Update app.py

app.py

@@ -38,10 +38,7 @@ def load_flant5_pipeline():
     return pipeline(
         "text2text-generation",
         model=seq_model,
-        tokenizer=seq_tok,
-        max_new_tokens=300,
-        do_sample=True,
-        temperature=0.7
+        tokenizer=seq_tok
     )
 
 LABEL_MAP = {
@@ -117,7 +114,7 @@ def main():
     progress.text("Generating detailed recommendations...")
     if max_label in ["Very Negative", "Negative", "Neutral"]:
         prompt = f"""
-You are a senior product quality and customer experience specialist at an e-commerce food retailer.
+You are a product quality and customer experience specialist at an e-commerce food retailer.
 
 Customer Review:
 "{review}"
@@ -126,7 +123,16 @@ Instructions: Analyze the feedback and provide three distinct, actionable improv
 
 Output only the three numbered recommendations (1–3), each with its title, detailed explanation, steps, and impact measure.
 """
-        response = generation_pipeline(prompt)
+        # 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)
     else:
@@ -139,4 +145,3 @@ Output only the three numbered recommendations (1–3), each with its title, det
 
 if __name__ == "__main__":
     main()
-