Update app.py

app.py

@@ -32,8 +32,9 @@ Given the user's appliance usage information (device name, wattage, hours used p
 1. Flag the highest energy consumers.
 2. Recommend practical, empathetic, achievable actions.
 3. Suggest appliance swaps (e.g. LED, inverter AC) and habit changes.
-Give at most 5 suggestions and format with bullet points that is <= 120 tokens.
-Here is the summary:
+Give at most 5 suggestions and format with bullet points that is <= 100 tokens.
+Don't add anything to the response besides the recommendation
+Here is the user's input:
 """
 
 def generate_recommendation(appliance_info: str) -> str:
@@ -42,14 +43,21 @@ def generate_recommendation(appliance_info: str) -> str:
     with torch.no_grad():
         outputs = model.generate(
             **inputs,
-            max_new_tokens=120,
+            max_new_tokens=100,
             use_cache=True,
             do_sample=False,
             temperature=0.0
         )
-    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    recommendation = text.split("Recommendations:")[-1].strip()
-    cleaned_recommendation = "\n".join(line.strip() for line in recommendation.splitlines() if line.strip())
+        text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        recommendation = text.split("Recommendations:")[-1].strip()
+        
+        
+        if "Note:" in recommendation:
+            
+            recommendation = recommendation.split("Note:")[0].strip()
+            
+        cleaned_recommendation = "\n".join(line.strip() for line in recommendation.splitlines() if line.strip())
+        
     return cleaned_recommendation
 
 # Define the Gradio interface