app.py

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()

def extract_features(sequence):
    """Calculate AAC, Dipeptide Composition, and normalize features."""
    # Calculate Amino Acid Composition (AAC) and convert to array
    aac = np.array(list(propy.AAComposition.CalculateAAC(sequence).values()))

    # Calculate Dipeptide Composition and convert to array
    dipeptide_comp = np.array(list(propy.AAComposition.CalculateAADipeptideComposition(sequence).values()))

    # Combine both features (AAC and Dipeptide Composition)
    features = np.concatenate((aac, dipeptide_comp))

    # Normalize using the pre-trained scaler (Ensure the scaler is loaded correctly)
    normalized_features = scaler.transform([features])  # Don't use fit_transform(), only transform()

    return normalized_features


def predict(sequence):
    """Predict AMP vs Non-AMP"""
    features = extract_features(sequence)
    prediction = model.predict(features)[0]
    return "AMP" if prediction == 1 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)