"""
对821道英文问题进行处理
1. 判断是否包含多个子问题，将问题拆分为完整子问题(去掉推理过程，只保留最后结果)
2. 判断题目类型
3. 将题目做成选择题
   对计算题，在数值附近随机生成三个相似答案作为错误选项
   对简答题，与标准答案最相近的其他问题的答案作为三个错误选项
4. 将正确和错误选项随机打乱，生成ABCD选择题的模型
5. 添加prompt，并将选择题包裹在[ANSWER]<answer>[/ANSWER]标签中
6. 模型打分
"""
import json
import threading
from tqdm import tqdm
import concurrent.futures
from openai import OpenAI
import numpy as np
from sklearn.metrics import precision_score, recall_score, f1_score
import re

client = OpenAI(
    api_key="sk-oYh3Xrhg8oDY2gW02c966f31C84449Ad86F9Cd9dF6E64a8d",
    base_url="https://vip.apiyi.com/v1"
)

thread_lock = threading.Lock()

def load_json_data(filepath):
    with open(filepath, 'r') as file:
        data = json.load(file)
    return data

def get_response(input,max_retries=10):
    retries = 0
    while retries<max_retries:
        try:
            response = client.chat.completions.create(
                #
                model="qwen-max-2025-01-25",
                messages= [
                    {"role": "system", "content": "You are an expert in the field of materials science, adept at answering questions related to fundamental aspects of materials science, including material structure, properties, processing, and applications."},
                    {"role": "user", "content": input}
                ],
                temperature=0
            )
            answer = response.choices[0].message.content
            return answer
        except Exception as e:
            print(f"Error in getting LLM response (Attempt {retries + 1}/{max_retries}): {e}")
            retries += 1
    
    print(f"Failed to get response after {max_retries} attempts, returning None.")
    return "error!"

def process_item(item, index):
    question = item['question']
    text = item['choices']['text']
    label = item['choices']['label']
    prompt = item['prompt']
    expected_answer = item['answer'].strip()

    formatted_choices = " ".join([f"({label}) {text}" for label, text in zip(label, text)])
    input = f"{question} {formatted_choices}. {prompt}"
    
    llm_answer = get_response(input)

    return {
        'index': index,
        'question': question,
        'choices': item['choices'],
        'answer': expected_answer,
        'llm_answer': llm_answer
    }

def extract_answer(answer_string):
    match = re.search(r'\[ANSWER\](.*?)\[/ANSWER\]', answer_string)
    if match:
        return match.group(1).strip()
    return None


def parse_answer(answer):
    if answer is None:
        return []
    return [a.strip() for a in answer.split(',')]

def compute_metrics(data):

    true_answers = []
    pred_answers = []
    
    for item in data:
        true_ans = extract_answer(item["answer"])
        pred_ans = extract_answer(item["llm_answer"])
        
        true_answers.append(parse_answer(true_ans))
        pred_answers.append(parse_answer(pred_ans))
    
    correct_counts = []
    for true_ans, pred_ans in zip(true_answers, pred_answers):
        if true_ans and pred_ans and set(true_ans) == set(pred_ans):
            correct_counts.append(1)
        else:
            correct_counts.append(0)

    accuracy = np.mean(correct_counts)

    y_true_multi = []
    y_pred_multi = []
    all_labels = set()
    
    for item in data:
        choices = item["choices"]["label"]
        for label in choices:
            all_labels.add(label)
    
    all_labels = sorted(list(all_labels))
    
    for true_ans, pred_ans in zip(true_answers, pred_answers):
        true_vector = [1 if label in true_ans else 0 for label in all_labels]
        pred_vector = [1 if label in pred_ans else 0 for label in all_labels]
        y_true_multi.append(true_vector)
        y_pred_multi.append(pred_vector)
    
    y_true_multi = np.array(y_true_multi)
    y_pred_multi = np.array(y_pred_multi)

    precision_micro = precision_score(y_true_multi, y_pred_multi, average='micro', zero_division=0)
    recall_micro = recall_score(y_true_multi, y_pred_multi, average='micro', zero_division=0)
    f1_micro = f1_score(y_true_multi, y_pred_multi, average='micro', zero_division=0)
    
    precision_macro = precision_score(y_true_multi, y_pred_multi, average='macro', zero_division=0)
    recall_macro = recall_score(y_true_multi, y_pred_multi, average='macro', zero_division=0)
    f1_macro = f1_score(y_true_multi, y_pred_multi, average='macro', zero_division=0)
    
    return {
        "accuracy": accuracy,
        "precision_micro": precision_micro,
        "recall_micro": recall_micro,
        "f1_micro": f1_micro,
        "precision_macro": precision_macro,
        "recall_macro": recall_macro,
        "f1_macro": f1_macro
    }

def calculate_accuracy_multithreaded(data, max_workers=5):
    results = []

    with tqdm(total=len(data), desc="Processing items") as pbar:
        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:

            future_to_index = {executor.submit(process_item, item, i): i for i, item in enumerate(data)}

            for future in concurrent.futures.as_completed(future_to_index):
                result = future.result()
                results.append(result)
                pbar.update(1)

    results.sort(key=lambda x: x['index'])

    metric = compute_metrics(results)

    return metric, results

def main():
    filepath = '/home/ubuntu/50T/fsy/benchmark-dataset-third/ALL-merge/merged.json'
    data = load_json_data(filepath)
    max_workers = 8

    metric, results = calculate_accuracy_multithreaded(data,max_workers)
    print(f"Accuracy of qwen-max-2025-01-25: {metric}")

    with open('qwen-max-2025-01-25.json', 'w') as f:
        json.dump(results, f, indent=2)

if __name__ == "__main__":
    main()