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app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the sentiment pipeline
+sentiment_pipeline = pipeline("sentiment-analysis", model="fitsblb/YelpReviewsAnalyzer")
+
+def map_sentiment_label(label):
+    return {
+        "LABEL_0": "❌ Negative",
+        "LABEL_1": "⚖️ Neutral",
+        "LABEL_2": "✅ Positive"
+    }.get(label, "🤷 Unknown")
+
+st.set_page_config(page_title="Yelp Sentiment Analyzer", layout="centered")
+st.title("🍽️ Yelp Sentiment Analyzer")
+st.markdown("Enter a restaurant review and see the model's sentiment prediction:")
+
+example = "The food was amazing but the wait was long."
+review = st.text_area("Review", example)
+
+if st.button("Analyze Sentiment"):
+    if review.strip() == "":
+        st.warning("Please enter a review.")
+    else:
+        with st.spinner("Analyzing..."):
+            result = sentiment_pipeline(review)[0]
+            label = result["label"]
+            confidence = round(result["score"], 3)
+            st.success(f"**Sentiment:** {map_sentiment_label(label)}\n\n**Confidence:** {confidence}")

requirements.txt

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+streamlit
+transformers
+torch