app.py

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()