Update app.py

app.py

@@ -9,38 +9,37 @@ import torch.nn.functional as F
 from Bio.Blast import NCBIWWW, NCBIXML
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModel
 from huggingface_hub import hf_hub_download
-import spaces  # REQUIRED FOR ZEROGPU
 
 # ===================================
 # 1. LOAD AI MODELS (GLOBALLY CACHED)
 # ===================================
 print("Waking up the Genomic Oracle...\n")
 
-# A. Kadir's Gatekeeper (Local in the Space)
+# A. Kadir's Gatekeeper
 print("Loading Logression Model...\n")
 clf_coding = joblib.load("coding_classifier_universal.joblib")
 
-# B. Base DNABERT (Local in the Space)
-print("Loading foundational DNABERT Model Architecture...\n")
+# B. Base DNABERT 
+print("Loading foundational DNABERT Architecture...\n")
 tokenizer_base = AutoTokenizer.from_pretrained("DNABERT_Local", trust_remote_code=True)
-model_base = AutoModel.from_pretrained("DNABERT_Local", trust_remote_code=True, low_cpu_mem_usage=False)
+model_base = AutoModel.from_pretrained("DNABERT_Local", trust_remote_code=True)
 model_base.eval()
 
-# C. DNABERT-2 Promoter Model (Local in the Space)
+# C. DNABERT-2 Promoter Model 
 print("Loading DNABERT-2 Neural Network...\n")
 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, low_cpu_mem_usage=False)
+model_promoter = AutoModelForSequenceClassification.from_pretrained("llm_promoter_classifier_v2", trust_remote_code=True)
 model_promoter.eval()
 
-# D. Multi-Feature LightGBM (Fetched from Cloud Repo)
+# D. Multi-Feature LightGBM 
 print("Downloading LightGBM from Model Repository...")
 lgbm_path = hf_hub_download(repo_id="Geonomic/Genomic-Oracle-Weights", filename="dnabert_lightgbm_model_feature_type.pkl")
 lightgbm_model = joblib.load(lgbm_path)
 
-# E. Custom ALiBi Lean/Obese BERT (Fetched from Cloud Repo)
+# E. Custom ALiBi Lean/Obese BERT 
 print("Downloading Phenotype BERT from Model Repository...")
 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, low_cpu_mem_usage=False)
+model_pheno = AutoModelForSequenceClassification.from_pretrained("Geonomic/Genomic-Oracle-Weights", trust_remote_code=True)
 model_pheno.eval()
 
 # Structural Feature Dictionary
@@ -52,7 +51,9 @@ FEATURE_DICT = {
 # ==============================================
 # 2. CORE INFERENCE ENGINE (ZeroGPU Accelerated)
 # ==============================================
-@spaces.GPU  # This tells Hugging Face to teleport this specific math to the A100 GPU
+import spaces  # Import ZeroGPU here to prevent the PyTorch Meta Device Error
+
+@spaces.GPU  
 def run_deep_learning_cascade(dna_sequence):
     device = torch.device("cuda")  # Wakes up the A100 connection
     clean_seq = "".join(dna_sequence.split()).upper()