Update app.py

app.py

@@ -4,21 +4,20 @@ from huggingface_hub import login
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 from keybert import KeyBERT
 
-# ─── Streamlit Page Config must be first Streamlit command ────────────────
+# ─── Streamlit Page Config ─────────────────────────────────────────────────
 st.set_page_config(page_title="Review Analyzer", layout="wide")
 
+# ─── Authenticate to Hugging Face Hub ───────────────────────────────────────
+# ensure HF_TOKEN is set in your environment
+token = os.environ.get("HF_TOKEN", "")
+login(token=token, add_to_git_credential=False)
+
 # ─── 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,
-        use_auth_token=True
-    )
-    model = AutoModelForSequenceClassification.from_pretrained(
-        model_name,
-        use_auth_token=True
-    )
+    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,
@@ -37,7 +36,6 @@ def main():
     kw_model = load_keybert_model()
 
     st.title("📊 Review Sentiment & Keyword Analyzer")
-
     review = st.text_area("Enter your review:")
 
     if st.button("Analyze Review"):
@@ -47,9 +45,9 @@ def main():
 
         # Sentiment Analysis
         scores = sentiment_pipeline(review)[0]
-        sentiment_results = {item['label']: round(item['score'], 4) for item in scores}
+        sentiment_results = {item['label']: float(item['score']) for item in scores}
         st.subheader("Sentiment Scores")
-        st.json(sentiment_results)
+        st.json({k: round(v, 4) for k, v in sentiment_results.items()})
 
         # Keyword Extraction (top 3)
         keywords = kw_model.extract_keywords(
@@ -62,11 +60,11 @@ def main():
         for kw, score in keywords:
             st.write(f"- **{kw}** (Score: {score:.4f})")
 
-        # 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:")
+        # Highest Sentiment Label
+        max_label, max_score = max(sentiment_results.items(), key=lambda x: x[1])
+        st.subheader("Highest Sentiment")
+        st.write(f"{max_label} (Score: {max_score:.4f})")
+        st.write("Keywords used for this analysis:")
         st.write(', '.join([kw for kw, _ in keywords]))
 
 if __name__ == "__main__":