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


title = "CodeGen Generator"
description = "This is a subspace to make code generation with [CodeGen](https://huggingface.co/Salesforce/codegen-16B-mono), it is used in a larger [space](https://huggingface.co/spaces/loubnabnl/Code-generation-models-v1) for model comparison. We use the 6.1B parameters model in this space."
example = [
    ["def print_hello_world():", 8, 0.6, 42],
    ["def get_file_size(filepath):", 24, 0.6, 42],
    ["def count_lines(filename):", 40, 0.6, 42],
    ["def count_words(filename):", 40, 0.6, 42]]
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-mono")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-mono")


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=example,
    layout="horizontal",
    theme="peach",
    description=description,
    title=title
)
iface.launch()