import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline

device = 'cuda' if torch.cuda.is_available() else 'cpu'

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_0 = ""#!/bin/bash\n# This script removes files larger than 2MB in the current folder\nfind ."
examples = [[EXAMPLE_0, 14, 0.6, 42]]
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):
    set_seed(seed)
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
    generated_text = pipe(gen_prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
    return generated_text


iface = gr.Interface(
    fn=code_generation, 
    inputs=[
        gr.Textbox(lines=10, label="Input code"),
        gr.inputs.Slider(
            minimum=8,
            maximum=256,
            step=1,
            default=8,
            label="Number of tokens to generate",
        ),
        gr.inputs.Slider(
            minimum=0,
            maximum=2,
            step=0.1,
            default=0.6,
            label="Temperature",
        ),
        gr.inputs.Slider(
            minimum=0,
            maximum=1000,
            step=1,
            default=42,
            label="Random seed to use for the generation"
        )
    ],
    outputs=gr.Textbox(label="Predicted code", lines=10),
    examples=examples,
    layout="horizontal",
    theme="peach",
    description=description,
    title=title
)
iface.launch()