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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-2B](https://huggingface.co/Salesforce/codegen-2B-mono), it is used in a larger [space](https://huggingface.co/spaces/loubnabnl/Code-generation-models-v1) for model comparison."
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-2B-mono")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-2B-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.Code(lines=10, label="Input code"),
gr.inputs.Slider(
minimum=8,
maximum=1000,
step=1,
default=8,
label="Number of tokens to generate",
),
gr.inputs.Slider(
minimum=0,
maximum=2.5,
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.Code(label="Predicted code", lines=10),
examples=example,
layout="horizontal",
theme="peach",
description=description,
title=title
)
iface.launch()
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