import gradio as gr
import nltk

# Download the VADER lexicon
nltk.download('vader_lexicon')

# Rest of your code
from nltk.sentiment import SentimentIntensityAnalyzer

# Text to Speech
title = "Text to Speech with Sentiment Analysis"
tts_examples = [
    "I love learning machine learning",
    "How do you do?",
]

def get_sentiment(text):
    # Get sentiment score
    sia = SentimentIntensityAnalyzer()
    sentiment_score = sia.polarity_scores(text)["compound"]

    # Adjust speech synthesis parameters based on sentiment
    # 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_score to adjust the tone.

    # Replace the following line with your desired text-to-speech model and parameters.
    speech_output = f"This is a text with sentiment score {sentiment_score}: {text}"

    return {"audio": speech_output}

tts_demo = gr.Interface(
    fn=get_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()