Update app.py

app.py

@@ -1,8 +1,8 @@
 import gradio as gr
-from transformers import pipeline
+from nltk.sentiment import SentimentIntensityAnalyzer
 
 # Load sentiment analysis model
-sentiment_analyzer = pipeline("sentiment-analysis")
+sia = SentimentIntensityAnalyzer()
 
 # Text to Speech
 title = "Text to Speech with Sentiment Analysis"
@@ -11,25 +11,25 @@ tts_examples = [
     "How do you do?",
 ]
 
-def tts_with_sentiment(text):
-    # Get sentiment
-    sentiment_result = sentiment_analyzer(text)[0]
+def get_sentiment(text):
+    # Get sentiment score
+    sentiment_score = sia.polarity_scores(text)["compound"]
 
     # Adjust speech synthesis parameters based on sentiment
-    # You can customize this part based on the sentiment labels returned by your sentiment analysis model
+    # You can customize this part based on the sentiment score.
 
     # For example, if sentiment is positive, use a happy tone; if negative, use a sad tone.
 
     # Modify the speech synthesis model and parameters accordingly.
-    # Use the sentiment_result['label'] to access sentiment label (positive/negative/neutral).
+    # Use the sentiment_score to adjust the tone.
 
     # Replace the following line with your desired text-to-speech model and parameters.
-    speech_output = f"This is a {sentiment_result['label']} sentiment: {text}"
+    speech_output = f"This is a text with sentiment score {sentiment_score}: {text}"
 
     return speech_output
 
 tts_demo = gr.Interface(
-    fn=tts_with_sentiment,
+    fn=get_sentiment,
     inputs="text",
     outputs="audio",
     examples=tts_examples,