name_classify/name_classify_api.py

import pandas as pd 
import os, time, shutil, sys
import openai
from fastapi import FastAPI, UploadFile, File, Form
from pydantic import BaseModel
from fastapi.responses import JSONResponse
from datetime import datetime
import uvicorn, socket
from tqdm import tqdm
from fastapi.staticfiles import StaticFiles
from config import *

from functions import split_dataframe_to_dict, extract_list_from_string
sys.path.append(os.path.join(os.path.dirname(__file__), 'bert'))
import torch
# from model import BertClassifier
from bert.model import BertClassifier
from transformers import BertTokenizer, BertConfig

app = FastAPI()
app.mount("/data", StaticFiles(directory='./process'), name="static")
class ClassificationRequest(BaseModel):
    path: str
    client_id: str
    one_key: str
    name_column: str
    api_key: str = "sk-iREtaVNjamaBArOTlc_2BfGFJVPiU-9EjSFMUspIPBT3BlbkFJxS0SMmKZD9L9UumPczee4VKawCwVeGBQAr9MgsWGkA"
    proxy: bool = False
    chunk_size: int = 100

class ClassificationRequestBert(BaseModel):
    path: str
    client_id: str
    name_column: str

bert_config = BertConfig.from_pretrained(pre_train_model)
# 定义模型
model = BertClassifier(bert_config, len(label_revert_map.keys()))
# 加载训练好的模型
model.load_state_dict(torch.load(model_save_path, map_location=torch.device('cpu')))
model.eval()
tokenizer = BertTokenizer.from_pretrained(pre_train_model)

def bert_predict(text):
    token = tokenizer(text, add_special_tokens=True, padding='max_length', truncation=True, max_length=512)
    input_ids = token['input_ids']
    attention_mask = token['attention_mask']
    token_type_ids = token['token_type_ids']

    input_ids = torch.tensor([input_ids], dtype=torch.long)
    attention_mask = torch.tensor([attention_mask], dtype=torch.long)
    token_type_ids = torch.tensor([token_type_ids], dtype=torch.long)

    predicted = model(
        input_ids,
        attention_mask,
        token_type_ids,
    )
    pred_label = torch.argmax(predicted, dim=1).numpy()[0]
    
    return label_revert_map[pred_label]
def predict_excel(file_path, name_col, temp_path, save_path, chunksize=5):
    # 初始化变量
    error_file_name, error_file_extension = os.path.splitext(os.path.basename(save_path))
    # 添加后缀
    error_file = error_file_name + '_error' + error_file_extension
    origin_csv_file = error_file_name + '_origin.csv'
    # 生成新的文件路径
    error_file_path = os.path.join(os.path.dirname(save_path), error_file)
    origin_csv_path = os.path.join(os.path.dirname(save_path), origin_csv_file)
    total_processed = 0
    df_origin = pd.read_excel(file_path)
    df_error = pd.DataFrame(columns=df_origin.columns)
    df_origin.to_csv(origin_csv_path, index=False)
    # 按块读取 CSV 文件
    
    for chunk in tqdm(pd.read_csv(origin_csv_path, chunksize=chunksize, iterator=True), desc='Processing', unit='item'):
        try:
            # 对每个块进行处理
            chunk['classify'] = chunk[name_col].apply(bert_predict)
            # 增量保存处理结果
            if total_processed == 0:
                chunk.to_csv(temp_path, mode='w', index=False)
            else:
                chunk.to_csv(temp_path, mode='a', header=False, index=False)
            # 更新已处理的数据量
            total_processed += len(chunk)
        except Exception as e:
            df_error = pd.concat([df_error, chunk])
    df_final = pd.read_csv(temp_path)
    df_final.to_excel(save_path)
    os.remove(origin_csv_path)
    if len(df_error) == 0:
        return save_path, '', 0
    else:
        df_error.to_excel(error_file_path)
        return save_path, error_file_path, len(df_error)
def remove_files():
    current_time = time.time()
    TIME_THRESHOLD_FILEPATH = 30 * 24 * 60 * 60
    TIME_THRESHOLD_FILE = 10 * 24 * 60 * 60
    for root, dirs, files in os.walk(basic_path, topdown=False):
        # 删除文件
        for file in files:
            file_path = os.path.join(root, file)
            if current_time - os.path.getmtime(file_path) > TIME_THRESHOLD_FILE:
                print(f"删除文件: {file_path}")
                os.remove(file_path)
        # 删除文件夹
        for dir in dirs:
            dir_path = os.path.join(root, dir)
            if current_time - os.path.getmtime(dir_path) > TIME_THRESHOLD_FILEPATH:
                print(f"删除文件夹: {dir_path}")
                shutil.rmtree(dir_path)
@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile = File(...), client_id: str = Form(...)):
    user_directory = f'{basic_path}/{client_id}'
    if not os.path.exists(user_directory):
        os.makedirs(user_directory)
        os.chmod(user_directory, 0o777)  # 设置用户目录权限为777
    file_location = os.path.join(user_directory, file.filename)
    try:
        with open(file_location, "wb+") as file_object:
            file_object.write(file.file.read())
        os.chmod(file_location, 0o777)  # 设置文件权限为777
        return JSONResponse(content={
            "message": f"文件 '{file.filename}' 上传成功",
            "client_id": client_id,
            "file_path": file_location
        }, status_code=200)
    except Exception as e:
        return JSONResponse(content={"message": f"发生错误: {str(e)}"}, status_code=500)
    
@app.post("/classify_openai/")
async def classify_data(request: ClassificationRequest):
    try:
        remove_files()
        work_path = f'{basic_path}/{request.client_id}'
        if not os.path.exists(work_path):
            os.makedirs(work_path, exist_ok=True)
        timestamp_str = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
        df_origin = pd.read_excel(request.path)
        df_origin['name'] = df_origin[request.name_column]
        df_origin['classify'] = ''
        df_use = df_origin[['name', 'classify']]
        deal_result = split_dataframe_to_dict(df_use, request.chunk_size)
        # 生成当前时间的时间戳字符串
        
        temp_csv = work_path + '/' + timestamp_str + 'output_temp.csv'
        final_file_name, final_file_extension = os.path.splitext(os.path.basename(request.path))
        # 添加后缀
        final_file = final_file_name + '_classify' + final_file_extension

        # 生成新的文件路径
        new_file_path = os.path.join(os.path.dirname(request.path), final_file)

        if not request.proxy:
            print(f'用户{request.client_id}正在使用直连的gpt-API')
            client = openai.OpenAI(api_key=request.api_key, base_url=openai_url)
        else:
            client = openai.OpenAI(api_key=request.api_key, base_url=proxy_url)
        for name, value in tqdm(deal_result.items(), desc='Processing', unit='item'):
            try:
                message = [
                    {'role':'system', 'content': cls_system_prompt},
                    {'role':'user', 'content':user_prompt.format(chunk=str(value))}
                ]
                # result_string = post_openai(message)
                response = client.chat.completions.create(model='gpt-4',messages=message)
                result_string = response.choices[0].message.content
                result = extract_list_from_string(result_string)
                if result:
                    df_output = pd.DataFrame(result)
                    df_output.to_csv(temp_csv, mode='a', header=True, index=False)
                else:
                    continue
            except Exception as e:
                print(f'{name}出现问题啦, 错误为:{e} 请自行调试')
        if os.path.exists(temp_csv):
            df_result = pd.read_csv(temp_csv)
            df_final = df_origin.merge(df_result, on='name', how='left').drop_duplicates(subset=[request.one_key,'name'], keep='first')
            df_final.to_excel(new_file_path)
            return {"message": "分类完成", "output_file": file_base_url + new_file_path.split(basic_path)[1]}
        else:
            return {"message": "文件没能处理成功"}
    except Exception as e:
        return {"message": f"处理出现错误: {e}"}

@app.post("/classify_bert/")
async def classify_data(request: ClassificationRequestBert):
    remove_files()
    work_path = f'{basic_path}/{request.client_id}'
    if not os.path.exists(work_path):
        os.makedirs(work_path, exist_ok=True)
    final_file_name, final_file_extension = os.path.splitext(os.path.basename(request.path))
    # 添加后缀
    final_file = final_file_name + '_classify' + final_file_extension

    # 生成新的文件路径
    new_file_path = os.path.join(os.path.dirname(request.path), final_file)
    timestamp_str = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
    temp_csv = work_path + '/' + timestamp_str + 'output_temp.csv'
    save_path, error_path, error_len = predict_excel(request.path, request.name_column, temp_path=temp_csv ,save_path=new_file_path)
    if error_len == 0:
        return {"message": "分类完成", "output_file": file_base_url + save_path.split(basic_path)[1]}
    else:
        return {"message": "分类完成只完成部分", "output_file": file_base_url + save_path.split(basic_path)[1], "output_file_nonprocess":file_base_url + save_path.split(error_path)[1],}
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=port)