app.py

import gradio as gr
from transformers import pipeline
from gtts import gTTS
import tempfile

# Load models from Hugging Face
sentiment_model = pipeline("sentiment-analysis")
summarizer_model = pipeline("summarization")

# Sentiment analysis function
def analyze_sentiment(text):
    result = sentiment_model(text)[0]
    label = result['label']
    score = round(result['score'], 2)
    return f"Sentiment: {label}, Confidence: {score}"

# Summarization function
def summarize_text(text):
    summary = summarizer_model(text, max_length=60, min_length=15, do_sample=False)
    return summary[0]['summary_text']

# Text-to-speech function
def text_to_speech(text):
    tts = gTTS(text)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
        tts.save(fp.name)
        return fp.name

# Build Gradio interface with Tabs
with gr.Blocks() as demo:
    gr.Markdown("## 📘 Homework - Tuwaiq Academy")

    with gr.Tabs():
        with gr.TabItem("🔍 Sentiment Analysis"):
            input_sentiment = gr.Textbox(label="Enter your text", lines=6, placeholder="Type your text here...")
            output_sentiment = gr.Textbox(label="Sentiment Result")
            btn_sentiment = gr.Button("Analyze Sentiment")
            btn_sentiment.click(analyze_sentiment, inputs=input_sentiment, outputs=output_sentiment)

        with gr.TabItem("📝 Summarization"):
            input_summary = gr.Textbox(label="Enter your text", lines=6, placeholder="Type your text here...")
            output_summary = gr.Textbox(label="Summary")
            btn_summarize = gr.Button("Summarize")
            btn_summarize.click(summarize_text, inputs=input_summary, outputs=output_summary)

        with gr.TabItem("🔊 Text to Speech"):
            input_tts = gr.Textbox(label="Enter your text", lines=6, placeholder="Type your text here...")
            output_audio = gr.Audio(label="Speech Output", type="filepath")
            btn_tts = gr.Button("Convert to Speech")
            btn_tts.click(text_to_speech, inputs=input_tts, outputs=output_audio)

demo.launch()