import gradio as gr
import transformers
import torch


def fmt_prompt(prompt: str) -> str:
    return f"""[Instructions]:\n{prompt}\n\n[Response]:"""


    
    model_name = "abacaj/starcoderbase-1b-sft"
    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

    model = (
        transformers.AutoModelForCausalLM.from_pretrained(
            model_name,
        )
        .to("cuda:0")
        .eval()
    )
def chat_fn(prompt):
    #prompt = "Write a python function to sort the following array in ascending order, don't use any built in sorting methods: [9,2,8,1,5]"
    prompt_input = fmt_prompt(prompt)
    inputs = tokenizer(prompt_input, return_tensors="pt").to(model.device)
    input_ids_cutoff = inputs.input_ids.size(dim=1)

    with torch.no_grad():
        generated_ids = model.generate(
            **inputs,
            use_cache=True,
            max_new_tokens=512,
            temperature=0.2,
            top_p=0.95,
            do_sample=True,
            eos_token_id=tokenizer.eos_token_id,
            pad_token_id=tokenizer.pad_token_id,
        )

    completion = tokenizer.decode(
        generated_ids[0][input_ids_cutoff:],
        skip_special_tokens=True,
    )

    print(completion)
    return completion

with gr.Blocks() as app:
    inp = gr.Textbox()
    outp = gr.Textbox()
    btn = gr.Button()

    btn.click(chat_fn,inp,outp)

app.launch()