Update app.py

app.py

@@ -1,13 +1,21 @@
+import os
 import streamlit as st
+from huggingface_hub import login
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 from keybert import KeyBERT
 
-# Initialize sentiment analysis pipeline
+# ─── Cached resource: Sentiment Analysis Pipeline ────────────────────────────
 @st.cache_resource
 def load_sentiment_pipeline():
     model_name = "mayf/amazon_reviews_bert_ft"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name,
+        use_auth_token=True
+    )
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_name,
+        use_auth_token=True
+    )
     return pipeline(
         "sentiment-analysis",
         model=model,
@@ -15,27 +23,28 @@ def load_sentiment_pipeline():
         return_all_scores=True
     )
 
-# Initialize KeyBERT model
+# ─── Cached resource: KeyBERT Model ─────────────────────────────────────────
 @st.cache_resource
 def load_keybert_model():
     return KeyBERT(model="all-MiniLM-L6-v2")
 
-sentiment_pipeline = load_sentiment_pipeline()
-kw_model = load_keybert_model()
+# ─── Main Application ───────────────────────────────────────────────────────
+def main():
+    sentiment_pipeline = load_sentiment_pipeline()
+    kw_model = load_keybert_model()
 
-# Streamlit App
-st.set_page_config(page_title="Review Analyzer", layout="wide")
-st.title("📊 Review Sentiment & Keyword Analyzer")
+    st.set_page_config(page_title="Review Analyzer", layout="wide")
+    st.title("📊 Review Sentiment & Keyword Analyzer")
 
-review = st.text_area("Enter your review:")
+    review = st.text_area("Enter your review:")
+
+    if st.button("Analyze Review"):
+        if not review:
+            st.warning("Please enter a review to analyze.")
+            return
 
-if st.button("Analyze Review"):
-    if not review:
-        st.warning("Please enter a review to analyze.")
-    else:
         # Sentiment Analysis
         scores = sentiment_pipeline(review)[0]
-        # Convert scores to displayable format
         sentiment_results = {item['label']: round(item['score'], 4) for item in scores}
         st.subheader("Sentiment Scores")
         st.json(sentiment_results)
@@ -51,9 +60,12 @@ if st.button("Analyze Review"):
         for kw, score in keywords:
             st.write(f"- **{kw}** (Score: {score:.4f})")
 
-        # Final Score: example combining sentiment and keywords
+        # Composite Score
         avg_sentiment = sum(sentiment_results.values()) / len(sentiment_results)
         st.subheader("Composite Score with Keywords")
         st.write(f"Average Sentiment Score: {avg_sentiment:.4f}")
         st.write("Keywords used for this score:")
-        st.write(', '.join([kw for kw, _ in keywords]))
+        st.write(', '.join([kw for kw, _ in keywords]))
+
+if __name__ == "__main__":
+    main()