Update app.py

app.py

@@ -5,8 +5,6 @@ from transformers import pipeline
 info = get_article()
 
 
-
-
 #model_name2id = {"Model A": "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD", "Model B": "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"}
 
 def classify_sentiment(audio):
@@ -25,18 +23,10 @@ Link to Processed MESD dataset: [Please Click Here](https://huggingface.co/datas
 
 Note: The Audio examples provided for testing this app were randomly picked from the test dataset.
 """
-#gr.Interface(
-    #fn = classify_sentiment,
-    #inputs = input_audio,
-    #outputs = label,
-    #examples=[["basta_neutral.wav"], ["detras_disgust.wav"], ["mortal_sadness.wav"], ["respiracion_happiness.wav"], ["robo_fear.wav"]],
-    #title = "🔊 Audio Sentiment Classifier",
-    #description = "Demostración de Gradio para la clasificación de sentimientos de audios usando Wav2Vec2",
-    #theme="huggingface").launch()
-
-article = "a"
+
+
 # generate and launch interface
-interface = gr.Interface(fn=classify_sentiment, inputs=input_audio, outputs=label, examples=[["basta_neutral.wav"], ["detras_disgust.wav"], ["mortal_sadness.wav"], ["respiracion_happiness.wav"], ["robo_fear.wav"]], article=article, css=info['css'], theme='huggingface', title=info['title'], allow_flagging='never', description=description)
+interface = gr.Interface(fn=classify_sentiment, inputs=input_audio, outputs=label, examples=[["basta_neutral.wav"], ["detras_disgust.wav"], ["mortal_sadness.wav"], ["respiracion_happiness.wav"], ["robo_fear.wav"]], article=info['article'], css=info['css'], theme='huggingface', title=info['title'], allow_flagging='never', description=description)
 interface.launch() 
 
-#info['description'] info['article']
+#info['description']