app.py

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
import torch
from transformers import BertTokenizer, BertModel
from lime.lime_tabular import LimeTabularExplainer
from math import expm1

Load AMP Classifier

model = joblib.load("RF.joblib")
scaler = joblib.load("norm (4).joblib")

Load ProtBert

tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False)
protbert_model = BertModel.from_pretrained("Rostlab/prot_bert")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
protbert_model = protbert_model.to(device).eval()

Full list of selected features

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"]

LIME Explainer Setup

sample_data = np.random.rand(100, len(selected_features))
explainer = LimeTabularExplainer(
training_data=sample_data,
feature_names=selected_features,
class_names=["AMP", "Non-AMP"],
mode="classification"
)

def extract_features(sequence):
sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"])
if len(sequence) < 10:
return "Error: Sequence too short."
dipeptide_features = AAComposition.CalculateAADipeptideComposition(sequence)
filtered_dipeptide_features = {k: dipeptide_features[k] for k in list(dipeptide_features.keys())[:420]}
ctd_features = CTD.CalculateCTD(sequence)
auto_features = Autocorrelation.CalculateAutoTotal(sequence)
pseudo_features = PseudoAAC.GetAPseudoAAC(sequence, lamda=9)
all_features_dict = {}
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)
feature_df_all = pd.DataFrame([all_features_dict])
normalized_array = scaler.transform(feature_df_all.values)
normalized_df = pd.DataFrame(normalized_array, columns=feature_df_all.columns)
if not set(selected_features).issubset(set(normalized_df.columns)):
return "Error: Some selected features are missing from computed features."
selected_df = normalized_df[selected_features].fillna(0)
return selected_df.values

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."}
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:
scaler = joblib.load(cfg["scaler"])
scaled = scaler.transform(embedding)
transformed = joblib.load(cfg["pca"]).transform(scaled) if cfg["pca"] else scaled
model = joblib.load(cfg["model"])
mic_log = 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
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)
result = f"Prediction: {amp_result}\nConfidence: {confidence}%\n"
if prediction == 0:
mic_values = predictmic(sequence)
result += "\nPredicted MIC Values (\u00b5M):\n"
for org, mic in mic_values.items():
result += f"- {org}: {mic}\n"
else:
result += "\nMIC prediction skipped for Non-AMP sequences.\n"
explanation = explainer.explain_instance(
data_row=features[0],
predict_fn=model.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"
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 (\u226510 characters). Get AMP classification, MIC predictions, and LIME interpretability insights."
)

iface.launch(share=True)