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添加ai智能体标书接口，修改ai_agent仅暴露一个方法供外部调用

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ninghongbin
2025-12-02 19:28:37 +08:00
parent 11d857e000
commit bc1f19542f
3 changed files with 259 additions and 58 deletions
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56
AI_Agent.py
View File

@ -8,11 +8,10 @@ from docx import Document
from shutil import which
import requests
from config import OLLAMA_BASE_URL, OLLAMA_MODEL
INPUT_WORD = r"C:\Users\YC\Desktop\1.docx" # 你的招标文件
OUTPUT_WORD = r"C:\Users\YC\Desktop\投标文件-最终版.docx" # 最终输出路径
OLLAMA_MODEL = "alibayram/Qwen3-30B-A3B-Instruct-2507:latest" # 当前最强本地模型
OLLAMA_BASE_URL = "http://192.168.110.5:11434"
# ==================== Ollama 本地调用（支持 128K 上下文 + 长输出）===================
@ -82,7 +81,7 @@ def word_to_md(word_path: str) -> str:
if not pandoc_cmd:
common = [
os.path.expanduser(r"~\AppData\Local\Pandoc\pandoc.exe"),
r"C:\Program Files\Pandoc\pandoc.exe",
r"./util/pandoc.exe",
]
for p in common:
if os.path.exists(p):
@ -255,26 +254,37 @@ def update_word_toc(docx_path: str):
print(f"Word目录自动更新失败（可手动右键更新）：{e}")
# ==================== 主流程 ====================
def main():
print("启动本地 Qwen3-30B 投标文件生成器（128K上下文版）\n")
# ==================== 统一对外暴露的方法（唯一入口）===================
def generate_tender_from_input(input_word_path: str, output_word_path: str) -> bool:
"""
统一对外暴露的投标文件生成方法
Args:
input_word_path: 输入招标文件路径（.docx/.doc）
output_word_path: 输出投标文件路径（.docx）
Returns:
bool: 生成成功返回True，失败返回False
"""
try:
print("启动投标文件生成器（统一入口）\n")
os.makedirs("output", exist_ok=True)
# 1. 转换招标文件
tender_md = word_to_md(INPUT_WORD)
tender_md = word_to_md(input_word_path)
tender_text = Path(tender_md).read_text(encoding="utf-8")
# 2. 生成超级详细目录
outline = generate_full_outline(tender_md)
# 3. 分批生成正文（超长内容
# 3. 分批生成正文
content = batch_fill_content(outline, tender_text)
content = expand_to_50000_words(content)
# 4. 合成最终 Markdown
final_md = f"""# 【投标单位全称】
## {Path(INPUT_WORD).stem} - 投标文件
## {Path(input_word_path).stem} - 投标文件
{outline}
@ -296,8 +306,9 @@ def main():
success = False
pandoc_cmd = which("pandoc") or which("pandoc.exe")
if pandoc_cmd and os.path.exists(pandoc_cmd):
cmd = [pandoc_cmd, final_md_path, "-o", OUTPUT_WORD, "--reference-doc=template.docx"] if os.path.exists(
"template.docx") else [pandoc_cmd, final_md_path, "-o", OUTPUT_WORD]
cmd = [pandoc_cmd, final_md_path, "-o", output_word_path,
"--reference-doc=template.docx"] if os.path.exists(
"template.docx") else [pandoc_cmd, final_md_path, "-o", output_word_path]
if subprocess.run(cmd, capture_output=True).returncode == 0:
success = True
@ -316,12 +327,27 @@ def main():
doc.add_heading(l[5:], 3)
elif l:
doc.add_paragraph(l)
doc.save(OUTPUT_WORD)
doc.save(output_word_path)
update_word_toc(OUTPUT_WORD)
update_word_toc(output_word_path)
print(f"\n大功告成！投标文件已生成：")
print(f" {OUTPUT_WORD}")
print(f" {output_word_path}")
print(f" 总字数约：{len(final_md) // 2} 字")
# 清理临时生成的md文件（可选，保留也可以）
if os.path.exists(tender_md):
os.remove(tender_md)
return True
except Exception as e:
print(f"投标文件生成失败：{str(e)}")
return False
# ==================== 原有主流程（保持不变，方便单独运行）===================
def main():
generate_tender_from_input(INPUT_WORD, OUTPUT_WORD)
os.startfile(OUTPUT_WORD)

4
config.py
View File

@ -64,3 +64,7 @@ class DetectionResponse(BaseModel):
originalImgSize: List[int]
targets: List[dict]
processing_errors: List[str] = []
# 智能体相关
OLLAMA_MODEL = "alibayram/Qwen3-30B-A3B-Instruct-2507:latest" # 当前最强本地模型
OLLAMA_BASE_URL = "http://192.168.110.5:11434"

175
main.py
View File

@ -1,18 +1,30 @@
import io
import os
import time
import tempfile
import shutil
from contextlib import asynccontextmanager
from pathlib import Path
from typing import Optional
import requests
import uvicorn
from PIL import Image
from fastapi import FastAPI, HTTPException
from fastapi import FastAPI, HTTPException, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from pydantic import BaseModel, HttpUrl
from AI_Agent import generate_tender_from_input
from config import DetectionResponse
from process import detect_large_image_from_url
# 配置文件存储路径
UPLOAD_DIR = Path("uploaded_files")
OUTPUT_DIR = Path("generated_tenders")
UPLOAD_DIR.mkdir(exist_ok=True)
OUTPUT_DIR.mkdir(exist_ok=True)
# 全局检测管理器
detector_manager = None
@ -28,9 +40,17 @@ async def lifespan(app: FastAPI):
print(f"初始化失败：{str(e)}")
raise
yield
# 程序关闭时清理临时文件（可选）
print("清理临时文件...")
for file in UPLOAD_DIR.glob("*"):
try:
if file.is_file():
file.unlink()
except Exception as e:
print(f"清理文件 {file} 失败：{e}")
app = FastAPI(lifespan=lifespan, title="目标检测API", version="1.0.0")
app = FastAPI(lifespan=lifespan, title="目标检测与投标文件生成API", version="1.0.0")
# 配置跨域请求
app.add_middleware(
@ -51,6 +71,15 @@ class DetectionProcessRequest(BaseModel):
url: HttpUrl
class TenderGenerateResponse(BaseModel):
"""投标文件生成响应模型"""
status: str
message: str
relative_path: str
file_name: str
file_size: Optional[int] = None
@app.post("/detect_image", response_model=DetectionResponse)
async def run_detection_image(request: DetectionRequest):
# 解析检测类型
@ -114,12 +143,154 @@ async def get_supported_types():
return {"supported_types": []}
@app.post("/generate_tender", response_model=TenderGenerateResponse)
async def generate_tender_file(file: UploadFile = File(...)):
"""
上传招标文件（Word格式），生成投标文件
支持的文件格式：.docx
返回生成文件的相对路径，用于下载接口
"""
upload_path = None
# 验证文件类型
if not file.filename.endswith((".docx", ".doc")):
raise HTTPException(
status_code=400,
detail="仅支持Word文件（.docx或.doc格式）"
)
try:
# 保存上传的文件
timestamp = int(time.time())
file_ext = Path(file.filename).suffix
upload_filename = f"tender_{timestamp}{file_ext}"
upload_path = UPLOAD_DIR / upload_filename
# 保存文件内容
with open(upload_path, "wb") as f:
shutil.copyfileobj(file.file, f)
print(f"已接收上传文件：{upload_path}")
# 生成输出文件名和路径
output_filename = f"投标文件_生成版_{timestamp}.docx"
output_path = OUTPUT_DIR / output_filename
# 调用AI_Agent的统一入口方法（仅这一个调用）
print("开始生成投标文件...")
generate_success = generate_tender_from_input(
input_word_path=str(upload_path),
output_word_path=str(output_path)
)
if not generate_success:
raise Exception("投标文件生成核心流程执行失败")
# 验证生成的文件是否存在
if not output_path.exists() or not output_path.is_file():
raise Exception("投标文件生成后未找到目标文件")
# 计算文件大小
file_size = output_path.stat().st_size
# 构建相对路径（相对于项目根目录）
relative_path = str(output_path.relative_to(Path.cwd()))
print(f"投标文件生成成功：{output_path}")
return {
"status": "success",
"message": "投标文件生成完成",
"relative_path": relative_path,
"file_name": output_filename,
"file_size": file_size
}
except Exception as e:
error_msg = f"生成投标文件失败：{str(e)}"
print(error_msg)
raise HTTPException(
status_code=500,
detail=error_msg
)
finally:
# 关闭上传文件流
await file.close()
# 清理上传的原始文件，节省空间
if upload_path and upload_path.exists():
try:
upload_path.unlink()
print(f"已清理上传文件：{upload_path}")
except Exception as e:
print(f"清理上传文件失败：{e}")
@app.get("/download_file")
async def download_generated_file(relative_path: str):
"""
根据相对路径下载生成的投标文件
Args:
relative_path: 生成文件的相对路径（从/generate_tender接口获取）
"""
# 解析绝对路径，防止路径穿越攻击
try:
abs_path = Path(relative_path).resolve()
# 验证路径是否在允许的输出目录内
if not abs_path.is_relative_to(OUTPUT_DIR.resolve()):
raise HTTPException(
status_code=403,
detail="访问禁止：文件路径不在允许范围内"
)
if not abs_path.exists() or not abs_path.is_file():
raise HTTPException(
status_code=404,
detail="文件不存在或已被删除"
)
# 返回文件下载响应
return FileResponse(
path=abs_path,
filename=abs_path.name,
media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
)
except HTTPException:
raise
except Exception as e:
print(f"文件下载失败：{str(e)}")
raise HTTPException(
status_code=500,
detail=f"文件下载失败：{str(e)}"
)
@app.get("/list_generated_files")
async def list_generated_files():
"""列出所有生成的投标文件（可选接口）"""
files = []
for file in OUTPUT_DIR.glob("*.docx"):
files.append({
"file_name": file.name,
"relative_path": str(file.relative_to(Path.cwd())),
"file_size": file.stat().st_size,
"created_time": file.stat().st_ctime
})
return {
"total": len(files),
"files": files
}
if __name__ == "__main__":
import argparse
# 补充必要的导入（在主函数中导入，避免启动时依赖冲突）
parser = argparse.ArgumentParser()
parser.add_argument("--host", default="0.0.0.0")
parser.add_argument("--port", type=int, default=8000)
parser.add_argument("--reload", action="store_true")
args = parser.parse_args()
uvicorn.run("main:app", host=args.host, port=args.port, reload=args.reload)