app.py

import gradio as gr
from transformers import pipeline

# 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?",
]

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 with sentiment"])
if __name__ == "__main__":
    demo.launch()