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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
title = "Code Generator"
description = "This is a space to convert english text to Python code using with [codeparrot-small-text-to-code](https://huggingface.co/codeparrot/codeparrot-small-text-to-code),\
a code generation model for Python finetuned on [github-jupyter-text](https://huggingface.co/datasets/codeparrot/github-jupyter-text) a dataset of doctrings\
and their Python code extracted from Jupyter notebooks."
example = [
["Utility function to compute the accuracy of predictions using metric from sklearn", 65, 0.6, 42],
["Let's implement a function that computes the size of a file called filepath", 60, 0.6, 42],
["Let's implement bubble sort in a helper function:", 87, 0.6, 42],
]
# change model to the finetuned one
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-text-to-code")
model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-text-to-code")
def make_doctring(gen_prompt):
return "\"\"\"\n" + gen_prompt + "\n\"\"\"\n\n"
def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
set_seed(seed)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = make_doctring(gen_prompt)
generated_text = pipe(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, language="python", label="English instructions"),
gr.inputs.Slider(
minimum=8,
maximum=256,
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 Python code", language="python", lines=10),
examples=example,
layout="horizontal",
theme="peach",
description=description,
title=title
)
iface.launch()
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