Update app.py

app.py

@@ -21,60 +21,67 @@ with gr.Blocks() as demo:
     # Train, evaluate and test a ML
     # image classification model for
     # clothes images
-    def modelTraining(img):
-        class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
-               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+
+    class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
+           'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+
+    # Normalize the pixel values
+    img = np.array(img) / 255.0
     
-        # Normalize the pixel values
-        img = np.array(img) / 255.0
-        
-        # 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'),
-          tf.keras.layers.Dropout(0.2),
-          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)
-        post_train_results = f"Test accuracy: {test_acc} Test Loss: {test_loss}"
-        print(post_train_results)
+    # 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 final model for production
-        probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
+    # 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'),
+      tf.keras.layers.Dropout(0.2),
+      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)
+    post_train_results = f"Test accuracy: {test_acc} Test Loss: {test_loss}"
+    print(post_train_results)
+
+    # create the final model for production
+    probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
+
+        
+    def classifyImage(img):
+        #global probability_model
+        #global class_names
+        
         input_array = np.expand_dims(img, axis=0) # add an extra dimension to represent the batch size
 
         # Make a prediction using the model
         prediction = probability_model.predict(input_array)
-        predicted_label = class_names[np.argmax(prediction)]
+
         # Postprocess the prediction and return it
+        predicted_label = class_names[np.argmax(prediction)]
+
         return predicted_label
         
 
@@ -83,8 +90,10 @@ with gr.Blocks() as demo:
         with gr.Column(scale=2):
             image_data = gr.Image(label="Upload Image", type="numpy", image_mode="L", shape=[28,28], invert_colors=True)
         with gr.Column(scale=1):
+            train_test_btn = gr.Button(value="Train/Test")
+            model_performance = gr.Text(label="Model Performance Results", interactive=False)
             model_prediction = gr.Text(label="Model Prediction", interactive=False)
-    image_data.change(modelTraining, image_data, model_prediction)
+    image_data.change(classifyImage, image_data, model_prediction)
     
     
 # creates a local web server