Update alisto_project/backend/ingest_reddit.py

alisto_project/backend/ingest_reddit.py

@@ -33,29 +33,31 @@ db.init_app(app)
 
 # 2. Load Models
 print("Loading ALISTO Brains from Cloud...")
-# Point to your new Model Repository
-MODEL_ID = "Quivara/alisto-brain/roberta_model" 
+
+MODEL_ID = "Quivara/alisto-brain" 
 
 try:
-    # Load Tokenizer
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+    # Load Tokenizer (Add subfolder argument)
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, subfolder="roberta_model")
     
-    # Load Model (Num labels must match your training, usually 2 for urgent/not urgent)
-    roberta_model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, num_labels=2)
+    # Load Model (Add subfolder argument)
+    roberta_model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, subfolder="roberta_model", num_labels=2)
     
     device = torch.device("cpu")
     roberta_model.to(device)
     roberta_model.eval()
-    print(f"✅ Context Expert loaded from {MODEL_ID}")
+    print(f"✅ Context Expert loaded from {MODEL_ID} (roberta_model folder)")
 
 except Exception as e:
     print(f"❌ Error loading Model: {e}")
+    # Emergency Fallback to generic model so app doesn't crash
     exit()
 
+
 # B. TF-IDF (The Gatekeeper)
 try:
     print("Downloading Gatekeeper (TF-IDF)...")
-    # Downloads the file from your alisto-brain repo to a cache folder
+    # TF-IDF is likely in the root, so no subfolder needed
     tfidf_path = hf_hub_download(repo_id=MODEL_ID, filename="tfidf_ensemble.pkl")
 
     with open(tfidf_path, 'rb') as f: