Update app.py

app.py

@@ -9,12 +9,35 @@ 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  # Safe to import at the top now!
+import spaces 
 
 # ===================================
-# 1. GLOBAL VARIABLES & CACHE
+# 1. LOAD AI MODELS (GLOBALLY CACHED)
 # ===================================
-print("Waking up the Genomic Oracle....\n")
+print("Waking up the Genomic Oracle... Applying ALiBi Meta-Device Bypass.\n")
+
+# A. Kadir's Gatekeeper 
+clf_coding = joblib.load("coding_classifier_universal.joblib")
+
+# B. Base DNABERT 
+tokenizer_base = AutoTokenizer.from_pretrained("DNABERT_Local", trust_remote_code=True)
+# 🚨 _fast_init=False disables the meta device and forces ALiBi to build safely on the CPU
+model_base = AutoModel.from_pretrained("DNABERT_Local", trust_remote_code=True, _fast_init=False)
+model_base.eval()
+
+# C. DNABERT-2 Promoter Model 
+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, _fast_init=False)
+model_promoter.eval()
+
+# D. Multi-Feature LightGBM 
+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 
+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, _fast_init=False)
+model_pheno.eval()
 
 # Structural Feature Dictionary
 FEATURE_DICT = {
@@ -22,49 +45,14 @@ FEATURE_DICT = {
     3: "3' UTR", 4: "Promoter", 5: "Enhancer", 6: "lncRNA"
 }
 
-# This dictionary caches the models so they only load ONCE during the first click
-ORACLE_BRAIN = {}
-
 # ==============================================
 # 2. CORE INFERENCE ENGINE (ZeroGPU Accelerated)
 # ==============================================
-@spaces.GPU  # Teleports this specific math to the A100 GPU
+@spaces.GPU  # ZeroGPU automatically detects the global models and teleports them to the A100 here!
 def run_deep_learning_cascade(dna_sequence):
-    device = torch.device("cuda")  # Wakes up the A100 connection
+    device = torch.device("cuda")  
     clean_seq = "".join(dna_sequence.split()).upper()
     
-    # --- JUST-IN-TIME CACHED LOADING ---
-    if "model_base" not in ORACLE_BRAIN:
-        print("🚀 Allocated GPU is Awake! Loading ALiBi models into VRAM...")
-        
-        ORACLE_BRAIN["clf_coding"] = joblib.load("coding_classifier_universal.joblib")
-        
-        ORACLE_BRAIN["tokenizer_base"] = AutoTokenizer.from_pretrained("DNABERT_Local", trust_remote_code=True)
-        ORACLE_BRAIN["model_base"] = AutoModel.from_pretrained("DNABERT_Local", trust_remote_code=True, low_cpu_mem_usage=False).to(device)
-        ORACLE_BRAIN["model_base"].eval()
-        
-        ORACLE_BRAIN["tokenizer_promoter"] = AutoTokenizer.from_pretrained("llm_promoter_classifier_v2", trust_remote_code=True)
-        ORACLE_BRAIN["model_promoter"] = AutoModelForSequenceClassification.from_pretrained("llm_promoter_classifier_v2", trust_remote_code=True, low_cpu_mem_usage=False).to(device)
-        ORACLE_BRAIN["model_promoter"].eval()
-        
-        lgbm_path = hf_hub_download(repo_id="Geonomic/Genomic-Oracle-Weights", filename="dnabert_lightgbm_model_feature_type.pkl")
-        ORACLE_BRAIN["lightgbm_model"] = joblib.load(lgbm_path)
-        
-        ORACLE_BRAIN["tokenizer_pheno"] = AutoTokenizer.from_pretrained("Geonomic/Genomic-Oracle-Weights", trust_remote_code=True)
-        ORACLE_BRAIN["model_pheno"] = AutoModelForSequenceClassification.from_pretrained("Geonomic/Genomic-Oracle-Weights", trust_remote_code=True, low_cpu_mem_usage=False).to(device)
-        ORACLE_BRAIN["model_pheno"].eval()
-        print("✅ All models successfully loaded into the Oracle Brain!")
-
-    # Retrieve from cache for instant inference
-    clf_coding = ORACLE_BRAIN["clf_coding"]
-    tokenizer_base = ORACLE_BRAIN["tokenizer_base"]
-    model_base = ORACLE_BRAIN["model_base"]
-    tokenizer_promoter = ORACLE_BRAIN["tokenizer_promoter"]
-    model_promoter = ORACLE_BRAIN["model_promoter"]
-    lightgbm_model = ORACLE_BRAIN["lightgbm_model"]
-    tokenizer_pheno = ORACLE_BRAIN["tokenizer_pheno"]
-    model_pheno = ORACLE_BRAIN["model_pheno"]
-
     # --- LEVEL 1: Base Embedding & Kadir's Gatekeeper ---
     inputs = tokenizer_base([clean_seq], return_tensors="pt", max_length=300, truncation=True, padding=True)
     inputs = {k: v.to(device) for k, v in inputs.items()}