Update app.py

app.py

@@ -348,14 +348,40 @@ def clear_all():
     
     return outputs
 
+# Function to load example audio
+def load_example_audio(example_name):
+    # This function would load the example audio based on the selected example
+    # For now, we'll return a placeholder path - you should replace these with actual paths to your example audio files
+    example_paths = {
+        "Happy Speech": "examples/happy_speech.wav",
+        "Sad Story": "examples/sad_story.wav",
+        "Neutral News": "examples/neutral_news.wav"
+    }
+    
+    # Return the path to the selected example
+    return example_paths.get(example_name, "examples/happy_speech.wav")
+
 # Create the Gradio interface with proper output handling
 with gr.Blocks(title="Affective Virtual Environments - Chunked Processing") as interface:
     gr.Markdown("# Affective Virtual Environments")
     gr.Markdown("Create an AVE using your voice. Audio is split into chunks, with separate predictions and generations for each segment.")
     
     with gr.Row():
-        audio_input = gr.Audio(label="Input Audio", type="filepath", sources=["microphone", "upload"])
-        with gr.Column():
+        with gr.Column(scale=2):
+            audio_input = gr.Audio(label="Input Audio", type="filepath", sources=["microphone", "upload"])
+            
+            # Add example audio selection
+            example_selector = gr.Dropdown(
+                label="Select Example Audio",
+                choices=["Happy Speech", "Sad Story", "Neutral News"],
+                value=None,
+                info="Choose from pre-recorded example speeches"
+            )
+            
+            # Add button to load selected example
+            load_example_btn = gr.Button("Load Example", variant="secondary")
+        
+        with gr.Column(scale=1):
             # Add chunk duration input
             chunk_duration_input = gr.Number(
                 label="Chunk Duration (seconds)",
@@ -454,6 +480,12 @@ with gr.Blocks(title="Affective Virtual Environments - Chunked Processing") as i
         # Hide loading indicator and show results
         yield [gr.HTML("")] + group_visibility + outputs
     
+    # Function to handle example selection
+    def load_example(example_name):
+        if not example_name:
+            return None
+        return load_example_audio(example_name)
+    
     # Set up the button click
     process_btn.click(
         fn=process_and_display,
@@ -479,5 +511,12 @@ with gr.Blocks(title="Affective Virtual Environments - Chunked Processing") as i
             container['music']
         ]] + [loading_indicator] + [chunk_duration_input]
     )
+    
+    # Set up the example loading button
+    load_example_btn.click(
+        fn=load_example,
+        inputs=[example_selector],
+        outputs=[audio_input]
+    )
 
 interface.launch()