Update app.py

app.py

@@ -1,11 +1,10 @@
 import gradio as gr
 import tensorflow as tf
 import librosa
+import librosa.display
 import numpy as np
 import matplotlib.pyplot as plt
 import time
-import io
-from PIL import Image
 
 # Load the pre-trained model (optional for this logic, depending on use case)
 model = tf.keras.models.load_model("model.h5")
@@ -100,35 +99,23 @@ def process_audio(audio_file, inhale_duration, exhale_duration):
         countdown_message = f"Starting recording with total duration of {total_duration} seconds.\n"
         countdown_message += f"Starting Inhale for {inhale_duration} seconds...\n"
         
-        # Balloon size animation (use Gradio's JS for animation)
-        balloon_animation = []
-
         for t in range(inhale_duration, 0, -1):
             time.sleep(1)
             countdown_message += f"Inhale: {t}s remaining...\n"
-            balloon_animation.append(f"scaleBalloon({t}, 'inhale')")  # Animation call
-
+        
         countdown_message += f"Switching to Exhale for {exhale_duration} seconds...\n"
-
+        
         for t in range(exhale_duration, 0, -1):
             time.sleep(1)
             countdown_message += f"Exhale: {t}s remaining...\n"
-            balloon_animation.append(f"scaleBalloon({t}, 'exhale')")  # Animation call
-
+        
         countdown_message += "Recording Finished!"
 
-        # Balloon image to return as the animated image
-        balloon_image = create_balloon_image()
-
-        return result_table, "waveform_highlighted.png", countdown_message, balloon_image, balloon_animation
+        return result_table, "waveform_highlighted.png", countdown_message
+    
+    except Exception as e:
+        return f"Error: {str(e)}", None, None
 
-# Create a balloon image to display
-def create_balloon_image():
-    # Create a balloon using PIL or load a pre-existing image and resize it
-    balloon = Image.new('RGBA', (200, 200), (255, 255, 255, 0))
-    # Drawing a simple circle for the balloon
-    balloon = balloon.resize((200, 200))  # Initial size
-    return balloon
 
 # Define Gradio interface
 with gr.Blocks() as demo:
@@ -144,14 +131,13 @@ with gr.Blocks() as demo:
         result_output = gr.Textbox(label="Prediction Results (Table)")
         waveform_output = gr.Image(label="Waveform with Highlighted Segments")
         countdown_output = gr.Textbox(label="Countdown Timer")
-        balloon_output = gr.Image(label="Balloon Animation")
 
     submit_button = gr.Button("Start Record")
     
     submit_button.click(
         fn=process_audio,
         inputs=[audio_input, inhale_duration, exhale_duration],
-        outputs=[result_output, waveform_output, countdown_output, balloon_output]
+        outputs=[result_output, waveform_output, countdown_output]
     )
 
 # Run the Gradio app