Update app.py

app.py

@@ -97,30 +97,17 @@ def make_prediction(image, taxonomic_level):
     
     return output_text
 
-# Define a function to update the welcome message based on the logged-in user
-def update_message(request: gr.Request):
-    return f"Welcome to the demo, Dr. {request.username}!"
-
-# Define the Gradio interface
 with gr.Blocks() as demo:
-    # Add a Markdown component for displaying the welcome message
-    welcome_message = gr.Markdown()
+    # Define the input and output components for predictions
+    image_input = gr.Image(type="pil", label="Upload Image")  # Input type: Image (PIL format)
+    taxonomic_level_input = gr.Dropdown(choices=taxonomic_levels, label="Taxonomic level", value="species")  # Dropdown for taxonomic level
+    output_html = gr.HTML(label="Prediction Result")  # Output type: HTML for formatting
     
-    # Load the update_message function to display the welcome message
-    demo.load(update_message, None, welcome_message)
-
-    # Define the main interface for predictions
-    interface = gr.Interface(
-        fn=make_prediction,            # Function to be called for predictions
-        inputs=[gr.Image(type="pil"),   # Input type: Image (PIL format)
-                gr.Dropdown(choices=taxonomic_levels, label="Taxonomic level", value="species")],  # Use 'value' instead of 'default'
-        outputs="html",                # Output type: HTML for formatting
-        title="Amazon arboreal species classification",
-        description="Upload an image and select the taxonomic level to classify the species."
-    )
+    # Create the prediction button
+    predict_button = gr.Button("Make Prediction")
 
-    # Add the prediction interface to the main demo
-    interface.render()
+    # Define what happens when the button is clicked
+    predict_button.click(make_prediction, inputs=[image_input, taxonomic_level_input], outputs=output_html)
 
 # Launch the Gradio interface with authentication for the specified users
 demo.launch(auth=[