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import gradio as gr | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
title = "Code Compexity Predictor" | |
description = "This is a space to predict complexity of Java code with [CodeParrot-Multi-Complexity](https://huggingface.co/codeparrot/codeparrot-small-multi),\ | |
a multilingual model for code generation, finetuned on [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex), a dataset for complexity prediction of Java code." | |
example = [ | |
["code example 1", "nlogn"], | |
["code example 2", "constant"]] | |
# model to be changed to the finetuned one | |
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-multi") | |
model = AutoModelForSequenceClassification.from_pretrained("codeparrot/codeparrot-small-multi", num_labels=7) | |
def complexity_estimation(gen_prompt, topk): | |
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) | |
output = pipe(gen_prompt)[0] | |
# add label conversion to class | |
label = output['label'] | |
score = output['score'] | |
return label, score | |
iface = gr.Interface( | |
fn=complexity_estimation, | |
inputs=[ | |
gr.Textbox(lines=10, label="Input code"), | |
gr.inputs.Slider( | |
minimum=1, | |
maximum=3, | |
step=1, | |
default=1, | |
label="Number of results to return", | |
), | |
], | |
outputs=[ | |
gr.Textbox(label="Predicted complexity", lines=1) , | |
gr.Textbox(label="Corresponding probability", lines=1) , | |
], | |
examples=example, | |
layout="vertical", | |
theme="peach", | |
description=description, | |
title=title | |
) | |
iface.launch() |