Update app.py

app.py

@@ -33,15 +33,13 @@ def load_keybert_model():
 # ─── FLAN-T5 Generation Pipeline ────────────────────────────────────────────
 @st.cache_resource
 def load_flant5_pipeline():
-    # Explicitly load the Seq2Seq model & tokenizer to avoid truncation/classification fallback
-    seq_tok = AutoTokenizer.from_pretrained("google/flan-t5-large")
-    seq_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
+    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,
-        # ensure we generate up to 400 new tokens
-        max_new_tokens=400,
+        max_new_tokens=300,
         do_sample=True,
         temperature=0.7
     )
@@ -77,10 +75,7 @@ def main():
     # Sentiment Analysis
     progress.text("Analyzing sentiment...")
     raw_scores = sentiment_pipeline(review)[0]
-    sentiment_results = {
-        LABEL_MAP[item['label']]: float(item['score'])
-        for item in raw_scores
-    }
+    sentiment_results = {LABEL_MAP[item['label']]: float(item['score']) for item in raw_scores}
     progress.progress(40)
 
     # Keyword Extraction
@@ -115,28 +110,47 @@ def main():
     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})"
-    )
-
-    # FLAN-T5 Analysis & Suggestions
-    progress.text("Generating insights...")
-    prompt = f"""
-You are an analytical Amazon feedback expert.
-Review: \"{review}\"
-Sentiment Scores: {sentiment_results}
-Top Keywords: {[kw for kw, _ in keywords]}
-
-Please complete the following:
-- ANALYSIS: A concise paragraph (3 sentences) interpreting customer sentiment.
-- RECOMMENDATIONS: Three separate paragraphs with actionable suggestions (max 30 words each).
-"""
-    response = generation_pipeline(prompt)
-    output = response[0]["generated_text"]
-    st.markdown(output)
+    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 = (
+            "You are a senior product quality and customer experience specialist at an e-commerce food retailer.
+
+"
+            f"Customer Review:
+\"{review}\"
+
+"
+            "Please analyze this feedback and provide **three** distinct, actionable improvement recommendations designed to reduce customer pain points.
+"
+            "For each recommendation, include:
+"
+            "  1. **Recommendation Title**: a concise summary of the action.
+"
+            "  2. the specific issue or frustration extracted from the review.
+"
+            "  3. why this action addresses the pain point and how it will improve the customer experience.
+"
+            "  4. a bullet-point list of 3–5 clear steps for operations or product teams to execute.
+"
+            "  5. how to measure the impact.
+
+"
+            "Write each recommendation in at least 5–7 sentences, grounding every detail in the customer's own words. "
+            "Avoid generic advice—focus on specifics from the review.
+
+"
+            "Recommendations:
+"
+        )
+        response = generation_pipeline(prompt)
+        detailed = response[0]["generated_text"]
+        st.markdown(detailed)
+    else:
+        st.info("Detailed recommendations are provided only for Neutral, Negative, or Very Negative reviews.")
 
     # Done
     progress.progress(100)
@@ -145,3 +159,4 @@ Please complete the following:
 
 if __name__ == "__main__":
     main()
+