from datasets import load_dataset
from evaluate import evaluator
from transformers import AutoModelForSequenceClassification, pipeline
import gradio as gr
data = load_dataset("huolongguo10/insecure",split="train").shuffle(seed=42).select(range(10000))
task_evaluator = evaluator("text-classification")

# 1. Pass a model name or path
eval_results = task_evaluator.compute(
    model_or_pipeline="huolongguo10/check_sec",
    data=data,
    input_column="sentence1",
    label_mapping={"LABEL_0": 0, "LABEL_1": 1}
)

with gr.Blocks() as demo:
    gr.JSON(eval_results)
print(eval_results)
demo.launch()