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File size: 656 Bytes
48b2dea 24882c7 48b2dea 24882c7 48b2dea 52478dc 48b2dea 24882c7 48b2dea 24882c7 48b2dea 52478dc 24882c7 48b2dea 24882c7 48b2dea 24882c7 48b2dea 24882c7 48b2dea 24882c7 48b2dea 24882c7 48b2dea 24882c7 48b2dea 24882c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | from flask import Flask, request, jsonify
import joblib
import pandas as pd
app = Flask(__name__)
model = joblib.load("tuned_xgboost_model.pkl")
@app.route('/')
def home():
return "SuperKart Sales Forecast Modal Deployment API"
@app.route('/predict', methods=['POST'])
def predict():
try:
data = request.get_json()
input_df = pd.DataFrame([data])
prediction = model.predict(input_df)[0]
return jsonify({'Predicted_Sales': round(float(prediction), 2)})
except Exception as e:
return jsonify({'error': str(e)})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860) |