Update app.py

app.py

@@ -33,11 +33,17 @@ def load_keybert_model():
 # ─── FLAN-T5 Generation Pipeline ────────────────────────────────────────────
 @st.cache_resource
 def load_flant5_pipeline():
-    # High-level helper for text2text generation
+    # Explicitly load the Seq2Seq model & tokenizer to avoid truncation/classification fallback
+    seq_tok = AutoTokenizer.from_pretrained("google/flan-t5-base")
+    seq_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
     return pipeline(
         "text2text-generation",
-        model="google/flan-t5-base",
-        tokenizer="google/flan-t5-base"
+        model=seq_model,
+        tokenizer=seq_tok,
+        # ensure we generate up to 200 new tokens
+        max_new_tokens=200,
+        do_sample=True,
+        temperature=0.7
     )
 
 LABEL_MAP = {
@@ -119,20 +125,17 @@ def main():
     # FLAN-T5 Analysis & Suggestions
     progress.text("Generating insights...")
     prompt = f"""
-You are an analytical amazon feedback expert.
+You are an analytical Amazon feedback expert.
 Review: \"{review}\"
 Sentiment Scores: {sentiment_results}
 Top Keywords: {[kw for kw, _ in keywords]}
 
-Tasks:
-1. Analysis: Write a concise paragraph (3 sentences) interpreting customer sentiment by combining the scores and keywords.
-2. Recommendations: Three separate paragraphs with actionable suggestions (max 30 words each).
+Please complete the following:
+- ANALYSIS: A concise paragraph (3 sentences) interpreting customer sentiment.
+- RECOMMENDATIONS: Three separate paragraphs with actionable suggestions (max 30 words each).
 """
-    output = generation_pipeline(
-        prompt,
-        max_length=200,
-        do_sample=False
-    )[0]['generated_text']
+    response = generation_pipeline(prompt)
+    output = response[0]["generated_text"]
     st.markdown(output)
 
     # Done