Task2/Task2_knn_1.py

from knn_prompt import generate_prompt
from LLM import zero_shot
from save_response import save_responses_knn
import json

model_series = 'gpt'
model_name='deepseek-ai/DeepSeek-R1'

type = 'yes_no' 

shot_type = 'one_shot'
shot_type = 'two_shot'
shot_type = 'three_shot'

prompt = generate_prompt(type, './data/knn_'+type+'.json', example_num=3)
j=0
q=0

# # 统计目前处理到第几条
# json_path = './output/knn/'+shot_type+'/'+model_name+'.json'
# j=len(json.load(open(json_path,'r',encoding='utf-8')))
# q=len(json.load(open('./output/knn/'+shot_type+'_raw/'+model_name+'.json','r',encoding='utf-8')))

print(j)
# # 当q=j时才继续处理
if q==j:        
    for i in range(j,len(prompt)):

        response = zero_shot(model_series, model_name, prompt[i]+'\n'+'不要包含任何其他无关解释和无意义回复')
        print(prompt[i])
        # print(response)

        # 解析响应并保存
        save_responses_knn(prompt[i], type, i, response, './output/knn/'+shot_type+'/'+model_name+'.json', './output/knn/'+shot_type+'_raw/'+model_name+'.json')

else:
    print('q!=j')