Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load sentiment analysis model
+@st.cache_resource
+def load_model():
+    return pipeline(
+        "sentiment-analysis",
+        model="distilbert-base-uncased-finetuned-sst-2-english"
+    )
+
+sentiment_model = load_model()
+
+# Main app
+def main():
+    # UI setup
+    st.title("Sentiment Analyzer")
+    st.write("Enter a sentence to analyze its sentiment (Positive/Negative)")
+
+    # Text input
+    user_input = st.text_area("Your Text", placeholder="Type your sentence here...", height=100)
+
+    # Analyze button and result display
+    if st.button("Analyze"):
+        if user_input:
+            try:
+                result = sentiment_model(user_input)[0]
+                sentiment = result['label']
+                confidence = result['score']
+                
+                # Display results
+                st.success(f"Sentiment: {sentiment}")
+                st.info(f"Confidence: {confidence:.4f}")
+
+                # Footer shown only after analysis
+                st.markdown("""
+                    <p style="font-size: small; color: grey; text-align: center;">
+                        Developed By: Krishna Prakash
+                        <a href="https://www.linkedin.com/in/krishnaprakash-profile/" target="_blank">
+                            <img src="https://img.icons8.com/ios-filled/30/0077b5/linkedin.png" alt="LinkedIn" style="vertical-align: middle; margin: 0 5px;"/>
+                        </a>
+                    </p>
+                """, unsafe_allow_html=True)
+
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}")
+        else:
+            st.warning("Please enter some text to analyze!")
+
+if __name__ == "__main__":
+    main()