Upload app.py

app.py

@@ -1,6 +1,9 @@
+from reader import get_article
 import gradio as gr
 from transformers import pipeline
 
+info = get_article()
+
 #Model_1 = "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"
 #Model_2 ="hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"
 
@@ -16,7 +19,7 @@ label = gr.outputs.Label(num_top_classes=5)
 
 ################### Gradio Web APP ################################
 
-title = "Audio Sentiment Classifier"
+#title = "Audio Sentiment Classifier"
 
 description = """
 <p>
@@ -33,11 +36,15 @@ This application classifies the sentiment of the audio input provided by the use
 
 
 
-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()
+#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()
+
+# 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=info['article'], css=info['css'], theme='huggingface', title=info['title'], allow_flagging='never', description=info['description'])
+interface.launch()