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| import streamlit as st | |
| from transformers import pipeline | |
| st.set_page_config(page_title="Multi-Task NLP Hub", page_icon="🤖") | |
| st.title("🤖 Multi-Task NLP ") | |
| st.write( | |
| "Choose an NLP task and enter your text below." | |
| ) | |
| TASKS = { | |
| "Sentiment Analysis": { | |
| "pipeline": "sentiment-analysis", | |
| "model": "distilbert-base-uncased-finetuned-sst-2-english", | |
| "description": "Classify text as positive or negative sentiment.", | |
| }, | |
| "Summarization": { | |
| "pipeline": "summarization", | |
| "model": "sshleifer/distilbart-cnn-12-6", | |
| "description": "Summarize long articles or text passages.", | |
| }, | |
| "Translation (English → French)": { | |
| "pipeline": "translation_en_to_fr", | |
| "model": "Helsinki-NLP/opus-mt-en-fr", | |
| "description": "Translate English text to French.", | |
| }, | |
| "Text Generation": { | |
| "pipeline": "text-generation", | |
| "model": "gpt2", | |
| "description": "Generate text based on a prompt.", | |
| }, | |
| } | |
| task = st.selectbox("Choose NLP Task:", list(TASKS.keys())) | |
| st.caption(TASKS[task]["description"]) | |
| user_input = st.text_area("Enter your text:", height=150) | |
| def get_pipeline(task_name): | |
| t = TASKS[task_name] | |
| if task_name == "Translation (English → French)": | |
| return pipeline("translation_en_to_fr", model=t["model"]) | |
| else: | |
| return pipeline(t["pipeline"], model=t["model"]) | |
| nlp = get_pipeline(task) | |
| if st.button("Run"): | |
| if not user_input.strip(): | |
| st.warning("Please enter some text first.") | |
| else: | |
| with st.spinner("Processing..."): | |
| if task == "Summarization": | |
| # Summarization expects max 1024 tokens, so we truncate for demo | |
| result = nlp(user_input[:1024], max_length=130, min_length=30, do_sample=False) | |
| summary = result[0]["summary_text"] | |
| st.markdown("**Summary:**") | |
| st.success(summary) | |
| elif task == "Sentiment Analysis": | |
| result = nlp(user_input) | |
| label = result[0]["label"] | |
| score = result[0]["score"] | |
| st.markdown(f"**Sentiment:** `{label}` \n**Confidence:** `{score:.2%}`") | |
| elif task == "Translation (English → French)": | |
| result = nlp(user_input) | |
| translation = result[0]["translation_text"] | |
| st.markdown("**French Translation:**") | |
| st.success(translation) | |
| elif task == "Text Generation": | |
| # For demo, limit to 50 tokens | |
| result = nlp(user_input, max_length=50, num_return_sequences=1) | |
| generated = result[0]["generated_text"] | |
| st.markdown("**Generated Text:**") | |
| st.info(generated) |