import gradio as gr
from transformers import pipeline

sentiment_model = pipeline("sentiment-analysis")
chatbot_model = pipeline("text-generation", model="microsoft/DialoGPT-medium")
summarization_model = pipeline("summarization")
text_to_speech_model = pipeline("text-to-speech")

def get_sentiment(input_text):
    analysis = sentiment_model(input_text)
    sent = analysis[0]['label']
    score = analysis[0]['score']
    return sent, score

def chatbot_response(input_text):
    response = chatbot_model(input_text, max_length=100, do_sample=True)[0]['generated_text']
    return response

def summarize_text(input_text):
    summary = summarization_model(input_text, max_length=100, min_length=30, do_sample=False)
    return summary[0]['summary_text']

def text_to_speech(input_text):
    audio = text_to_speech_model(input_text)
    return audio

with gr.Blocks() as demo:
    gr.Markdown("## Multi-Function AI Language Application")
    
    with gr.Tab("Sentiment Analysis"):
        text_input = gr.Textbox(label="Enter text for sentiment analysis:")
        sentiment_output = gr.Textbox(label="Sentiment")
        score_output = gr.Number(label="Confidence Score")
        sentiment_button = gr.Button("Analyze")
        sentiment_button.click(get_sentiment, inputs=text_input, outputs=[sentiment_output, score_output])
    
    with gr.Tab("Chatbot"):
        chat_input = gr.Textbox(label="Enter your message:")
        chat_output = gr.Textbox(label="Chatbot Response")
        chat_button = gr.Button("Send")
        chat_button.click(chatbot_response, inputs=chat_input, outputs=chat_output)
    
    with gr.Tab("Summarization"):
        summary_input = gr.Textbox(label="Enter text to summarize:", lines=5)
        summary_output = gr.Textbox(label="Summary")
        summary_button = gr.Button("Summarize")
        summary_button.click(summarize_text, inputs=summary_input, outputs=summary_output)
    
    with gr.Tab("Text-to-Speech"):
        tts_input = gr.Textbox(label="Enter text to convert to speech:")
        tts_output = gr.Audio(label="Generated Speech")
        tts_button = gr.Button("Convert")
        tts_button.click(text_to_speech, inputs=tts_input, outputs=tts_output)

# Launch the app
demo.launch()