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import gradio as gr
import joblib
import numpy as np
from propy import AAComposition
from sklearn.preprocessing import MinMaxScaler
# Load trained SVM model and scaler (Ensure both files exist in the Space)
model = joblib.load("SVM.joblib")
scaler = MinMaxScaler()
# List of features used in your model
selected_features = [
"A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V",
"AA", "AR", "AN", "AD", "AC", "AE", "AQ", "AG", "AI", "AL", "AK", "AF", "AP", "AS", "AT", "AY", "AV",
"RA", "RR", "RN", "RD", "RC", "RE", "RQ", "RG", "RH", "RI", "RL", "RK", "RM", "RF", "RS", "RT", "RY", "RV",
"NA", "NR", "ND", "NC", "NE", "NG", "NI", "NL", "NK", "NP",
"DA", "DR", "DN", "DD", "DC", "DE", "DQ", "DG", "DI", "DL", "DK", "DP", "DS", "DT", "DV",
"CA", "CR", "CN", "CD", "CC", "CE", "CG", "CH", "CI", "CL", "CK", "CF", "CP", "CS", "CT", "CY", "CV",
"EA", "ER", "EN", "ED", "EC", "EE", "EQ", "EG", "EI", "EL", "EK", "EP", "ES", "ET", "EV",
"QA", "QR", "QC", "QG", "QL", "QK", "QP", "QT", "QV",
"GA", "GR", "GD", "GC", "GE", "GQ", "GG", "GI", "GL", "GK", "GF", "GP", "GS", "GW", "GY", "GV",
"HC", "HG", "HL", "HK", "HP",
"IA", "IR", "ID", "IC", "IE", "II", "IL", "IK", "IF", "IP", "IS", "IT", "IV",
"LA", "LR", "LN", "LD", "LC", "LE", "LQ", "LG", "LI", "LL", "LK", "LM", "LF", "LP", "LS", "LT", "LV",
"KA", "KR", "KN", "KD", "KC", "KE", "KQ", "KG", "KH", "KI", "KL", "KK", "KM", "KF", "KP", "KS", "KT", "KV",
"MA", "ME", "MI", "ML", "MK", "MF", "MP", "MS", "MT", "MV",
"FR", "FC", "FQ", "FG", "FI", "FL", "FF", "FS", "FT", "FY", "FV",
"PA", "PR", "PD", "PC", "PE", "PG", "PL", "PK", "PS", "PV",
"SA", "SR", "SD", "SC", "SE", "SG", "SH", "SI", "SL", "SK", "SF", "SP", "SS", "ST", "SY", "SV",
"TA", "TR", "TN", "TC", "TE", "TG", "TI", "TL", "TK", "TF", "TP", "TS", "TT", "TV",
"WC",
"YR", "YD", "YC", "YG", "YL", "YS", "YV",
"VA", "VR", "VD", "VC", "VE", "VQ", "VG", "VI", "VL", "VK", "VP", "VS", "VT", "VY", "VV"
]
def extract_features(sequence):
"""Extract only the required features and normalize them."""
# Compute all possible features
aac = AAComposition.CalculateAADipeptideComposition(sequence) # Amino Acid Composition
# Combine both feature sets
all_features = aac
# Extract only the selected features
selected_feature_values = [all_features[feature] for feature in selected_features if feature in all_features]
# Convert to NumPy array for normalization
feature_array = np.array(selected_feature_values)
# Min-Max Normalization
scaler = MinMaxScaler()
normalized_features = scaler.fit_transform(feature_array).flatten()
return normalized_features
def predict(sequence):
"""Predict AMP vs Non-AMP"""
features = extract_features(sequence)
prediction = model.predict(features)[0]
return "AMP" if prediction == 0 else "Non-AMP"
# Create 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 to predict whether it's an antimicrobial peptide (AMP) or not."
)
# Launch app
iface.launch(share=True) |