Update app.py

app.py

@@ -4,6 +4,7 @@ import torchaudio
 from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
 from queue import Queue
 import threading
+import numpy as np
 
 # Check for device
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -39,7 +40,6 @@ def audio_processing_thread():
             audio_chunk, sampling_rate = audio_queue.get()
             emotion = inference_chunk(audio_chunk, sampling_rate)
             results_queue.put(emotion)
-            print(f"Processed Emotion: {emotion}")  # Debugging: Show the processed emotion
 
 processing_thread = threading.Thread(target=audio_processing_thread, daemon=True)
 processing_thread.start()
@@ -60,25 +60,24 @@ def real_time_inference_live(microphone_audio):
         if audio_chunk.size(0) > 0:
             audio_queue.put((audio_chunk, sampling_rate))
 
-        # Display results as they are processed
+        # Retrieve results from the results queue
         while not results_queue.empty():
             emotion = results_queue.get()
             emotions.append(emotion)
-            print(f"Emotion Detected: {emotion}")  # Debugging: Show detected emotion in live output
 
     return "\n".join(emotions)
 
 with gr.Blocks() as demo:
     gr.Markdown("# Live Emotion Detection from Audio")
 
-    audio_input = gr.Audio(streaming=True, label="Real-Time Audio Input", type="numpy", elem_id="audio-input")
-    emotion_output = gr.Textbox(label="Live Detected Emotions", lines=10)
+    audio_input = gr.Audio(streaming=True, label="Real-Time Audio Input", type="numpy")
+    emotion_output = gr.Textbox(label="Detected Emotions", lines=10)
 
     def stream_audio_live(audio):
         return real_time_inference_live(audio)
 
     audio_input.stream(stream_audio_live, outputs=emotion_output)
 
-    gr.Markdown("This application uses a live model to process audio and infer emotions in real-time.")
+    gr.Markdown("This application processes audio in 5-second chunks and detects emotions in real-time.")
 
-demo.launch(share=True)
+demo.launch(share=True)