| import gradio as gr | |
| import torch | |
| import pandas as pd | |
| from datasets import Dataset, load_dataset | |
| from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model | |
| from transformers import (AutoTokenizer, BitsAndBytesConfig, TrainingArguments, AutoModelForSequenceClassification, Trainer, EarlyStoppingCallback, DataCollatorWithPadding) | |
| import bitsandbytes as bnb | |
| import evaluate | |
| import numpy as np | |
| import random | |
| def process(model, dataset): | |
| dataset_imdb = load_dataset(dataset) | |
| return "Done" | |
| demo = gr.Interface(fn=process, inputs=["model", "dataset"], outputs="text") | |
| demo.launch() |