app.py

import gradio as gr
from transformers import pipeline

# Preload models
summarizer = pipeline("summarization")
sentiment_analyzer = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
translator_hi = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
translator_fr = pipeline("Helsinki-NLP/opus-mt-en-fr")
translator_de = pipeline("Helsinki-NLP/opus-mt-en-de")
translator_es = pipeline("Helsinki-NLP/opus-mt-en-es")
translator_ta = pipeline("translation", model="facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="tam_Taml")
speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-small")
question_generator = pipeline("e2e-qg")

# --- Functional Modules ---

def summarize(text):
    return summarizer(text, max_length=60, min_length=20, do_sample=False)[0]['summary_text']

def analyze_sentiment(text):
    result = sentiment_analyzer(text)[0]
    label = result["label"]
    if label == "LABEL_1":
        return "Neutral"
    elif label == "LABEL_2":
        return "Positive"
    else:
        return "Negative"

def translate(text, lang):
    if lang == "Tamil":
        return translator_ta(text)[0]["translation_text"]
    elif lang == "Hindi":
        return translator_hi(text)[0]["translation_text"]
    elif lang == "French":
        return translator_fr(text)[0]["translation_text"]
    elif lang == "German":
        return translator_de(text)[0]["translation_text"]
    elif lang == "Spanish":
        return translator_es(text)[0]["translation_text"]
    else:
        return "Unsupported Language"

def transcribe(audio):
    return speech_to_text(audio)["text"]

def generate_questions(text):
    output = question_generator(text)
    return "\n".join(f"- {item['question']}" for item in output[:10])

# --- UI Sections for each task ---

def summarization_ui():
    with gr.Column():
        input_text = gr.Textbox(label="Enter a long paragraph", lines=8, placeholder="Paste your paragraph here...")
        output_text = gr.Textbox(label="Summarized text", lines=4)
        gr.Button("Summarize").click(summarize, inputs=input_text, outputs=output_text)

def sentiment_ui():
    with gr.Column():
        input_text = gr.Textbox(label="Enter a sentence", lines=3)
        output_text = gr.Textbox(label="Sentiment")
        gr.Button("Analyze Sentiment").click(analyze_sentiment, inputs=input_text, outputs=output_text)

def translation_ui():
    with gr.Column():
        input_text = gr.Textbox(label="Enter English text", lines=3)
        lang_dropdown = gr.Dropdown(["Tamil", "Hindi", "French", "German", "Spanish"], value="Tamil", label="Target Language")
        output_text = gr.Textbox(label="Translated text", lines=3)
        gr.Button("Translate").click(translate, inputs=[input_text, lang_dropdown], outputs=output_text)

def speech_ui():
    with gr.Column():
        audio = gr.Audio(source="microphone", type="filepath", label="Record or Upload")
        output_text = gr.Textbox(label="Recognized Text")
        gr.Button("Convert Speech to Text").click(transcribe, inputs=audio, outputs=output_text)

def question_ui():
    with gr.Column():
        input_text = gr.Textbox(label="Enter a paragraph", lines=8)
        output_text = gr.Textbox(label="Generated Questions", lines=10)
        gr.Button("Generate Questions").click(generate_questions, inputs=input_text, outputs=output_text)

# --- Homepage Navigation ---

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("<h1 style='text-align:center;'>🌐 Multi-Task AI App (Hugging Face Space)</h1>")
    gr.Markdown("<h4 style='text-align:center;'>By Joel — Powered by Transformers</h4>")
    
    with gr.Row():
        btn1 = gr.Button("Text Summarization")
        btn2 = gr.Button("Sentiment Analysis")
        btn3 = gr.Button("Translation")
        btn4 = gr.Button("Speech-to-Text")
        btn5 = gr.Button("Question Generation")

    main_content = gr.Column()

    with main_content:
        output_area = gr.Column(visible=False)

    def load_tab(tab_name):
        with output_area:
            output_area.children.clear()
            if tab_name == "summarization":
                summarization_ui()
            elif tab_name == "sentiment":
                sentiment_ui()
            elif tab_name == "translation":
                translation_ui()
            elif tab_name == "speech":
                speech_ui()
            elif tab_name == "question":
                question_ui()
            output_area.visible = True

    btn1.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'summarization'")
    btn2.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'sentiment'")
    btn3.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'translation'")
    btn4.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'speech'")
    btn5.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'question'")

demo.launch()