Update app.py

app.py

@@ -2,6 +2,9 @@ import torch
 from peft import PeftModel, PeftConfig
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
+HUGGING_FACE_USER_NAME = "indikamk"
+model_name = "BLOOMZ_finetuned_Misconceptions"
+
 peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}"
 config = PeftConfig.from_pretrained(peft_model_id)
 model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto')
@@ -10,9 +13,6 @@ tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
 # Load the Lora model
 model = PeftModel.from_pretrained(model, peft_model_id)
 
-HUGGING_FACE_USER_NAME = "indikamk"
-model_name = "BLOOMZ_finetuned_Misconceptions"
-
 def make_inference(sentence):
     batch =  tokenizer(f"### INSTRUCTION\nBelow is a student response to a writen question about an electrical circuit. Please identify whether there is a sequential misconception. A sequential misconception in terms of electric circuits is one in which it is believed that elements that are further “downstream” from a source (such as R2 and R3 in the example circuit of Figure 1) “receive” current after elements closer to the source (R1 in the example circuit). With such a misconception, it is likely that a student will think that changes in R2 have no effect on the potential difference and current associated with R1 or Vs..\n\n### Sentence:\n{sentence}\n### Response:\n", return_tensors='pt')