Update app.py

app.py

@@ -32,13 +32,15 @@ def load_keybert_model():
 
 # ─── FLAN-T5 Generation Pipeline ────────────────────────────────────────────
 @st.cache_resource
-def load_flant5_pipeline():
+def load_response_pipeline():
     seq_tok = AutoTokenizer.from_pretrained("google/flan-t5-base")
     seq_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
     return pipeline(
         "text2text-generation",
         model=seq_model,
-        tokenizer=seq_tok
+        tokenizer=seq_tok,
+        max_new_tokens=150,
+        do_sample=False
     )
 
 LABEL_MAP = {
@@ -60,30 +62,20 @@ def main():
         st.warning("Please enter a review to analyze.")
         return
 
-    progress = st.progress(0)
-
-    # Load models
-    progress.text("Loading models...")
-    sentiment_pipeline = load_sentiment_pipeline()
-    kw_model = load_keybert_model()
-    generation_pipeline = load_flant5_pipeline()
-    progress.progress(20)
-
+    # ─── KEEP THIS BLOCK UNCHANGED ─────────────────────────────────────────
     # Sentiment Analysis
-    progress.text("Analyzing sentiment...")
+    sentiment_pipeline = load_sentiment_pipeline()
     raw_scores = sentiment_pipeline(review)[0]
     sentiment_results = {LABEL_MAP[item['label']]: float(item['score']) for item in raw_scores}
-    progress.progress(40)
 
     # Keyword Extraction
-    progress.text("Extracting keywords...")
+    kw_model = load_keybert_model()
     keywords = kw_model.extract_keywords(
         review,
         keyphrase_ngram_range=(1, 2),
         stop_words="english",
         top_n=3
     )
-    progress.progress(60)
 
     # Display Results
     col1, col2 = st.columns(2)
@@ -96,7 +88,6 @@ def main():
             st.write(f"• {kw} ({score:.4f})")
 
     # Bar Chart
-    progress.text("Rendering chart...")
     df_scores = pd.DataFrame.from_dict(
         sentiment_results,
         orient='index',
@@ -104,44 +95,27 @@ def main():
     )
     df_scores.index.name = 'Sentiment'
     st.bar_chart(df_scores)
-    progress.progress(80)
 
     # Highlight Highest Sentiment
     max_label, max_score = max(sentiment_results.items(), key=lambda x: x[1])
     st.markdown(f"**Highest Sentiment:** **{max_label}** ({max_score:.4f})")
+    # ────────────────────────────────────────────────────────────────────
 
-    # Generate Detailed Recommendations for select sentiments
-    progress.text("Generating detailed recommendations...")
-    if max_label in ["Very Negative", "Negative", "Neutral"]:
-        prompt = f"""
-You are a product quality and customer experience specialist at an e-commerce food retailer.
-
-Customer Review:
-"{review}"
-
-Instructions: Analyze the feedback and provide three distinct, actionable improvement recommendations. For each, include a concise title and a detailed explanation in 5–7 sentences, plus a bullet list of 3–5 execution steps and a measure of impact.
-
-Output only the three numbered recommendations (1–3), each with its title, detailed explanation, steps, and impact measure.
-"""
-        # Ensure longer outputs by specifying generation parameters
-        response = generation_pipeline(
-            prompt,
-            max_new_tokens=300,
-            min_length=200,
-            do_sample=True,
-            temperature=0.7,
-            top_p=0.9,
-            no_repeat_ngram_size=2
-        )
-        detailed = response[0]["generated_text"]
-        st.markdown(detailed)
+    # Generate appropriate reply
+    response_pipeline = load_response_pipeline()
+    if max_label in ["Positive", "Very Positive"]:
+        prompt = f"You are a friendly customer success representative. The customer said: \"{review}\". Write a warm, appreciative reply celebrating their positive experience."
     else:
-        st.info("Detailed recommendations are provided only for Neutral, Negative, or Very Negative reviews.")
-
-    # Done
-    progress.progress(100)
-    progress.text("Done!")
+        prompt = (
+            f"You are a helpful customer support specialist. The customer said: \"{review}\". "
+            f"Key topics: {[kw for kw, _ in keywords]}. "
+            "Ask 1-2 clarifying questions to understand their concerns, then suggest specific improvements or next steps."
+        )
+    result = response_pipeline(prompt)
+    reply = result[0]['generated_text'].strip()
 
+    st.subheader("Generated Reply")
+    st.write(reply)
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()