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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
title = "Code Explainer"
description = "This is a space to convert Python code into english text explaining what it does using [codeparrot-small-code-to-text](https://huggingface.co/codeparrot/codeparrot-small-code-to-text),\
a code generation model for Python finetuned on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text) a dataset of Python code followed by a docstring explaining it, the data was originally extracted from Jupyter notebooks."
EXAMPLE_1 = "def sort_function(arr):\n n = len(arr)\n \n # Traverse through all array elements\n for i in range(n):\n \n # Last i elements are already in place\n for j in range(0, n-i-1):\n \n # traverse the array from 0 to n-i-1\n # Swap if the element found is greater\n # than the next element\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]"
EXAMPLE_2 = "from sklearn import model_selection\nX_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=0.2)"
EXAMPLE_3 = "def load_text(file)\n with open(filename, 'r') as f:\n text = f.read()\n return text"
example = [
[EXAMPLE_1, 32, 0.6, 42],
[EXAMPLE_2, 16, 0.6, 42],
[EXAMPLE_3, 11, 0.2, 42],
]
# change model to the finetuned one
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-code-to-text")
model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-code-to-text")
def make_doctring(gen_prompt):
return gen_prompt + f"\n\n\"\"\"\nExplanation:"
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, label="Python code"),
gr.inputs.Slider(
minimum=8,
maximum=256,
step=1,
default=8,
label="Number of tokens to generate",
),
gr.inputs.Slider(
minimum=0,
maximum=2.5,
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 explanation", lines=10),
examples=example,
layout="horizontal",
theme="peach",
description=description,
title=title
)
iface.launch()
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