59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
from flask import Flask, request, jsonify
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import joblib
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import json
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import numpy as np
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app = Flask(__name__)
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model = joblib.load("../models/random_forest_stunting.pkl")
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with open("../models/label_mapping.json", "r") as f:
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label_mapping = json.load(f)
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@app.route('/', methods=['GET'])
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def home():
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return jsonify({
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"message" : "API Deteksi Stunting",
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"version" : "1.0",
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"endpoint": "/predict"
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})
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@app.route('/predict', methods=['POST'])
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def predict():
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try:
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data = request.get_json()
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# Ambil input
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jk = 0 if str(data['jenis_kelamin']).upper() == 'L' else 1
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usia = float(data['usia_bulan'])
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berat = float(data['berat_badan'])
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tinggi = float(data['tinggi_badan'])
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# Klasifikasi
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features = np.array([[jk, usia, berat, tinggi]])
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pred = model.predict(features)[0]
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prob = model.predict_proba(features)[0]
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label = label_mapping[str(pred)]
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return jsonify({
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"status" : "success",
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"input" : {
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"jenis_kelamin": data['jenis_kelamin'],
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"usia_bulan" : usia,
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"berat_badan" : berat,
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"tinggi_badan" : tinggi
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},
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"prediksi" : label,
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"probabilitas" : {
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label_mapping[str(i)]: round(float(p), 4)
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for i, p in enumerate(prob)
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}
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})
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except Exception as e:
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return jsonify({
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"status" : "error",
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"message": str(e)
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}), 400
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if __name__ == '__main__':
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app.run(debug=True, port=5000)
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