Update app.py

app.py

@@ -1,54 +1,70 @@
 import gradio as gr
 import tensorflow as tf
 
-print("TensorFlow version:", tf.__version__)
-
-mnist = tf.keras.datasets.mnist
-
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-x_train, x_test = x_train / 255.0, x_test / 255.0
-
-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)
-])
-
-predictions = model(x_train[:1]).numpy()
-print(predictions)
-
-tf.nn.softmax(predictions).numpy()
-
-loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-loss_fn(y_train[:1], predictions).numpy()
-
-model.compile(optimizer='adam',
-              loss=loss_fn,
-              metrics=['accuracy'])
-
-
-model.fit(x_train, y_train, epochs=5)
-model.evaluate(x_test,  y_test, verbose=2)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-def greet(name):
-    return f"Hello, {name}!"
-
-iface = gr.Interface(greet, "text", "text")
-iface.launch()
-
+# Create a Gradio App using Blocks    
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """
+    # Welcome to the Virtual Therapist Chat Bot!
+    """
+    )
+    with gr.Accordion("Click for Instructions:"):
+            gr.Markdown(
+    """
+    * Tell the therapist your problems, by recording your query.
+    * Submit your query, and follow the chat or listen to the Therapists advice.
+    * When you are ready to respond, clear your last recording and resubmit.
+    note: Transcribe Audio does not work on iOS
+    """)
+    def modelTraining():
+        
+
+        print("TensorFlow version:", tf.__version__)
+
+        mnist = tf.keras.datasets.mnist
+
+        (x_train, y_train), (x_test, y_test) = mnist.load_data()
+        x_train, x_test = x_train / 255.0, x_test / 255.0
+
+        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)
+        ])
+
+        predictions = model(x_train[:1]).numpy()
+        print(predictions)
+        
+        tf.nn.softmax(predictions).numpy()
+        
+        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+        loss_fn(y_train[:1], predictions).numpy()
+        
+        model.compile(optimizer='adam',
+                      loss=loss_fn,
+                      metrics=['accuracy'])
+        
+        
+        model.fit(x_train, y_train, epochs=5)
+        model.evaluate(x_test,  y_test, verbose=2)
+
+        return "done"
+        
+
+    # Creates the Gradio interface objects
+    # The submit button triggers a cascade of
+    # events that each engage a different 
+    # component as input/output
+    with gr.Row():
+        with gr.Column(scale=1):
+            submit_btn = gr.Button(value="Train/Eval")
+        with gr.Column(scale=2):
+            model_data = gr.Text(label="Model Results", interactive=False)
+    submit_btn.click(modelTraining, [], user_transcript)
+    
+    
+# creates a local web server
+# if share=True creates a public
+# demo on huggingface.co
+demo.launch(share=False)