def predict(): if request.method == 'POST': Cylinders = int(request.form['Cylinders']) Displacement = float(request.form['Displacement']) Horsepower = float(request.form['Horsepower']) Weight = float(request.form['Weight']) Acceleration = float(request.form['Acceleration']) Model_Year = int(request.form['Model_Year']) Origin = int(request.form['Origin']) if Origin == 1: country = [1, 2, 3] elif Origin == 2: country = [2, 1, 3] else: country = [3, 1, 2] vehicle_config = { 'Cylinders': [Cylinders, 2, 4], 'Displacement': [Displacement, 100, 130], 'Horsepower': [Horsepower, 93, 120], 'Weight': [Weight, 2500, 3500], 'Acceleration': [Acceleration, 13, 15], 'Model Year': [Model_Year, 78, 82], 'Origin': country } # data = np.array([[preg, glucose, bp, st, insulin, bmi, dpf, age]]) my_prediction = predict_mpg(vehicle_config, classifier) return render_template('result.html', prediction=my_prediction[0])
def predict(): vehicle = request.get_json() print(vehicle) with open('./model_files/model.bin', 'rb') as f_in: model = pickle.load(f_in) f_in.close() predictions = predict_mpg(vehicle, model) result = {'mpg_prediction': list(predictions)} return jsonify(result)
def predict(): vehicle_config = request.get_json() with open(" ./model_files/model.bin", 'rb') as f_in: model = pickle.load(f_in) f_in.close() predictions = predict_mpg(vehicle_config, model) response = {'mpg_predictions': list(predictions)} return jsonify(response)
def predict(): vehicle = request.get_json() print(vehicle) with open('./model_files/model.bin', 'rb') as f_in: model = pickle.load(f_in) f_in.close() predictions = predict_mpg(vehicle, model) result = {'mpg_prediction': list(predictions)} return jsonify(result) # @app.route('/ping', methods=['GET']) # def ping(): # return "Pinging Model!!" # Commented code below while deploying. # if __name__ == '__main__': # app.run(debug=True, host='localhost', port=9696)