app.py

import warnings
warnings.filterwarnings('ignore')
import transformers
import gradio as gr
from transformers import pipeline
from TTS.api import TTS

#1 - Sentiment Analysis Tab:  
sentiment =pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")

def get_sentiment(text):
    result = sentiment(text)
    label = result[0]['label']
    score = result[0]['score']
    return label, score

#2 - Summarization Tab:  
summary_pipe = pipeline("summarization", model="google/pegasus-cnn_dailymail")

def get_summary(text):
    max_len = int(len(text.split()) * 0.35)
    max_len = max(5, min(max_len, 512))
    min_len = int(len(text.split()) * 0.2)
    
    result = summary_pipe(text, max_length=max_len, min_length=min_len, do_sample=False)
    return result[0]['summary_text']

#3 - text-to-Speech Tab:
tts_model= TTS("tts_models/en/ljspeech/tacotron2-DDC", gpu=False)

def text_to_speech(text):
    filename = "output.wav"
    tts_model.tts_to_file(text=text, file_path=filename)
    return filename


with gr.Blocks() as AI_applications:
    with gr.Tab("Sentiment Analysis"):
        sentiment_input = gr.Textbox(label="Enter Text")
        output_label = gr.Text(label="Sentiment Label")
        output_score = gr.Text(label="Confidence Score")
        analyze_button = gr.Button("Analyze Sentiment")
        analyze_button.click(fn=get_sentiment,inputs=sentiment_input ,outputs=[output_label, output_score])

    with gr.Tab("Summarization"):

        summary_input  = gr.Textbox(label="Enter Long Text", lines=10, placeholder="Paste your text here...")
        output_summary = gr.Textbox(label="Summary", lines=5)
        summarize_btn  = gr.Button("Summarize")
        summarize_btn .click(fn=get_summary, inputs=summary_input, outputs=output_summary)

    with gr.Tab("Text to Speech"):

        tts_input = gr.Textbox(label="Enter Text to Speak", lines=4, placeholder="Type something...")
        output_audio = gr.Audio(label="Generated Speech", type="filepath")
        generate_btn = gr.Button("Generate Speech")
        generate_btn.click(fn=text_to_speech, inputs=tts_input , outputs=output_audio)

AI_applications.launch()