Update app.py

app.py

@@ -14,7 +14,6 @@ openai_client = AzureOpenAI(
   azure_endpoint = "https://hkust.azure-api.net" # per HKUST instructions
 )
 
-# ─── (your existing cache decorators) ────────────────────────────────────────
 @st.cache_resource
 def load_sentiment_pipeline():
     model_name = "mayf/amazon_reviews_bert_ft"
@@ -31,11 +30,9 @@ def load_sentiment_pipeline():
 def load_keybert_model():
     return KeyBERT(model="all-MiniLM-L6-v2")
 
-
 def main():
     st.title("📊 Review Sentiment & Keyword Analyzer + GPT Insights")
 
-    # ─── Inputs & Models ──────────────────────────────────────────────────────
     review = st.text_area("Enter your review:")
     if not st.button("Analyze Review"):
         return
@@ -44,15 +41,27 @@ def main():
         st.warning("Please enter a review to analyze.")
         return
 
-    # ─── Sentiment & Keywords ─────────────────────────────────────────────────
+    # Sentiment & Keywords
     sentiment_pipeline = load_sentiment_pipeline()
     kw_model = load_keybert_model()
 
     scores = sentiment_pipeline(review)[0]
     sentiment_results = {item['label']: float(item['score']) for item in scores}
+
     st.subheader("Sentiment Scores")
     st.json({k: round(v, 4) for k, v in sentiment_results.items()})
 
+    # Bar chart of sentiment scores
+    df_scores = pd.DataFrame.from_dict(sentiment_results, orient='index', columns=['score'])
+    df_scores.index.name = 'label'
+    st.bar_chart(df_scores)
+
+    # Highest sentiment
+    max_label, max_score = max(sentiment_results.items(), key=lambda x: x[1])
+    st.subheader("Highest Sentiment")
+    st.write(f"**{max_label}** ({max_score:.4f})")
+
+    # Top 3 keywords
     keywords = kw_model.extract_keywords(
         review,
         keyphrase_ngram_range=(1, 2),
@@ -61,50 +70,31 @@ def main():
     )
     st.subheader("Top 3 Keywords")
     for kw, score in keywords:
-        st.write(f"- **{kw}** (Score: {score:.4f})")
-
-    # ─── Determine Highest Sentiment ───────────────────────────────────────────
-    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(f"• {kw} ({score:.4f})")
 
-    # ─── GPT-Driven Analysis & Suggestions ────────────────────────────────────
+    # GPT-Driven Analysis & Suggestions (concise)
     st.subheader("GPT Analysis & Seller Suggestions")
-
-    # build a single text prompt for GPT
     prompt = f"""
-You are a helpful assistant for e-commerce sellers. 
-Here is a product review, its sentiment breakdown, and the top keywords:
-
-Review:
-\"\"\"{review}\"\"\"
-
-Sentiment scores:
-{sentiment_results}
-
-Top keywords:
-{[kw for kw, _ in keywords]}
-
-First, provide a one-paragraph professional analysis of what the customer feels and why (combine sentiment + keywords). 
-Then, give 3 detailed, actionable suggestions the seller can implement to improve future reviews or address the feedback.
+You are a concise e-commerce feedback analyst.
+Review: """{review}"""
+Scores: {sentiment_results}
+Keywords: {[kw for kw, _ in keywords]}
+Provide:
+1. One-sentence summary of customer sentiment.
+2. Three bullet-point suggestions, each no more than 8 words.
 """
 
-    # call Azure OpenAI
     response = openai_client.chat.completions.create(
-    model="gpt-35-turbo",                     # ← use your deployed model name here
-    messages=[
-        {"role": "system", "content": "You are a product-feedback analyst."},
-        {"role": "user", "content": prompt}
-    ],
-    temperature=0.7,
-    max_tokens=400
-)
-
-    # display GPT’s reply
-    gpt_reply = response.choices[0].message.content
+        model="gpt-35-turbo",
+        messages=[
+            {"role": "system", "content": "You are a product-feedback analyst."},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0.5,
+        max_tokens=120
+    )
+    gpt_reply = response.choices[0].message.content.strip()
     st.markdown(gpt_reply)
 
-
 if __name__ == "__main__":
-    # make sure your env vars are set: AZURE_OPENAI_KEY, AZURE_OPENAI_ENDPOINT
     main()