Update app.py

app.py

@@ -1,20 +1,20 @@
 from speechbrain.pretrained.interfaces import foreign_class
 import gradio as gr
 import os
-import warnings
 
+import warnings
 warnings.filterwarnings("ignore")
 
 # Loading the speechbrain emotion detection model
 learner = foreign_class(
     source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
-    pymodule_file="custom_interface.py",
+    pymodule_file="custom_interface.py", 
     classname="CustomEncoderWav2vec2Classifier"
 )
 
-# Emotion dictionary
+# Building prediction function for gradio
 emotion_dict = {
-    'sad': 'Sad',
+    'sad': 'Sad', 
     'hap': 'Happy',
     'ang': 'Anger',
     'fea': 'Fear',
@@ -22,48 +22,46 @@ emotion_dict = {
     'neu': 'Neutral'
 }
 
-# Function for classification of uploaded files
-def predict_emotion_upload(audio):
+def predict_emotion(audio):
     out_prob, score, index, text_lab = learner.classify_file(audio.name)
     return emotion_dict[text_lab[0]]
 
-# Function for classification of selected files from the dropdown
-def predict_emotion_select(filename):
-    file_path = os.path.join('rec', filename)
-    out_prob, score, index, text_lab = learner.classify_file(file_path)
-    return emotion_dict[text_lab[0]]
-
-# Function to create an audio player component
-def create_audio_player(filename):
-    file_path = os.path.join('rec', filename)
-    return file_path
+def predict_emotion_from_file(file_path):
+    audio = gr.Audio(file_path=file_path, source="upload", type="filepath")
+    return predict_emotion(audio)
 
-# Retrieve a list of audio file names from the 'rec' directory
-audio_files = os.listdir('rec')
-audio_files_dropdown = gr.inputs.Dropdown(choices=audio_files, label="Select Audio File")
+# Define the Gradio interface with the first tab for uploading an audio file
+inputs = gr.inputs.Audio(label="Input Audio", type="file")
+outputs = "text"
+title = "ML Speech Emotion Detection"
+description = "Speechbrain powered wav2vec 2.0 pretrained model on IEMOCAP dataset using Gradio."
 
-# Define Gradio interface components for both tabs
-with gr.Blocks() as demo:
-    gr.Markdown("## ML Speech Emotion Detection")
-    gr.Markdown("Speechbrain powered wav2vec 2.0 pretrained model on IEMOCAP dataset.")
+# Define the second tab for selecting an audio file from the dropdown
+rec_folder = "rec"
+audio_files = [f for f in os.listdir(rec_folder) if f.endswith(('.wav', '.mp3'))]
+audio_files = [os.path.join(rec_folder, f) for f in audio_files]
+file_dropdown = gr.inputs.Dropdown(label="Select Preloaded Audio File", choices=audio_files)
 
-    with gr.Tabs():
-        with gr.TabItem("Upload Audio"):
-            with gr.Group():
-                audio_upload = gr.Audio(label="Upload Audio", type="file")
-                submit_btn_1 = gr.Button("Classify Uploaded Audio")
-            audio_player_1 = gr.Audio(label="Uploaded Audio Player", interactive=True)
-            output_text_1 = gr.Textbox(label="Prediction")
-
-            submit_btn_1.click(predict_emotion_upload, inputs=audio_upload, outputs=[output_text_1, audio_player_1])
-
-        with gr.TabItem("Select from List"):
-            with gr.Group():
-                submit_btn_2 = gr.Button("Classify Selected Audio")
-            audio_player_2 = gr.Audio(label="Selected Audio Player", interactive=True)
-            output_text_2 = gr.Textbox(label="Prediction")
+# Create the Gradio interface with both tabs
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    description=description,
+    tab_name="Upload Audio"
+)
 
-            audio_files_dropdown.change(create_audio_player, inputs=audio_files_dropdown, outputs=audio_player_2)
-            submit_btn_2.click(predict_emotion_select, inputs=audio_files_dropdown, outputs=output_text_2)
+# Add the second tab to the interface
+iface.add_tab(
+    "Select Preloaded Audio",
+    gr.Interface(
+        fn=predict_emotion_from_file,
+        inputs=file_dropdown,
+        outputs=outputs,
+        tab_name="Select Preloaded Audio"
+    )
+)
 
-demo.launch()
+# Launch the Gradio app
+iface.launch()