app.py

# ============================
# 🟢 Install & Imports
# ============================

import gradio as gr
from transformers import pipeline
import torch
import gtts

print("Torch version:", torch.__version__)

# ============================
# 💬 Sentiment Analysis
# ============================

# Create sentiment analysis pipeline
sentiment_pipe = pipeline("sentiment-analysis")

def analyze_sentiment(text):
    result = sentiment_pipe(text)[0]
    label = result["label"]
    score = result["score"]
    return f"Label: {label}\nConfidence: {score:.2f}"

# ============================
# 🤖 Chatbot (DialoGPT)
# ============================

# Use Microsoft DialoGPT for more relevant replies
chatbot_pipe = pipeline("text-generation", model="microsoft/DialoGPT-medium")

def chat_response(user_message):
    # Provide prompt format to simulate a dialog
    prompt = f"User: {user_message}\nBot:"
    response = chatbot_pipe(prompt, max_length=100, do_sample=True, temperature=0.7)[0]["generated_text"]
    # Clean the output to extract only the bot reply
    reply = response.split("Bot:")[-1].strip()
    return reply

# ============================
# ✨ Summarization
# ============================

# Summarization pipeline
summarization_pipe = pipeline("summarization", model="facebook/bart-large-cnn")

def summarize_text(text):
    summary = summarization_pipe(text, max_length=130, min_length=30, do_sample=False)[0]["summary_text"]
    return summary

# ============================
# 🔊 Text-to-Speech
# ============================

def text_to_speech(text):
    tts = gtts.gTTS(text)
    tts.save("output.mp3")
    return "output.mp3"

# ============================
# 🌐 Gradio App (Multi-Tab)
# ============================

with gr.Blocks() as demo:
    gr.Markdown("# 🌟 Multi-Task Language Application\nChoose a tab below to explore different language AI tasks!")

    with gr.Tab("Sentiment Analysis"):
        text_input = gr.Textbox(label="Enter text")
        output = gr.Textbox(label="Sentiment Result")
        analyze_btn = gr.Button("Analyze")
        analyze_btn.click(analyze_sentiment, inputs=text_input, outputs=output)

    with gr.Tab("Chatbot"):
        chat_input = gr.Textbox(label="Ask something")
        chat_output = gr.Textbox(label="Bot Reply")
        chat_btn = gr.Button("Send")
        chat_btn.click(chat_response, inputs=chat_input, outputs=chat_output)

    with gr.Tab("Summarization"):
        long_text = gr.Textbox(label="Paste text", lines=10, placeholder="Paste a long text here...")
        summary_output = gr.Textbox(label="Summary")
        summary_btn = gr.Button("Summarize")
        summary_btn.click(summarize_text, inputs=long_text, outputs=summary_output)

    with gr.Tab("Text-to-Speech"):
        tts_text = gr.Textbox(label="Enter text to convert to speech")
        audio_output = gr.Audio(label="Generated Speech")
        tts_btn = gr.Button("Generate Voice")
        tts_btn.click(text_to_speech, inputs=tts_text, outputs=audio_output)

demo.launch()