Update app.py

app.py

@@ -3,14 +3,13 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
 # Load the pre-trained model and tokenizer
-model_name = "microsoft/phi-2"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained("Wh1plashR/energy-saving-recommender-phi2-lora")
+tokenizer = AutoTokenizer.from_pretrained("Wh1plashR/energy-saving-recommender-phi2-lora")
 
 def generate_recommendation(appliance_info):
     prompt = f"Input: {appliance_info}\nOutput:"
     inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=100)
+    outputs = model.generate(**inputs, max_new_tokens=200)
     recommendation = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return recommendation.split("Output:")[-1].strip()