Update app.py

app.py

@@ -1,5 +1,3 @@
-#import gradio as gr
-
 import os
 os.environ["CUDA_VISIBLE_DEVICES"]="0"
 import torch
@@ -18,12 +16,12 @@ model = AutoModelForCausalLM.from_pretrained(
 tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-3b", load_in_8bit_fp32_cpu_offload=True)
 
 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')
+    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')
 
 	with torch.cuda.amp.autocast():
-    	output_tokens = model.generate(**batch, max_new_tokens=200)
+        output_tokens = model.generate(**batch, max_new_tokens=200)
 
-	return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
 
 if __name__ == "__main__":
     # make a gradio interface