Update handler.py

handler.py

@@ -4,7 +4,7 @@ import torch
 import soundfile as sf
 from transformers import AutoTokenizer, AutoModelForTextToWaveform
 import cloudinary.uploader
-import streamlit as st
+
 # Configure logging
 logging.basicConfig(level=logging.DEBUG)
 # Configure logging
@@ -18,6 +18,7 @@ class EndpointHandler():
         
         self.tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
         self.model= AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-eng")
+        
     def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
         # Prepare the payload with input data
         logging.warning(f"------input_data-- {str(data)}")
@@ -33,7 +34,7 @@ class EndpointHandler():
         # Save the audio to a file
         sf.write("StoryAudio.wav", outputs["waveform"][0].numpy(), self.model.config.sampling_rate)
         uploadGraphFile("StoryAudio.wav")
-        st.audio("StoryAudio.wav")
+        main()
         #return 'StoryAudio.wav'
         # Check if the request was successful
         
@@ -47,3 +48,24 @@ def uploadGraphFile(fileName):
     # Upload a file to Cloudinary
     result = cloudinary.uploader.upload(fileName, folder="poc-graph", resource_type="raw")
     return result
+import pygame
+import time
+
+def play_audio(file_path):
+    pygame.init()
+    pygame.mixer.music.load(file_path)
+    pygame.mixer.music.play()
+
+def stop_audio():
+    pygame.mixer.music.stop()
+
+def main():
+    audio_file = "StoryAudio.wav"
+    play_audio(audio_file)
+    duration = pygame.mixer.Sound(audio_file).get_length()
+    start_time = time.time()
+    while pygame.mixer.music.get_busy():
+        current_time = time.time() - start_time
+        progress = current_time / duration * 100
+        print(f"Playback progress: {progress:.2f}%")
+        time.sleep(0.1)