Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+st.set_page_config(page_title="Multi-Task NLP Demo", page_icon="🤖")
+st.title("🤖 Multi-Task NLP with Hugging Face Transformers")
+st.write(
+    "Choose an NLP task and enter your text below. Powered by Hugging Face Transformers!"
+)
+
+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)
+
+@st.cache_resource
+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)