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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
title = "Python Code Generator"
description = "This is a space to convert English text to Python code using the [codeparrot-small-text-to-code](https://huggingface.co/codeparrot/codeparrot-small-text-to-code) model, a pre-trained Python code generation model trained on a dataset of docstrings and Python code extracted from Jupyter notebooks available at [github-jupyter-text](https://huggingface.co/datasets/codeparrot/github-jupyter-text)."
example = [
["Utility function to calculate the precision of predictions using sklearn metrics", 65, 0.6, 42],
["Let's implement a function that calculates the size of a file called filepath", 60, 0.6, 42],
["Let's implement the Bubble Sort sorting algorithm in an auxiliary function:", 87, 0.6, 42],
["Function to calculate the nth Fibonacci number.", 65, 0.6, 42],
["Function to calculate the factorial of a number.", 65, 0.6, 42],
["Function to reverse a string.", 65, 0.6, 42],
["Function to check if a number is prime.", 65, 0.6, 42],
["Function to generate the Fibonacci sequence up to the nth term.", 65, 0.6, 42],
["Function to generate the factorial sequence up to the nth term.", 65, 0.6, 42],
]
# Change the model to the pre-trained model
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-text-to-code")
model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-text-to-code")
def create_docstring(gen_prompt):
return "\"\"\"\n" + gen_prompt + "\n\"\"\"\n\n"
def generate_code(gen_prompt, max_tokens, temperature=0.6, seed=42):
set_seed(seed)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = create_docstring(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
def save_to_text_file(output_text):
with open("generated_code.txt", "w") as file:
file.write(output_text)
iface = gr.Interface(
fn=generate_code,
inputs=[
gr.Textbox(label="English instructions", placeholder="Enter 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 for generation"
)
],
outputs=gr.Code(label="Generated Python code", language="python", lines=10),
examples=example,
layout="horizontal",
theme="peach",
description=description,
title=title
)
iface.launch()
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