Create app.py

app.py

@@ -0,0 +1,29 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load model with error handling
+@st.cache_resource
+def load_model():
+    try:
+        return pipeline('sentiment-analysis', model="distilbert-base-uncased-finetuned-sst-2-english")
+    except Exception as e:
+        st.error(f"Model loading failed: {str(e)}")
+        st.stop()
+
+classifier = load_model()
+
+# Streamlit UI
+st.title("CPU Sentiment Analysis")
+user_input = st.text_area("Enter text to analyze:", "I love this simple version!")
+
+if st.button("Analyze"):
+    if user_input:
+        try:
+            result = classifier(user_input)
+            st.subheader("Result:")
+            emoji = "😊" if result[0]['label'] == 'POSITIVE' else "😞"
+            st.write(f"{emoji} **{result[0]['label']}** (confidence: {result[0]['score']:.2%})")
+        except Exception as e:
+            st.error(f"Analysis failed: {str(e)}")
+    else:
+        st.warning("Please enter some text first!")