app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline


title = "Python to Text Converter [WIP]"
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 = [
 ["example1", 65, 0.6, 42],
 ["example2", 60, 0.6, 42],
 ["example3", 87, 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()