Update app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Custom CSS for styling
+st.markdown(
+    """
+    <style>
+    .stApp {
+        background: linear-gradient(120deg, #2C5364, #203A43, #0F2027);
+        color: #F0F0F0;
+    }
+    .stTitle {
+        text-align: center;
+        color: #FFD700;
+    }
+    .css-1d391kg p, .css-1v0mbdj p {
+        color: #F0F0F0;
+    }
+    .stButton > button:hover {
+        background-color: #FFD700;
+        color: #0F2027;
+        transform: scale(1.05);
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.title("🧠 NLP Tool with Hugging Face", anchor=False)
+
+st.sidebar.subheader("🔍 Explore NLP Features")
+
+# Dropdown to select NLP task
+nlp_task = st.sidebar.selectbox(
+    "Select an NLP Task:",
+    ["Text Classification", "Sentiment Analysis", "Question Answering", "Summarization", "Text Generation"]
+)
+
+# Input text area
+user_input = st.text_area("Enter your text here:", "", height=150)
+
+# Functionality for each task
+if user_input:
+    if nlp_task == "Text Classification":
+        st.subheader("📋 Text Classification")
+        classifier = pipeline("text-classification")
+        result = classifier(user_input)
+        st.write("**Classification Results:**")
+        for res in result:
+            st.write(f"- **Label**: {res['label']}, **Score**: {res['score']:.2f}")
+
+    elif nlp_task == "Sentiment Analysis":
+        st.subheader("😊 Sentiment Analysis")
+        sentiment_analyzer = pipeline("sentiment-analysis")
+        result = sentiment_analyzer(user_input)
+        st.write("**Sentiment Analysis Results:**")
+        for res in result:
+            st.write(f"- **Label**: {res['label']}, **Score**: {res['score']:.2f}")
+
+    elif nlp_task == "Question Answering":
+        st.subheader("❓ Question Answering")
+        question = st.text_input("Ask a question about the provided text:")
+        if question:
+            qa_pipeline = pipeline("question-answering")
+            result = qa_pipeline(question=question, context=user_input)
+            st.write(f"**Answer:** {result['answer']}")
+
+    elif nlp_task == "Summarization":
+        st.subheader("✂️ Summarization")
+        summarizer = pipeline("summarization")
+        summary = summarizer(user_input, max_length=50, min_length=25, do_sample=False)
+        st.write("**Summary:**")
+        st.write(summary[0]['summary_text'])
+
+    elif nlp_task == "Text Generation":
+        st.subheader("📝 Text Generation")
+        generator = pipeline("text-generation")
+        generated_text = generator(user_input, max_length=50, num_return_sequences=1)
+        st.write("**Generated Text:**")
+        st.write(generated_text[0]['generated_text'])
+
+# Space for connecting this app to another space
+st.sidebar.markdown("---")
+st.sidebar.subheader("🔗 Connect to Other Spaces")
+other_space_url = st.sidebar.text_input("Enter the URL of another Streamlit app or Hugging Face space:")
+if other_space_url:
+    st.sidebar.markdown(f"[Go to Connected Space]({other_space_url})")
+
+st.sidebar.success("Explore NLP tasks or connect to another space!")