add prompt prefix

app.py

@@ -3,11 +3,29 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
 # Load the pre-trained model and tokenizer
-model = AutoModelForCausalLM.from_pretrained("Wh1plashR/energy-saving-recommender-phi2-lora")
-tokenizer = AutoTokenizer.from_pretrained("Wh1plashR/energy-saving-recommender-phi2-lora")
+model_name = "microsoft/phi-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+promptPre = f"""You are an energy-saving expert tasked to help households reduce their monthly electricity bills. 
+Given the user's appliance usage information (including device name, wattage, hours used per day, and days used per week), 
+analyze and recommend specific, practical ways they can reduce their energy consumption.
+
+Always prioritize suggestions like:
+- Reducing usage time or frequency when excessive
+- Replacing high-consumption appliances with more efficient alternatives (e.g., inverter air conditioners, LED lights)
+- Changing habits (e.g., using fans instead of air conditioners when possible)
+- Avoiding unnecessary standby power consumption
+- Considering lifestyle changes (e.g., reading books instead of watching TV)
+
+Format the output clearly with bullet points or short paragraphs.
+Be empathetic, practical, and encouraging. Focus on achievable actions for the user.
+
+Here is the user's input:
+"""
 
 def generate_recommendation(appliance_info):
-    prompt = f"Input: {appliance_info}\nOutput:"
+    prompt = f"Input: promptPre + {appliance_info}\nOutput:"
     inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(**inputs, max_new_tokens=200)
     recommendation = tokenizer.decode(outputs[0], skip_special_tokens=True)