import gradio as gr
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# 모델과 토크나이저 로드
model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

# KMMLU 데이터셋 로드 
# 직접 불러오기 df = pd.read_csv("kmmlu_sample.csv")

from datasets import load_dataset

df = load_dataset("HAERAE-HUB/KMMLU", "Accounting")

def evaluate_model(question, choices):
    prompt = f"질문: {question}\n\n선택지:\n"
    for i, choice in enumerate(choices):
        prompt += f"{chr(65 + i)}. {choice}\n"
    prompt += "\n답변:"

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=1, temperature=0.0)
    
    answer = tokenizer.decode(outputs[0][-1:], skip_special_tokens=True).strip()
    return answer

def run_kmmlu_test(subject):
    subject_df = df[df['subject'] == subject]
    correct = 0
    total = len(subject_df)
    
    results = []
    for _, row in subject_df.iterrows():
        question = row['question']
        choices = [row['A'], row['B'], row['C'], row['D']]
        correct_answer = row['answer']
        
        model_answer = evaluate_model(question, choices)
        is_correct = model_answer == correct_answer
        
        if is_correct:
            correct += 1
        
        results.append(f"질문: {question}\n모델 답변: {model_answer}\n정답: {correct_answer}\n정확도: {'맞음' if is_correct else '틀림'}\n")
    
    accuracy = correct / total
    summary = f"주제: {subject}\n정확도: {accuracy:.2%} ({correct}/{total})\n\n"
    return summary + "\n".join(results)

subjects = df['subject'].unique().tolist()
huggingface-cli login

iface = gr.Interface(
    
    fn=run_kmmlu_test,
    inputs=gr.Dropdown(choices=subjects, label="주제 선택"),
    outputs="text",
    title="Llama 3을 이용한 KMMLU 테스트",
    description="선택한 주제에 대해 KMMLU 테스트를 실행합니다."
)

iface.launch()