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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
title = "Code Compexity Predictor [WIP]"
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, fine-tuned on [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex), a dataset for complexity prediction of Java code."
#add examples
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() |