Update app.py

app.py

@@ -908,7 +908,10 @@ custom_css = """
 # Create the Gradio interface with proper output handling
 with gr.Blocks(title="Affective Virtual Environments - Chunked Processing", css=custom_css) 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.")
+    gr.Markdown("An Affective Virtual Environment or AVE is a digital space composed of two components: An emotion recognition system and a virtual environment generator.
+    In this project, we use bimodal speech emotion recognition to predict categorical emotions from semantic and acoustic modes. 
+    Virtual Environments are generated using deepAI and MusicGEN.
+    To interact, record your voice or upload an audio file. Define the length to Chunk your sample and if you want to generate Audio for each chunk or not. Generate your Affective Virtual Environment and wait for the results. Donwload the HTML file with your creation.")
     
     with gr.Row():
         with gr.Column(scale=2):
@@ -942,7 +945,7 @@ with gr.Blocks(title="Affective Virtual Environments - Chunked Processing", css=
                 info="Uncheck to skip music generation and speed up processing"
             )
             with gr.Row():
-                process_btn = gr.Button("Process Audio", variant="primary")
+                process_btn = gr.Button("Generate", variant="primary")
                 clear_btn = gr.Button("Clear All", variant="secondary")
     
     # Add a loading indicator
@@ -965,7 +968,7 @@ with gr.Blocks(title="Affective Virtual Environments - Chunked Processing", css=
             with gr.Row():
                 emotion_output = gr.Label(label="Acoustic Emotion Prediction")
                 transcription_output = gr.Label(label="Transcribed Text")
-                sentiment_output = gr.Label(label="Sentiment Analysis")
+                sentiment_output = gr.Label(label="Sentimental Analysis")
             with gr.Row():
                 image_output = gr.Image(label="Generated Equirectangular Image")
                 image_360_output = gr.File(label="Download 360 Image", type="filepath")