add plot to eval script

src/evalScript.py

@@ -1,7 +1,9 @@
+import csv
 import os
 import sys
 import pandas as pd
 import tensorflow as tf
+import matplotlib.pyplot as plt
 
 cwd = os.path.abspath(os.path.dirname(sys.argv[0]))
 modelPath = 'MyModel_tf'
@@ -24,7 +26,10 @@ new_model = tf.keras.models.load_model(modelPath)
 
 # Evaluate the restored model
 loss = new_model.evaluate(house_price_test_features, house_price_test_expected, verbose=2)
-print(loss)
+print("------\n")
+print(f"loss result: ${loss}\n")
+print("------")
+
 #print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))
 
 count = 0
@@ -36,3 +41,20 @@ except:
 
 with open('trainResults.csv', 'a+') as trainResults:
   trainResults.write(f"{count},{loss}" + "\n")
+
+try: 
+    x = []
+    y = []
+    with open('trainResults.csv', 'r') as trainResults:
+        plots = csv.reader(trainResults, delimiter = ',')
+    for row in plots:
+        x.append(row[0])
+        y.append(row[1])
+    plt.bar(x, y, color = 'g', label = "loss")
+    plt.xlabel('builds')
+    plt.ylabel('losses')
+    plt.title('loss for build')
+    plt.legend()
+    plt.show()   
+except:
+    pass