import gradio as gr import joblib import numpy as np import pandas as pd from propy import AAComposition, Autocorrelation, CTD, PseudoAAC from sklearn.preprocessing import MinMaxScaler # Load model and scaler model = joblib.load("RF.joblib") scaler = joblib.load("norm (4).joblib") # Feature list (KEEP THIS CONSISTENT) selected_features = [ "_SolventAccessibilityC3", "_SecondaryStrC1", "_SecondaryStrC3", "_ChargeC1", "_PolarityC1", "_NormalizedVDWVC1", "_HydrophobicityC3", "_SecondaryStrT23", "_PolarizabilityD1001", "_PolarizabilityD2001", "_PolarizabilityD3001", "_SolventAccessibilityD1001", "_SolventAccessibilityD2001", "_SolventAccessibilityD3001", "_SecondaryStrD1001", "_SecondaryStrD1075", "_SecondaryStrD2001", "_SecondaryStrD3001", "_ChargeD1001", "_ChargeD1025", "_ChargeD2001", "_ChargeD3075", "_ChargeD3100", "_PolarityD1001", "_PolarityD1050", "_PolarityD2001", "_PolarityD3001", "_NormalizedVDWVD1001", "_NormalizedVDWVD2001", "_NormalizedVDWVD2025", "_NormalizedVDWVD2050", "_NormalizedVDWVD3001", "_HydrophobicityD1001", "_HydrophobicityD2001", "_HydrophobicityD3001", "_HydrophobicityD3025", "A", "R", "D", "C", "E", "Q", "H", "I", "M", "P", "Y", "V", "AR", "AV", "RC", "RL", "RV", "CR", "CC", "CL", "CK", "EE", "EI", "EL", "HC", "IA", "IL", "IV", "LA", "LC", "LE", "LI", "LT", "LV", "KC", "MA", "MS", "SC", "TC", "TV", "YC", "VC", "VE", "VL", "VK", "VV", "MoreauBrotoAuto_FreeEnergy30", "MoranAuto_Hydrophobicity2", "MoranAuto_Hydrophobicity4", "GearyAuto_Hydrophobicity20", "GearyAuto_Hydrophobicity24", "GearyAuto_Hydrophobicity26", "GearyAuto_Hydrophobicity27", "GearyAuto_Hydrophobicity28", "GearyAuto_Hydrophobicity29", "GearyAuto_Hydrophobicity30", "GearyAuto_AvFlexibility22", "GearyAuto_AvFlexibility26", "GearyAuto_AvFlexibility27", "GearyAuto_AvFlexibility28", "GearyAuto_AvFlexibility29", "GearyAuto_AvFlexibility30", "GearyAuto_Polarizability22", "GearyAuto_Polarizability24", "GearyAuto_Polarizability25", "GearyAuto_Polarizability27", "GearyAuto_Polarizability28", "GearyAuto_Polarizability29", "GearyAuto_Polarizability30", "GearyAuto_FreeEnergy24", "GearyAuto_FreeEnergy25", "GearyAuto_FreeEnergy30", "GearyAuto_ResidueASA21", "GearyAuto_ResidueASA22", "GearyAuto_ResidueASA23", "GearyAuto_ResidueASA24", "GearyAuto_ResidueASA30", "GearyAuto_ResidueVol21", "GearyAuto_ResidueVol24", "GearyAuto_ResidueVol25", "GearyAuto_ResidueVol26", "GearyAuto_ResidueVol28", "GearyAuto_ResidueVol29", "GearyAuto_ResidueVol30", "GearyAuto_Steric18", "GearyAuto_Steric21", "GearyAuto_Steric26", "GearyAuto_Steric27", "GearyAuto_Steric28", "GearyAuto_Steric29", "GearyAuto_Steric30", "GearyAuto_Mutability23", "GearyAuto_Mutability25", "GearyAuto_Mutability26", "GearyAuto_Mutability27", "GearyAuto_Mutability28", "GearyAuto_Mutability29", "GearyAuto_Mutability30", "APAAC1", "APAAC4", "APAAC5", "APAAC6", "APAAC8", "APAAC9", "APAAC12", "APAAC13", "APAAC15", "APAAC18", "APAAC19", "APAAC24" ] def extract_features(sequence): all_features_dict = {} # Calculate all dipeptide features dipeptide_features = AAComposition.CalculateAADipeptideComposition(sequence) # Add only the first 420 features to the dictionary first_420_keys = list(dipeptide_features.keys())[:420] # Get the first 420 keys filtered_dipeptide_features = {key: dipeptide_features[key] for key in first_420_keys} ctd_features = CTD.CalculateCTD(sequence) auto_features = Autocorrelation.CalculateAutoTotal(sequence) pseudo_features = PseudoAAC.GetAPseudoAAC(sequence, lamda=9) all_features_dict.update(ctd_features) all_features_dict.update(filtered_dipeptide_features) all_features_dict.update(auto_features) all_features_dict.update(pseudo_features) # Convert all features to DataFrame feature_df_all = pd.DataFrame([all_features_dict]) # Normalize ALL features normalized_feature_array = scaler.transform(feature_df_all.values) # Normalize the numpy array normalized_feature_df = pd.DataFrame(normalized_feature_array, columns=feature_df_all.columns) # Convert back to DataFrame with original column names # Select features AFTER normalization feature_df_selected = normalized_feature_df[selected_features].copy() feature_df_selected = feature_df_selected.fillna(0) # Fill missing if any after selection (though unlikely now) feature_array = feature_df_selected.values return feature_array def predict(sequence): """Predicts whether the input sequence is an AMP.""" features = extract_features(sequence) if isinstance(features, str) and features.startswith("Error:"): return features prediction = model.predict(features)[0] probabilities = model.predict_proba(features)[0] if prediction == 0: return f"{probabilities[0] * 100:.2f}% chance of being an Antimicrobial Peptide (AMP)" else: return f"{probabilities[1] * 100:.2f}% chance of being Non-AMP" def predictmic(sequence): import torch from transformers import BertTokenizer, BertModel import numpy as np import pickle from math import expm1 # === Load ProtBert model === tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False) model = BertModel.from_pretrained("Rostlab/prot_bert") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device).eval() # === Preprocess input sequence === sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"]) if len(sequence) < 10: return {"Error": "Sequence too short or invalid. Must contain at least 10 valid amino acids."} # === Tokenize & embed using mean pooling === 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 = model(**tokens) embedding = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy().reshape(1, -1) # Shape: (1, 1024) # === MIC models and scalers for each bacterium === 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" } } mic_results = {} for bacterium, cfg in bacteria_config.items(): try: # === Load scaler and transform === with open(cfg["scaler"], "rb") as f: scaler = pickle.load(f) scaled = scaler.transform(embedding) # === Apply PCA if exists === if cfg["pca"] is not None: with open(cfg["pca"], "rb") as f: pca = pickle.load(f) transformed = pca.transform(scaled) else: transformed = scaled # === Load model and predict === with open(cfg["model"], "rb") as f: mic_model = pickle.load(f) mic_log = mic_model.predict(transformed)[0] mic = round(expm1(mic_log), 3) # Inverse of log1p used in training mic_results[bacterium] = mic except Exception as e: mic_results[bacterium] = f"Error: {str(e)}" return mic_results def full_prediction(sequence): # AMP prediction features = extract_features(sequence) if isinstance(features, str) and features.startswith("Error:"): return "Error", 0.0, {} prediction = model.predict(features)[0] probabilities = model.predict_proba(features)[0] amp_result = "Antimicrobial Peptide (AMP)" if prediction == 0 else "Non-AMP" confidence = round(probabilities[0 if prediction == 0 else 1] * 100, 2) # MIC prediction mic_values = predictmic(sequence) return amp_result, f"{confidence}%", mic_values import gradio as gr iface = gr.Interface( fn=full_prediction, inputs=gr.Textbox(label="Enter Protein Sequence"), outputs=[ gr.Label(label="AMP Classification"), gr.Label(label="Confidence"), gr.JSON(label="Predicted MIC (µg/mL) for Each Bacterium") ], title="AMP & MIC Predictor", description="Enter an amino acid sequence (e.g., FLPVLAGGL) to predict AMP class and MIC values across bacteria." ) iface.launch(share=True)