Update app.py

app.py

@@ -1,17 +1,18 @@
+import torch
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
 
-#https://huggingface.co/spaces/lvwerra/codeparrot-generation
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
-title = "CodeParrot Generator 🦜"
-description = "This is a subspace to make code generation with [CodeParrot](https://huggingface.co/lvwerra/codeparrot), it is used in a larger [space](https://huggingface.co/spaces/loubnabnl/Code-generation-models-v1) for model comparison. For more flexibilty in sampling, you can find another demo for CodeParrot [here](https://huggingface.co/spaces/lvwerra/codeparrot-generation)."
+title = "Santacoder bash/shell Completion 🎅"
+description = "This is a subspace to make code generation with [SantaCoder fine-tuned on The Stack bash/shell](https://huggingface.co/mrm8488/santacoder-finetuned-the-stack-bash-4)"
 example = [
     ["def print_hello_world():", 8, 0.6, 42],
     ["def get_file_size(filepath):", 40, 0.6, 42],
     ["def count_lines(filename):", 40, 0.6, 42],
     ["def count_words(filename):", 40, 0.6, 42]]
-tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot")
-model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot", low_cpu_mem_usage=True)
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/santacoder-finetuned-the-stack-bash-4")
+model = AutoModelForCausalLM.from_pretrained("mrm8488/santacoder-finetuned-the-stack-bash-4", trust_remote_code=True).to(device)
 
 
 def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):