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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 = [
['import java.io.*;\nimport java.util.*;\n\npublic class C125 {\n\tpublic static void main(String[] args) throws IOException {\n\t\tBufferedReader r = new BufferedReader(new InputStreamReader(System.in));\n\t\tString s = r.readLine();\n\t\tint n = new Integer(s);\n\t\tSystem.out.println("0 0 "+n);\n\t}\n}\n', 1],
['import java.util.*;\n\npublic class ehab4 {\n public static void main( String[] args ) {\n Scanner in = new Scanner( System.in );\n\tint a = 0, b = 0;\n\tSystem.out.println( "? 0 0 " );\n\tSystem.out.flush();\n\tint c = in.nextInt();\n\tfor ( int i = 29; i >= 0; i-- ) {\n\t System.out.println( "? " + ( a + ( 1 << i ) ) + " " + b );\n\t System.out.flush();\n\t int q1 = in.nextInt();\n\t System.out.println( "? " + a + " " + ( b + ( 1 << i ) ) );\n\t System.out.flush();\n\t int q2 = in.nextInt();\n\t if ( q1 == q2 ) {\n\t\tif ( c == 1 )\n\t\t a += ( 1 << i );\n\t\telse if ( c == -1 )\n\t\t b += ( 1 << i );\n\t\tc = q1;\n\t }\n\t else if ( q1 == -1 ) {\n\t\ta += ( 1 << i );\n\t\tb += ( 1 << i );\n\t }\n\t else if ( q1 == -2 )\n\t\treturn;\n\t}\n\tSystem.out.println( "! " + a + " " + b );\n\tSystem.out.flush();\n }\n}\n', 1]]
# 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 get_label(output):
label = int(output[-1])
labels = ClassLabel(num_classes=7, names=['constant', 'linear', 'np', 'logn', 'quadratic', 'nlogn', 'cubic'])
return labels.int2str(label)
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 = get_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() |