Update app.py

app.py

@@ -1,23 +1,42 @@
 import gradio as gr
+from transformers import pipeline
 
-#################################################################
-#1: Text to Speech
-#import gradio as gr
-title = "Text to Speech Translation"
+# Load sentiment analysis model
+sentiment_analyzer = pipeline("sentiment-analysis")
+
+# Text to Speech
+title = "Text to Speech with Sentiment Analysis"
 tts_examples = [
     "I love learning machine learning",
     "How do you do?",
 ]
-tts_demo = gr.Interface.load(
-    "huggingface/facebook/fastspeech2-en-ljspeech",
-    title = title,
-    examples=tts_examples,
-    description="Give me something to say!",
-)
 
+def tts_with_sentiment(text):
+    # Get sentiment
+    sentiment_result = sentiment_analyzer(text)[0]
+
+    # Adjust speech synthesis parameters based on sentiment
+    # You can customize this part based on the sentiment labels returned by your sentiment analysis model
+
+    # 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).
+
+    # Replace the following line with your desired text-to-speech model and parameters.
+    speech_output = f"This is a {sentiment_result['label']} sentiment: {text}"
+
+    return speech_output
+
+tts_demo = gr.Interface(
+    fn=tts_with_sentiment,
+    inputs="text",
+    outputs="audio",
+    examples=tts_examples,
+    title=title,
+    description="Give me something to say with sentiment analysis!",
+)
 
-demo = gr.TabbedInterface([tts_demo], ["Text to speech"])
+demo = gr.TabbedInterface([tts_demo], ["Text to speech with sentiment"])
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()