Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,14 +3,14 @@ import streamlit as st
 
 # Fix permission errors by forcing writable directories
 os.environ["HF_HOME"] = "/tmp/hf_home"
-os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
+# os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
 os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/sbert"
 os.environ["XDG_CONFIG_HOME"] = "/tmp/.config"
 os.environ["STREAMLIT_HOME"] = "/tmp/.streamlit"
 
 # Make those folders if not already present
-for path in ["/tmp/hf_home", "/tmp/hf_cache", "/tmp/sbert", "/tmp/.config", "/tmp/.streamlit"]:
-    os.makedirs(path, exist_ok=True)
+# for path in ["/tmp/hf_home", "/tmp/hf_cache", "/tmp/sbert", "/tmp/.config", "/tmp/.streamlit"]:
+#     os.makedirs(path, exist_ok=True)
 
 # HuggingFace login (requires HF_TOKEN to be added as secret in Hugging Face Spaces)
 from huggingface_hub import login
@@ -107,27 +107,29 @@ def summarize_user_preferences(user_id, top_k=3):
     return ", ".join(brands), ", ".join(styles), " ".join(descs[:top_k])
 
 # ========== APP STARTS ==========
+import streamlit.runtime.metrics_util
+streamlit.runtime.metrics_util._config_file = "/tmp/.config/streamlit/config.toml"
+# os.makedirs("/tmp/.config/streamlit", exist_ok=True)
+
 st.set_page_config("🛍️ Fashion Visual Search")
 st.title("👗 Fashion Visual Search & Outfit Assistant")
 
 image_embeddings, text_embeddings, ids, _, faiss_index, df, user_history, trend_string = load_assets()
 
+# Load models
 clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14", cache_dir="/tmp/hf_cache")
 clip_model.eval()
 clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14", use_fast=True, cache_dir="/tmp/hf_cache")
 
 text_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2', cache_folder="/tmp/sbert")
 
-bnb_config = BitsAndBytesConfig(load_in_4bit=True)
 model_id = "google/gemma-3-4b-it"
 model = Gemma3ForConditionalGeneration.from_pretrained(
     model_id,
-    torch_dtype=torch.float16,
+    torch_dtype=torch.float32,
     device_map="auto",
-    quantization_config=bnb_config,
     cache_dir="/tmp/hf_cache"
-)
-model.eval()
+).eval()
 gemma_processor = AutoProcessor.from_pretrained(model_id, cache_dir="/tmp/hf_cache")
 
 username = st.text_input("👤 Enter your username:")