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| 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 all dipeptide features | |
| all_features_dict.update(dipeptide_features) | |
| auto_features = Autocorrelation.CalculateAutoTotal(sequence) | |
| all_features_dict.update(auto_features) | |
| ctd_features = CTD.CalculateCTD(sequence) | |
| all_features_dict.update(ctd_features) | |
| pseudo_features = PseudoAAC.GetAPseudoAAC(sequence, lamda=9) | |
| all_features_dict.update(pseudo_features) | |
| # Convert feature dictionary to DataFrame, handling missing features | |
| feature_df = pd.DataFrame([all_features_dict]) | |
| # Select features and handle missing columns | |
| feature_df_selected = feature_df[selected_features].copy() # Use .copy() to avoid SettingWithCopyWarning | |
| # Fill missing features with 0 (or another appropriate value) | |
| feature_df_selected = feature_df_selected.fillna(0) | |
| feature_array = feature_df_selected.values # Get numpy array directly | |
| # Normalize the features | |
| normalized_features = scaler.transform(feature_array) | |
| return normalized_features | |
| 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" | |
| # Gradio interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Textbox(label="Enter Protein Sequence"), | |
| outputs=gr.Label(label="Prediction"), | |
| title="AMP Classifier", | |
| description="Enter an amino acid sequence (e.g., FLPVLAGGL) to predict AMP." | |
| ) | |
| iface.launch(share=True) |