Update app.py

app.py

@@ -1,32 +1,26 @@
 import streamlit as st
+from transformers import pipeline
 
-# Sample positive and negative word lists
-positive_words = ["good", "great", "happy", "excellent", "love", "wonderful", "amazing", "nice", "awesome"]
-negative_words = ["bad", "terrible", "sad", "hate", "horrible", "awful", "worst", "angry", "disappointed"]
+# Load sentiment-analysis pipeline from Hugging Face
+@st.cache_resource
+def load_model():
+    return pipeline("sentiment-analysis")
 
-def analyze_sentiment(text):
-    text = text.lower()
-    pos_count = sum(word in text for word in positive_words)
-    neg_count = sum(word in text for word in negative_words)
-
-    if pos_count > neg_count:
-        return "Positive 😊", pos_count, neg_count
-    elif neg_count > pos_count:
-        return "Negative 😞", pos_count, neg_count
-    else:
-        return "Neutral 😐", pos_count, neg_count
+sentiment_pipeline = load_model()
 
 # Streamlit UI
-st.title("Simple Sentiment Analyzer (No ML) 🧠")
-st.write("This app detects sentiment using rule-based logic (no ML model).")
+st.title("Smart Sentiment Analyzer 🤖")
+st.markdown("Analyze your sentence using a real AI model (DistilBERT).")
 
-text_input = st.text_area("Enter your sentence here:")
+text = st.text_area("Enter your sentence:")
 
-if st.button("Analyze"):
-    if text_input.strip() == "":
-        st.warning("Please enter some text!")
+if st.button("Analyze Sentiment"):
+    if text.strip() == "":
+        st.warning("Please enter some text.")
     else:
-        sentiment, pos, neg = analyze_sentiment(text_input)
-        st.subheader("Result:")
-        st.write(f"**Sentiment:** {sentiment}")
-        st.write(f"Positive Words: {pos}, Negative Words: {neg}")
+        result = sentiment_pipeline(text)[0]
+        label = result["label"]
+        score = round(result["score"] * 100, 2)
+
+        st.success(f"**Sentiment:** {label}")
+        st.info(f"**Confidence:** {score}%")