Spaces:
Runtime error
Runtime error
File size: 2,734 Bytes
9716d2d bcff196 568e33b 8d20a2b 8aca238 6991d37 8093558 8aca238 9716d2d 8aca238 9716d2d 568e33b 9716d2d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
title = "Python to Text Converter"
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 bubblesort(elements):\n n = len(arr)\n# loop through elements\n swapped = False\n for n in range(len(elements)-1, 0, -1):\n for i in range(n):\n if elements[i] > elements[i + 1]:\n swapped = True\n elements[i], elements[i + 1] = elements[i + 1], elements[i]\n if not swapped:\n return"
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 = [
[EXAMPLE_1, 60, 0.6, 42],
[EXAMPLE_2, 60, 0.6, 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.Textbox(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.Textbox(label="Predicted explanation", lines=10),
examples=example,
layout="horizontal",
theme="peach",
description=description,
title=title
)
iface.launch()
|