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app.py

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+import streamlit as st
+from transformers import pipeline
+
+# ---------------------- Page Config ----------------------
+st.set_page_config(page_title="🧠 Smart NLP Assistant", layout="centered")
+
+# ---------------------- Sidebar --------------------------
+st.sidebar.title("🧠 Smart NLP Assistant")
+task = st.sidebar.selectbox("📌 Select an NLP Task:", 
+                            ["📝 Text Classification", "❓ Question Answering", "📰 Summarization"])
+
+st.title("🌟 NLP Companion App")
+st.markdown("Interact with powerful **Transformer models** for different Natural Language Processing tasks right in your browser!")
+
+# -------------------- Load Models ------------------------
+@st.cache_resource
+def load_pipelines():
+    sentiment_model = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
+    qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
+    summarizer_model = pipeline("summarization", model="facebook/bart-large-cnn")
+    return sentiment_model, qa_model, summarizer_model
+
+text_classifier, qa_pipeline, summarizer = load_pipelines()
+
+# -------------------- Task: Sentiment Analysis ---------------------
+if task == "📝 Text Classification":
+    st.subheader("📝 Sentiment Analysis")
+    user_input = st.text_input("✍️ Enter a sentence to analyze sentiment:")
+    
+    if user_input.strip():
+        with st.spinner("🔍 Analyzing sentiment..."):
+            result = text_classifier(user_input)[0]
+        st.success(f"📣 **Sentiment:** {result['label'].capitalize()}")
+        st.info(f"🎯 **Confidence Score:** {result['score']*100:.2f}%")
+    else:
+        st.warning("⚠️ Please enter some text above to proceed.")
+
+# -------------------- Task: Question Answering ---------------------
+elif task == "❓ Question Answering":
+    st.subheader("❓ Ask a Question")
+    context = st.text_area("📄 Paste the context passage:")
+    question = st.text_input("🔍 Ask your question here:")
+    
+    if context.strip() and question.strip():
+        with st.spinner("💡 Finding the answer..."):
+            result = qa_pipeline(question=question, context=context)
+        st.success(f"✅ **Answer:** {result['answer']}")
+        st.info(f"🎯 **Confidence Score:** {result['score']*100:.2f}%")
+    elif question or context:
+        st.warning("⚠️ Please provide both context and question.")
+
+# -------------------- Task: Summarization ---------------------
+elif task == "📰 Summarization":
+    st.subheader("📰 Summarize Text")
+    long_text = st.text_area("📃 Paste or type the long text to summarize:")
+    
+    if long_text.strip():
+        with st.spinner("🧠 Generating summary..."):
+            summary = summarizer(long_text, max_length=150, min_length=30, do_sample=False)[0]
+        st.success("📝 **Summary:**")
+        st.write(summary["summary_text"])
+    else:
+        st.warning("⚠️ Please enter content to summarize.")