import os # Quiet TensorFlow logs (must be set before importing tensorflow) os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3") os.environ.setdefault("TF_ENABLE_ONEDNN_OPTS", "0") import gradio as gr import joblib import numpy as np import pandas as pd from propy import AAComposition, CTD from math import expm1 # --------------------------------------------------------------------------- # LAZY LOADING — keeps the free 16GB Space from OOM-ing at startup. # Heavy libs (TF, torch, ProtBert) load only when first needed. # --------------------------------------------------------------------------- _amp_model = None _amp_scaler = None _protbert_tokenizer = None _protbert_model = None _torch = None _device = None def get_amp_model(): global _amp_model, _amp_scaler if _amp_model is None: from tensorflow.keras.models import load_model _amp_model = load_model("Comb1_aac_ctd_RFE_selected_features_model.keras") _amp_scaler = joblib.load("Comb1_aac_ctd_RFE_selected_features_scaler.joblib") return _amp_model, _amp_scaler def get_protbert(): global _protbert_tokenizer, _protbert_model, _torch, _device if _protbert_model is None: import torch from transformers import BertTokenizer, BertModel _torch = torch _device = torch.device("cuda" if torch.cuda.is_available() else "cpu") _protbert_tokenizer = BertTokenizer.from_pretrained( "Rostlab/prot_bert", do_lower_case=False ) _protbert_model = BertModel.from_pretrained("Rostlab/prot_bert") _protbert_model = _protbert_model.to(_device).eval() return _protbert_tokenizer, _protbert_model, _torch, _device # --------------------------------------------------------------------------- # The EXACT 343 features the scaler was fit on, IN THE EXACT TRAINING ORDER. # The scaler was fit on a numpy array (no stored names), so order is critical: # we must select these columns in this order BEFORE calling scaler.transform(). # --------------------------------------------------------------------------- selected_features = [ "_PolarizabilityC1", "_PolarizabilityC2", "_PolarizabilityC3", "_SolventAccessibilityC1", "_SolventAccessibilityC2", "_SolventAccessibilityC3", "_SecondaryStrC1", "_SecondaryStrC2", "_SecondaryStrC3", "_ChargeC1", "_ChargeC2", "_ChargeC3", "_PolarityC1", "_PolarityC2", "_PolarityC3", "_NormalizedVDWVC1", "_NormalizedVDWVC2", "_NormalizedVDWVC3", "_HydrophobicityC1", "_HydrophobicityC2", "_HydrophobicityC3", "_PolarizabilityT12", "_PolarizabilityT13", "_PolarizabilityT23", "_SolventAccessibilityT12", "_SolventAccessibilityT13", "_SolventAccessibilityT23", "_SecondaryStrT12", "_SecondaryStrT13", "_SecondaryStrT23", "_ChargeT12", "_ChargeT13", "_ChargeT23", "_PolarityT12", "_PolarityT13", "_PolarityT23", "_NormalizedVDWVT12", "_NormalizedVDWVT13", "_NormalizedVDWVT23", "_HydrophobicityT12", "_HydrophobicityT13", "_HydrophobicityT23", "_PolarizabilityD1001", "_PolarizabilityD1025", "_PolarizabilityD1050", "_PolarizabilityD1075", "_PolarizabilityD1100", "_PolarizabilityD2001", "_PolarizabilityD2025", "_PolarizabilityD2050", "_PolarizabilityD2075", "_PolarizabilityD2100", "_PolarizabilityD3001", "_PolarizabilityD3025", "_PolarizabilityD3050", "_PolarizabilityD3075", "_PolarizabilityD3100", "_SolventAccessibilityD1001", "_SolventAccessibilityD1025", "_SolventAccessibilityD1050", "_SolventAccessibilityD1075", "_SolventAccessibilityD1100", "_SolventAccessibilityD2001", "_SolventAccessibilityD2025", "_SolventAccessibilityD2050", "_SolventAccessibilityD2075", "_SolventAccessibilityD2100", "_SolventAccessibilityD3001", "_SolventAccessibilityD3025", "_SolventAccessibilityD3050", "_SolventAccessibilityD3075", "_SolventAccessibilityD3100", "_SecondaryStrD1001", "_SecondaryStrD1025", "_SecondaryStrD1050", "_SecondaryStrD1075", "_SecondaryStrD1100", "_SecondaryStrD2001", "_SecondaryStrD2025", "_SecondaryStrD2050", "_SecondaryStrD2075", "_SecondaryStrD2100", "_SecondaryStrD3001", "_SecondaryStrD3025", "_SecondaryStrD3050", "_SecondaryStrD3075", "_SecondaryStrD3100", "_ChargeD1001", "_ChargeD1025", "_ChargeD1050", "_ChargeD1075", "_ChargeD1100", "_ChargeD2001", "_ChargeD2025", "_ChargeD2050", "_ChargeD2075", "_ChargeD3001", "_ChargeD3025", "_ChargeD3050", "_ChargeD3075", "_ChargeD3100", "_PolarityD1001", "_PolarityD1025", "_PolarityD1050", "_PolarityD1075", "_PolarityD1100", "_PolarityD2001", "_PolarityD2025", "_PolarityD2050", "_PolarityD2075", "_PolarityD2100", "_PolarityD3001", "_PolarityD3025", "_PolarityD3050", "_PolarityD3075", "_PolarityD3100", "_NormalizedVDWVD1001", "_NormalizedVDWVD1025", "_NormalizedVDWVD1050", "_NormalizedVDWVD1075", "_NormalizedVDWVD1100", "_NormalizedVDWVD2001", "_NormalizedVDWVD2025", "_NormalizedVDWVD2050", "_NormalizedVDWVD2075", "_NormalizedVDWVD2100", "_NormalizedVDWVD3001", "_NormalizedVDWVD3025", "_NormalizedVDWVD3050", "_NormalizedVDWVD3075", "_NormalizedVDWVD3100", "_HydrophobicityD1001", "_HydrophobicityD1025", "_HydrophobicityD1050", "_HydrophobicityD1075", "_HydrophobicityD1100", "_HydrophobicityD2001", "_HydrophobicityD2025", "_HydrophobicityD2050", "_HydrophobicityD2075", "_HydrophobicityD2100", "_HydrophobicityD3001", "_HydrophobicityD3025", "_HydrophobicityD3050", "_HydrophobicityD3075", "_HydrophobicityD3100", "A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V", "AR", "AD", "AQ", "AG", "AL", "AK", "AF", "AP", "AT", "AV", "RA", "RC", "RE", "RG", "RI", "RL", "RS", "RT", "RV", "NR", "NC", "NG", "NI", "NP", "NS", "NY", "NV", "DR", "DN", "DC", "DE", "DG", "DF", "DS", "DT", "DY", "CR", "CN", "CD", "CC", "CI", "CL", "CK", "CT", "CY", "CV", "EA", "ER", "ED", "EC", "EE", "EG", "EI", "EL", "EK", "EF", "EP", "ET", "EV", "QN", "QF", "QV", "GA", "GR", "GC", "GE", "GG", "GI", "GL", "GK", "GF", "GP", "GY", "HA", "HP", "HT", "IA", "IR", "ID", "II", "IL", "IF", "IP", "IS", "IV", "LA", "LR", "LD", "LC", "LG", "LI", "LK", "LM", "LF", "LS", "LT", "LY", "LV", "KA", "KN", "KC", "KG", "KI", "KL", "KK", "KP", "KY", "MA", "MD", "ME", "MI", "MK", "MF", "MP", "MS", "MV", "FR", "FE", "FQ", "FG", "FL", "FF", "FS", "FT", "FY", "FV", "PA", "PR", "PC", "PE", "PL", "PK", "PP", "PS", "PV", "SA", "SR", "SD", "SC", "SG", "SH", "SI", "SL", "SP", "ST", "SY", "TA", "TR", "TC", "TE", "TQ", "TG", "TI", "TL", "TP", "TS", "TV", "WA", "YN", "YD", "YC", "YQ", "YG", "YP", "VA", "VR", "VD", "VC", "VE", "VG", "VI", "VL", "VK", "VS", "VT", "VY", "VV" ] assert len(selected_features) == 343, f"Expected 343 features, got {len(selected_features)}" def keras_predict_proba(X): """Return probabilities as [P(Non-AMP), P(AMP)] for LIME (X already scaled).""" amp_model, _ = get_amp_model() preds = amp_model.predict(X, verbose=0) if preds.ndim == 1 or preds.shape[1] == 1: preds = preds.reshape(-1, 1) return np.hstack([1 - preds, preds]) # sigmoid output assumed = P(AMP) return preds def extract_features(sequence): """Compute CTD + AAC, select the 343 training columns IN ORDER, then scale.""" sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"]) if len(sequence) < 10: return "Error: Sequence too short." try: _, amp_scaler = get_amp_model() # Compute full feature pool ctd_features = CTD.CalculateCTD(sequence) aac = AAComposition.CalculateAADipeptideComposition(sequence) # Merge everything into one lookup dict pool = {} pool.update(ctd_features) pool.update(aac) # Verify all needed features are present missing = [f for f in selected_features if f not in pool] if missing: return f"Error: Missing features from propy: {missing[:5]}..." # Build the 343-wide row IN THE EXACT TRAINING ORDER, THEN scale. ordered_values = [pool[f] for f in selected_features] feature_row = np.array(ordered_values, dtype=np.float64).reshape(1, -1) scaled = amp_scaler.transform(feature_row) # scaler expects exactly 343 cols return scaled.astype(np.float32) except Exception as e: return f"Error in feature extraction: {str(e)}" def predictmic(sequence): sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"]) if len(sequence) < 10: return {"Error": "Sequence too short or invalid."} tokenizer, protbert_model, torch, device = get_protbert() 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_model(**tokens) embedding = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy().reshape(1, -1) 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 = round(expm1(mic_log), 3) mic_results[bacterium] = mic except Exception as e: mic_results[bacterium] = f"Error: {str(e)}" return mic_results def full_prediction(sequence): features = extract_features(sequence) if isinstance(features, str): return features amp_model, _ = get_amp_model() raw_pred = amp_model.predict(features, verbose=0) if raw_pred.ndim == 1 or raw_pred.shape[1] == 1: prob_amp = float(raw_pred.flatten()[0]) # sigmoid output assumed = P(AMP) if prob_amp >= 0.5: prediction = 1 confidence = round(prob_amp * 100, 2) else: prediction = 0 confidence = round((1 - prob_amp) * 100, 2) else: class_idx = int(np.argmax(raw_pred[0])) prediction = class_idx confidence = round(float(raw_pred[0][class_idx]) * 100, 2) # Label convention: 1 = AMP, 0 = Non-AMP (swap if your model is reversed) amp_result = "Antimicrobial Peptide (AMP)" if prediction == 1 else "Non-AMP" result = f"Prediction: {amp_result}\nConfidence: {confidence}%\n" if prediction == 1: mic_values = predictmic(sequence) result += "\nPredicted MIC Values (μM):\n" for org, mic in mic_values.items(): result += f"- {org}: {mic}\n" else: result += "\nMIC prediction skipped for Non-AMP sequences.\n" try: from lime.lime_tabular import LimeTabularExplainer sample_data = np.random.rand(100, len(selected_features)) explainer = LimeTabularExplainer( training_data=sample_data, feature_names=selected_features, class_names=["Non-AMP", "AMP"], mode="classification" ) explanation = explainer.explain_instance( data_row=features[0], predict_fn=keras_predict_proba, num_features=10 ) result += "\nTop Features Influencing Prediction:\n" for feat, weight in explanation.as_list(): result += f"- {feat}: {round(weight, 4)}\n" except Exception as e: result += f"\nLIME explanation failed: {str(e)}\n" return result iface = gr.Interface( fn=full_prediction, inputs=gr.Textbox(label="Enter Protein Sequence"), outputs=gr.Textbox(label="Results"), title="AMP & MIC Predictor + LIME Explanation", description="Paste an amino acid sequence (≥10 characters). Get AMP classification, MIC predictions, and LIME interpretability insights." ) iface.launch()