update app.py

app.py

@@ -16,17 +16,17 @@ from keras.layers import Dense, Conv2D
 from keras.datasets import mnist
 
 # Gradio to build web app for presenting the ML model
-!pip install gradio
 import gradio as gr
 
 """# Loading pre-processed MNIST dataset"""
 
 # mnist.load_data() returns list of tuples
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
-print(f"X_train Shape: {x_train.shape} | Type - {type(x_train)}")
-print(f"y_train Shape: {y_train.shape} | Type - {type(y_train)}")
-print(f"X_test Shape: {x_test.shape} | Type - {type(x_test)}")
-print(f"y_test Shape: {y_test.shape} | Type - {type(y_test)}")
+
+# print(f"X_train Shape: {x_train.shape} | Type - {type(x_train)}")
+# print(f"y_train Shape: {y_train.shape} | Type - {type(y_train)}")
+# print(f"X_test Shape: {x_test.shape} | Type - {type(x_test)}")
+# print(f"y_test Shape: {y_test.shape} | Type - {type(y_test)}")
 
 """-> Make modifications to the train and test datasets to suit the **ML model**
 
@@ -61,9 +61,9 @@ model.fit(x_train, y_train, epochs=20, validation_split=0.3)
 """# Evaluation of the Model"""
 
 results = model.evaluate(x_test, y_test)
-print(f"Accuracy: {100*(results[1])}")
+# print(f"Accuracy: {100*(results[1])}")
 
-print(model.predict(x_test[0:1])) # returns list of lists
+# print(model.predict(x_test[0:1])) # returns list of lists
 
 """# Visualization using Gradio"""