Update app.py

app.py

@@ -1,5 +1,9 @@
 import gradio as gr
 import tensorflow as tf
+import numpy as np
+import os
+import PIL
+import PIL.Image
 
 # Create a Gradio App using Blocks    
 with gr.Blocks() as demo:
@@ -11,15 +15,21 @@ with gr.Blocks() as demo:
     with gr.Accordion("Click for Instructions:"):
             gr.Markdown(
     """
-    * Train/Eval will setup, train, and evaluate the base model
+    * uploading an image will setup, train, and evaluate the base model
     """)
 
+    # Train, evaluate and test a ML
+    # image classification model for
+    # clothes images
     def modelTraining(img):
+        # clothing dataset
         mnist = tf.keras.datasets.mnist
 
+        #split the training data in to a train/test sets
         (x_train, y_train), (x_test, y_test) = mnist.load_data()
         x_train, x_test = x_train / 255.0, x_test / 255.0
 
+        # create the neural net layers
         model = tf.keras.models.Sequential([
           tf.keras.layers.Flatten(input_shape=(28, 28)),
           tf.keras.layers.Dense(128, activation='relu'),
@@ -27,25 +37,34 @@ with gr.Blocks() as demo:
           tf.keras.layers.Dense(10)
         ])
 
+        #make a post-training predition on the 
+        #training set data
         predictions = model(x_train[:1]).numpy()
-        
+
+        # converts the logits into a probability
         tf.nn.softmax(predictions).numpy()
-        
+
+        #create and train the loss function
         loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
         loss_fn(y_train[:1], predictions).numpy()
-        
+
+        # compile the model with the loss function
         model.compile(optimizer='adam',
                       loss=loss_fn,
                       metrics=['accuracy'])
         
-        
+        # train the model - 5 runs
+        # evaluate the model on the test set
         model.fit(x_train, y_train, epochs=5)
         test_loss, test_acc = model.evaluate(x_test,  y_test, verbose=2)
 
         print("Test accuracy: ", test_acc)
 
         # Define any necessary preprocessing steps for the image input here
+        # the image can be passed as a PIL or numpy
+        # create the final model for production
         probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
+        
         # Make a prediction using the model
         prediction = probability_model.predict(img)