=False
 )
 
 FAISS_INDEX_PATH = snapshot_download(
     repo_id="negi2725/dataRag",
     token=HF_TOKEN,
-    local_dir="./faiss_indexes",
+    local_dir="/tmp/faiss_indexes",
     local_dir_use_symlinks=False
 )