3

app3.py

@@ -4,7 +4,7 @@ import os
 import warnings
 warnings.filterwarnings("ignore")
 
-# Function to get the list of audio files in the 'rec/' directory1
+# Function to get the list of audio files in the 'rec/' directory
 def get_audio_files_list(directory="rec"):
     try:
         return [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
@@ -32,34 +32,18 @@ emotion_dict = {
 def predict_emotion(selected_audio):
     file_path = os.path.join("rec", selected_audio)
     out_prob, score, index, text_lab = learner.classify_file(file_path)
-    return emotion_dict[text_lab[0]]
+    emotion = emotion_dict[text_lab[0]]
+    return emotion, file_path  # Return both emotion and file path
 
 # Get the list of audio files for the dropdown
 audio_files_list = get_audio_files_list()
 
-# Function to return the selected audio file path
-def get_audio_file_path(selected_audio):
-    file_path = os.path.join("rec", selected_audio)
-    return file_path
-
-# Gradio components
-dropdown = gr.Dropdown(label="Select Audio", choices=audio_files_list)
-audio_player = gr.Audio(source="file", label="Listen to the selected audio")
-
-# Update the audio player when a new selection is made from the dropdown
-def update_audio(selected_audio):
-    return get_audio_file_path(selected_audio)
+# Loading Gradio interface
+inputs = gr.Dropdown(label="Select Audio", choices=audio_files_list)
+outputs = [gr.outputs.Textbox(label="Predicted Emotion"), gr.outputs.Audio(label="Play Audio")]
 
-# Connect the dropdown to the audio player using the update_audio function
-dropdown.change(fn=update_audio, inputs=dropdown, outputs=audio_player)
-
-# Update the Gradio interface to use both the dropdown and the audio player as inputs
-interface = gr.Interface(
-    fn=predict_emotion,
-    inputs=[dropdown, audio_player],
-    outputs="text",
-    title="ML Speech Emotion Detection",
-    description="Speechbrain powered wav2vec 2.0 pretrained model on IEMOCAP dataset using Gradio."
-)
+title = "ML Speech Emotion Detection"
+description = "Speechbrain powered wav2vec 2.0 pretrained model on IEMOCAP dataset using Gradio."
 
+interface = gr.Interface(fn=predict_emotion, inputs=inputs, outputs=outputs, title=title, description=description)
 interface.launch()