Update app.py

app.py

@@ -23,7 +23,7 @@ def predict_and_ctc_lm_decode(input_file):
     transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1)["text"]
     pipe1 = pipeline("sentiment-analysis", model = "finiteautomata/beto-sentiment-analysis")
     sentiment = pipe1(transcribed_text)[0]["label"]
-    return sentiment
+    return f"Detected Sentiment: {sentiment}"
 
 description = """ This is a Gradio demo for Sentiment Analysis of Transcribed Spanish Audio. First, we do Speech to Text, and then we perform sentiment analysis on the obtained transcription of the input audio.