tx_flow_analysis/llmops/main.py

from fastapi import FastAPI, File, UploadFile, Form
import os
from llmops.agents.data_classify_agent import DataClassifyAgent
from llmops.agents.data_manager import DataManager
import csv
from fastapi.responses import StreamingResponse
import io
import urllib.parse
from llmops.complete_agent_flow_rule import run_complete_agent_flow
from llmops.config import DEEPSEEK_API_KEY

# 初始化FastAPI应用
app = FastAPI(
    title="智能体服务<Agents API>",
    description="提供数据分类打标服务",
    version="1.0"
)

@app.get("/")
def root():
    return {"message": "Hello, Agents for you!"}

# 设置文件保存路径
UPLOAD_FOLDER = "uploads"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)



data_classify_agent = DataClassifyAgent()

@app.post("/api/dataset/classify")
async def dataset_classify(file: UploadFile = File(...), industry: str = Form(...)):
    """
    上传原始数据文件（格式CSV)进行分类打标
    Args:
        file: 用户上传的CSV数据文件
        industry: 行业
    """
    # 获取文件的存储路径
    file_path = os.path.join(UPLOAD_FOLDER, file.filename)
    full_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), file_path)

    print(f"上传文件绝对路径：{full_path}")

    try:
        # 将文件内容写入到上传目录中
        with open(full_path, "wb") as f:
            f.write(file.file.read())

        # 读取文件内容
        data_set = DataManager.load_data_from_csv_file(full_path)
        # 对数据进行分类打标
        data_set_classified = data_classify_agent.invoke_data_classify(industry=industry, data_set=data_set, file_name=file.filename)
        # 格式转换 [{}] -> csv 列表形式
        # 生成器函数，用于逐行生成 CSV 内容
        def generate_csv_rows():
            # 创建内存中的字符串缓冲区
            output = io.StringIO()
            writer = csv.writer(output)
            output.write('\ufeff')  # BOM头，确保Excel正确显示中文

            # 写入表头
            writer.writerow(data_classify_agent.fields_order)
            yield output.getvalue()
            output.seek(0)
            output.truncate(0)
            # 逐行写入数据
            for row in data_set_classified:
                writer.writerow([row["txId"], row["txDate"], row["txTime"], row["txAmount"], row["txBalance"],
                                 row["txDirection"], row["txSummary"], row["txCounterparty"], row["createdAt"],
                                 row["businessType"]])
                yield output.getvalue()
                output.seek(0)
                output.truncate(0)

        # 输出文件名（打好标文件）
        output_file = str(file.filename).split(".")[0] + "_label.csv"

        # 对文件名进行URL编码，处理中文等非ASCII字符
        encoded_filename = urllib.parse.quote(output_file)

        # 使用 StreamingResponse 返回 CSV 文件
        return StreamingResponse(
            generate_csv_rows(),
            media_type="text/csv;charset=utf-8-sig",
            headers={
                "Content-Disposition": f"attachment; filename=\"{encoded_filename}\"; filename*=UTF-8''{encoded_filename}"
            }
        )
    except Exception as e:
        print(f"上传文件分类打标异常 {e}")
        import traceback
        traceback.print_exc()


@app.post("/api/report/gen")
async def gen_report(file: UploadFile = File(...), question: str = Form(...), industry: str = Form(...)):
    """
    上传原始数据文件（格式CSV)，输入问题和行业，生成对应的分析报告
    Args:
        file: 用户上传的CSV数据文件
        question: 用户问题
        industry: 行业
    Returns:
        报告的JSON结构
    """
    # 获取文件的存储路径
    file_path = os.path.join(UPLOAD_FOLDER, file.filename)
    full_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), file_path)

    print(f"上传文件绝对路径：{full_path}")

    try:
        # 将文件内容写入到上传目录中
        with open(full_path, "wb") as f:
            f.write(file.file.read())

        # 读取文件内容，标准化文件
        data_set = DataManager.load_data_from_csv_file(full_path)
        # 执行测试
        result = await run_complete_agent_flow(
            question=question,
            industry=industry,
            data=data_set,
            file_name=file.filename,
            api_key=DEEPSEEK_API_KEY,
            session_id="direct-test"
        )
        print(result)
        return {
            "status": 0,
            "message": "success",
            "outline_draft": result["result"]["outline_draft"],
            "computed_metrics": result["result"]["computed_metrics"]
        }
    except Exception as e:
        print(f"生成流水分析报告异常: {e}")
        import traceback
        traceback.print_exc()
        return {
            "status": 1,
            "message": "error",
            "report": {}
        }





if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=3699, reload=True)