Update mic_worker.py

mic_worker.py

@@ -1,90 +0,0 @@
-"""
-MIC prediction worker — runs in a SEPARATE process from the main app.
-
-Why: the main app loads TensorFlow (for the AMP Keras model). Loading PyTorch +
-ProtBert into that same process causes a native-library (OpenMP/MKL) clash and a
-SIGSEGV (exit 139), plus a large memory spike. By running ProtBert here in its own
-short-lived process, TensorFlow and PyTorch never coexist. This process loads
-torch, computes the MIC values, prints them as JSON to stdout, and exits — which
-frees all of its memory.
-
-Usage:
-    python mic_worker.py "<AMINO_ACID_SEQUENCE>"
-Output (stdout, last line):
-    {"E.coli": 12.3, "S.aureus": 4.5, ...}   on success
-    {"error": "..."}                          on failure
-"""
-import os
-# Keep threading modest to limit memory/CPU on the free tier.
-os.environ.setdefault("OMP_NUM_THREADS", "1")
-os.environ.setdefault("MKL_NUM_THREADS", "1")
-os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
-# Cache ProtBert in a stable location so it is downloaded only ONCE and reused
-# on every subsequent prediction (instead of re-downloading each run).
-os.environ.setdefault("HF_HOME", "/app/.cache/huggingface")
-
-import sys
-import json
-from math import expm1
-
-import joblib
-import numpy as np
-
-
-def main():
-    if len(sys.argv) < 2:
-        print(json.dumps({"error": "No sequence provided"}))
-        return
-
-    sequence = sys.argv[1]
-    sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"])
-    if len(sequence) < 10:
-        print(json.dumps({"error": "Sequence too short or invalid."}))
-        return
-
-    try:
-        import torch
-        from transformers import BertTokenizer, BertModel
-        torch.set_num_threads(1)
-
-        device = torch.device("cpu")  # free tier has no GPU
-        tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False)
-        protbert = BertModel.from_pretrained("Rostlab/prot_bert").to(device).eval()
-
-        seq_spaced = ' '.join(list(sequence))
-        tokens = tokenizer(seq_spaced, return_tensors="pt", padding='max_length',
-                           truncation=True, max_length=512)
-        tokens = {k: v.to(device) for k, v in tokens.items()}
-
-        with torch.no_grad():
-            outputs = protbert(**tokens)
-            embedding = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy().reshape(1, -1)
-    except Exception as e:
-        print(json.dumps({"error": f"ProtBert embedding failed: {str(e)}"}))
-        return
-
-    bacteria_config = {
-        "E.coli": {"model": "coli_xgboost_model.pkl", "scaler": "coli_scaler.pkl", "pca": None},
-        "S.aureus": {"model": "aur_xgboost_model.pkl", "scaler": "aur_scaler.pkl", "pca": None},
-        "P.aeruginosa": {"model": "arg_xgboost_model.pkl", "scaler": "arg_scaler.pkl", "pca": None},
-        "K.Pneumonia": {"model": "pne_mlp_model.pkl", "scaler": "pne_scaler.pkl", "pca": "pne_pca.pkl"}
-    }
-
-    mic_results = {}
-    for bacterium, cfg in bacteria_config.items():
-        try:
-            mic_scaler = joblib.load(cfg["scaler"])
-            scaled = mic_scaler.transform(embedding)
-            transformed = joblib.load(cfg["pca"]).transform(scaled) if cfg["pca"] else scaled
-            mic_model = joblib.load(cfg["model"])
-            mic_log = mic_model.predict(transformed)[0]
-            mic_results[bacterium] = round(expm1(float(mic_log)), 3)
-        except Exception as e:
-            mic_results[bacterium] = f"Error: {str(e)}"
-
-    # Final line of stdout = JSON result
-    print(json.dumps(mic_results))
-
-
-if __name__ == "__main__":
-    main()