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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()
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