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import gradio as gr
import joblib
import numpy as np
import pandas as pd
from propy import AAComposition
from sklearn.preprocessing import MinMaxScaler
from fastapi import FastAPI
from gradio.routes import mount_gradio_app
# Load trained SVM model and scaler
model = joblib.load("SVM.joblib")
scaler = joblib.load("norm.joblib")
# FastAPI instance
app = FastAPI()
# Selected features used in the model
selected_features = [
"A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V"
] # (Shortened for brevity)
def extract_features(sequence):
"""Extract only the required features and normalize them."""
all_features = AAComposition.CalculateAADipeptideComposition(sequence)
feature_values = list(all_features.values())
feature_array = np.array(feature_values).reshape(-1, 1)[:420] # Ensure correct shape
normalized_features = scaler.transform(feature_array.T).flatten()
selected_feature_dict = {
feature: normalized_features[i]
for i, feature in enumerate(selected_features)
if feature in all_features
}
selected_feature_df = pd.DataFrame([selected_feature_dict])
return selected_feature_df.to_numpy()
def predict(sequence):
"""Predict AMP vs Non-AMP"""
features = extract_features(sequence)
prediction = model.predict(features.T)[0]
return {"prediction": "AMP" if prediction == 0 else "Non-AMP"}
# FastAPI endpoint
@app.post("/predict/")
async def predict_api(request: dict):
sequence = request.get("sequence", "")
if not sequence or len(sequence) < 10 or len(sequence) > 100:
return {"error": "Sequence length must be between 10 and 100."}
return predict(sequence)
# Gradio Interface (optional if you want UI access)
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 AMP or Non-AMP."
)
app = mount_gradio_app(app, iface, path="/gradio")
# Run the server with: `uvicorn filename:app --host 0.0.0.0 --port 7860`
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