Update app.py

app.py

@@ -31,7 +31,7 @@ You are an energy‑saving expert tasked to help households reduce their monthly
 Analyze the following appliance usage data, which is provided in the format "Appliance Name: Wattage, hours/day, days/week". 
 1. Flag the highest energy consumers.
 2. Recommend practical, empathetic, achievable actions.
-3. Suggest appliance swaps (e.g. LED, inverter AC) and habit changes.
+3. Suggest appliance swaps (e.g. Incandescent to LED, inverter AC) and habit changes.
 Provide at most 5 recommendation bullet points and stop there.
 Input Data:
 """
@@ -42,22 +42,19 @@ def generate_recommendation(appliance_info: str) -> str:
     with torch.no_grad():
         outputs = model.generate(
             **inputs,
-            max_new_tokens=100,
+            max_new_tokens=120,
             use_cache=True,
             do_sample=False,
             temperature=0.0
         )
-        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
+    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract only the part after "Recommendations:"
+    recommendation = text.split("Recommendations:")[-1].strip()
+    # Keep only the first 5 numbered lines
+    lines = recommendation.splitlines()
+    filtered_lines = [line for line in lines if line.strip() and line.strip()[0].isdigit()][:5]
+    return "\n".join(filtered_lines)
+
 
 # Define the Gradio interface
 iface = gr.Interface(