Update app.py

app.py

@@ -1,37 +1,10 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
-from transformers import pipeline
-import os
-import torch
 
 description = """# <p style="text-align: center; color: white;"> 🎅 <span style='color: #ff75b3;'>SantaCoder:</span> Code Generation </p>
 <span style='color: white;'>This is a demo to generate code with <a href="https://huggingface.co/bigcode/santacoder" style="color: #ff75b3;">SantaCoder</a>,
 a 1.1B parameter model for code generation in Python, Java & JavaScript. The model can also do infilling, just specify where you would like the model to complete code
 with the <span style='color: #ff75b3;'>&lt;FILL-HERE&gt;</span> token.</span>"""
 
-token = os.environ["HUB_TOKEN"]
-device="cuda:0"
-
-
-FIM_PREFIX = "<fim-prefix>"
-FIM_MIDDLE = "<fim-middle>"
-FIM_SUFFIX = "<fim-suffix>"
-FIM_PAD = "<fim-pad>"
-EOD = "<|endoftext|>"
-
-GENERATION_TITLE= "<p style='font-size: 16px; color: white;'>Generated code:</p>"
-
-tokenizer_fim = AutoTokenizer.from_pretrained("bigcode/santacoder", use_auth_token=token, padding_side="left")
-
-tokenizer_fim.add_special_tokens({
-  "additional_special_tokens": [EOD, FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD],
-  "pad_token": EOD,
-})
-
-tokenizer = AutoTokenizer.from_pretrained("bigcode/christmas-models", use_auth_token=token)
-model = AutoModelForCausalLM.from_pretrained("bigcode/christmas-models", trust_remote_code=True, use_auth_token=token).to(device)
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
-
 def post_processing(prompt, completion):
     completion = "<span style='color: #ff75b3;'>" + completion + "</span>"
     prompt = "<span style='color: #727cd6;'>" + prompt + "</span>"