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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib
import numpy as np
from propy import AAComposition
from sklearn.preprocessing import MinMaxScaler
# Initialize FastAPI app
app = FastAPI()
# Load trained SVM model and scaler
model = joblib.load("SVM.joblib")
scaler = MinMaxScaler()
# Define request schema
class SequenceInput(BaseModel):
sequence: str
def extract_features(sequence: str):
"""Extract Amino Acid Composition (AAC) features and normalize them."""
try:
aac = np.array(list(AAComposition.CalculateAADipeptideComposition(sequence)), dtype=float)
normalized_features = scaler.fit_transform([aac]) # Don't use fit_transform(), only transform()
return normalized_features
except Exception as e:
raise HTTPException(status_code=400, detail=f"Feature extraction failed: {str(e)}")
@app.post("/predict/")
def predict(input_data: SequenceInput):
"""Predict AMP vs Non-AMP from protein sequence."""
features = extract_features(input_data.sequence)
prediction = model.predict(features)[0]
result = "AMP" if prediction == 1 else "Non-AMP"
return {"prediction": result}
# Run using: uvicorn script_name:app --host 0.0.0.0 --port 8000 --reload
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