Update app.py

app.py

@@ -10,8 +10,14 @@ MODEL_NAME = "google/flan-t5-small"
 # -------------------- Model Logic --------------------
 
 # CRITICAL FIX: Simplified and highly directive prompt for the smallest model
-sys_prompt = "Classify the sentiment of the following customer review as either 'positive', 'negative', or 'neutral'. Respond with only one word." 
-
+# sys_prompt = "Classify the sentiment of the following customer review as either 'positive', 'negative', or 'neutral'. Respond with only one word." 
+sys_prompt = """
+        Classify the sentiment of the following customer review as either 'positive', 'negative', or 'neutral'. Respond with only one word.
+        Leverage your expertise in the aviation industry and deep understanding of industry trends to analyze the nuanced expressions and overall tone.
+        It is crucial to accurately identify neutral sentiments, which may indicate a balanced view or neutral stance towards Us Airways. Neutral expressions could involve factual statements without explicit positive or negative opinions.
+        Consider the importance of these neutral sentiments in gauging the public sentiment towards the airline company.
+        For instance, a positive sentiment might convey satisfaction with the airline's services, a negative sentiment could express dissatisfaction, while neutral sentiment may reflect an impartial observation or a neutral standpoint
+    """
 @st.cache_resource
 def load_llm():
     # ... (load_llm function remains identical) ...