76 lines
1.6 KiB
Python
76 lines
1.6 KiB
Python
from flask import Flask, jsonify
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import pickle
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import pandas as pd
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app = Flask(__name__)
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# =============================
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# LOAD MODEL
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# =============================
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with open("model_pegas.pkl", "rb") as f:
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model_pegas = pickle.load(f)
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print("Model PeGas berhasil dimuat")
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# =============================
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# ROUTE TEST
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@app.route("/")
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def home():
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return jsonify({
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"status": "API PeGas aktif"
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})
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# PREDIKSI 30 HARI
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@app.route("/predict_30days")
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def predict_30days():
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future = model_pegas.make_future_dataframe(periods=30)
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future['is_pengetatan'] = (future['ds'] >= '2025-10-01').astype(int)
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forecast = model_pegas.predict(future)
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result = forecast[['ds','yhat','yhat_lower','yhat_upper']].tail(30)
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return jsonify(result.to_dict(orient="records"))
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# PREDIKSI 3 BULAN
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@app.route("/predict_3months")
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def predict_3months():
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future = model_pegas.make_future_dataframe(periods=90)
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future['is_pengetatan'] = (future['ds'] >= '2025-10-01').astype(int)
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forecast = model_pegas.predict(future)
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result = forecast[['ds','yhat','yhat_lower','yhat_upper']].tail(90)
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return jsonify(result.to_dict(orient="records"))
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# PREDIKSI 6 BULAN
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@app.route("/predict_6months")
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def predict_6months():
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future = model_pegas.make_future_dataframe(periods=180)
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future['is_pengetatan'] = (future['ds'] >= '2025-10-01').astype(int)
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forecast = model_pegas.predict(future)
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result = forecast[['ds','yhat','yhat_lower','yhat_upper']].tail(180)
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return jsonify(result.to_dict(orient="records"))
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# RUN FLASK
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if __name__ == "__main__":
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app.run(debug=True, port=5000) |