Update app.py

app.py

@@ -36,18 +36,18 @@ def run_deep_learning_cascade(dna_sequence):
     clf_coding = joblib.load("coding_classifier_universal.joblib")
     
     tokenizer_base = AutoTokenizer.from_pretrained("DNABERT_Local", trust_remote_code=True)
-    model_base = AutoModel.from_pretrained("DNABERT_Local", trust_remote_code=True).to(device)
+    model_base = AutoModel.from_pretrained("DNABERT_Local", trust_remote_code=True, low_cpu_mem_usage=False).to(device)
     model_base.eval()
     
     tokenizer_promoter = AutoTokenizer.from_pretrained("llm_promoter_classifier_v2", trust_remote_code=True)
-    model_promoter = AutoModelForSequenceClassification.from_pretrained("llm_promoter_classifier_v2", trust_remote_code=True).to(device)
+    model_promoter = AutoModelForSequenceClassification.from_pretrained("llm_promoter_classifier_v2", trust_remote_code=True, low_cpu_mem_usage=False).to(device)
     model_promoter.eval()
     
     lgbm_path = hf_hub_download(repo_id="Geonomic/Genomic-Oracle-Weights", filename="dnabert_lightgbm_model_feature_type.pkl")
     lightgbm_model = joblib.load(lgbm_path)
     
     tokenizer_pheno = AutoTokenizer.from_pretrained("Geonomic/Genomic-Oracle-Weights", trust_remote_code=True)
-    model_pheno = AutoModelForSequenceClassification.from_pretrained("Geonomic/Genomic-Oracle-Weights", trust_remote_code=True).to(device)
+    model_pheno = AutoModelForSequenceClassification.from_pretrained("Geonomic/Genomic-Oracle-Weights", trust_remote_code=True, low_cpu_mem_usage=False).to(device)
     model_pheno.eval()
     
     # --- LEVEL 1: Base Embedding & Kadir's Gatekeeper ---