Building the database using sql file

main.py

@@ -1,5 +1,6 @@
 import numpy as np # helps with the math
 import matplotlib.pyplot as plt # to plot error during training
+from data.db_connect import Database
 
 # input data
 inputs = np.array([[0, 0, 1, 0],
@@ -55,22 +56,30 @@ class NeuralNetwork:
         prediction = self.sigmoid(np.dot(new_input, self.weights))
         return prediction
 
-# create neural network   
-NN = NeuralNetwork(inputs, outputs)
-# train neural network
-NN.train()
+if __name__ == '__main__':
+    sql_file = "./data/build_db.sql"
+    db_file = "./database/baseball.db"
 
-# create two new examples to predict                                   
-example = np.array([[1, 1, 1, 0]])
-example_2 = np.array([[0, 0, 1, 1]])
+    db_conn = Database(db_file)
+    db_conn.build_database(sql_file)
 
-# print the predictions for both examples                                   
-print(NN.predict(example), ' - Correct: ', example[0][0])
-print(NN.predict(example_2), ' - Correct: ', example_2[0][0])
+else:
+    # create neural network   
+    NN = NeuralNetwork(inputs, outputs)
+    # train neural network
+    NN.train()
 
-# plot the error over the entire training duration
-plt.figure(figsize=(15,5))
-plt.plot(NN.epoch_list, NN.error_history)
-plt.xlabel('Epoch')
-plt.ylabel('Error')
-plt.savefig('plot.png')
+    # create two new examples to predict                                   
+    example = np.array([[1, 1, 1, 0]])
+    example_2 = np.array([[0, 0, 1, 1]])
+
+    # print the predictions for both examples                                   
+    print(NN.predict(example), ' - Correct: ', example[0][0])
+    print(NN.predict(example_2), ' - Correct: ', example_2[0][0])
+
+    # plot the error over the entire training duration
+    plt.figure(figsize=(15,5))
+    plt.plot(NN.epoch_list, NN.error_history)
+    plt.xlabel('Epoch')
+    plt.ylabel('Error')
+    plt.savefig('plot.png')