File size: 619 Bytes
cea83d5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 |
from flask import Flask, request, jsonify
import pickle
import numpy as np
app = Flask(__name__)
# Load model
with open("xgb_superkart_model.pkl", "rb") as f:
model = pickle.load(f)
@app.route("/")
def home():
return "SuperKart Sales Forecast API is Live"
@app.route("/predict", methods=["POST"])
def predict():
data = request.get_json(force=True)
input_features = np.array(data["features"]).reshape(1, -1)
prediction = model.predict(input_features)
return jsonify({"predicted_sales": prediction[0]})
if __name__ == "__main__":
app.run(host='0.0.0.0', port=7860)
|