Jelajahi Sumber

报告生成web接口调整

jiaqiang 3 hari lalu
induk
melakukan
d2a6d069da
1 mengubah file dengan 20 tambahan dan 45 penghapusan
  1. 20 45
      llmops/main.py

+ 20 - 45
llmops/main.py

@@ -6,8 +6,8 @@ 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
+from llmops.complete_agent_flow_rule import run_complete_agent_flow, run_flow
+from llmops.config import DEEPSEEK_API_KEY, LLM_API_KEY, LLM_BASE_URL, LLM_MODEL_NAME
 from llmops.agents.data_stardard import TransactionParserAgent
 from llmops.config import multimodal_api_url
 
@@ -102,15 +102,6 @@ async def dataset_classify(file: UploadFile = File(...), industry: str = Form(..
 
 
 
-
-
-
-# 数据标准化agent
-standard_agent = TransactionParserAgent(
-            api_key=DEEPSEEK_API_KEY,
-            multimodal_api_url=multimodal_api_url
-)
-
 @app.post("/api/report/gen")
 async def gen_report(file: UploadFile = File(...), question: str = Form(...), industry: str = Form(...)):
     """
@@ -127,46 +118,30 @@ async def gen_report(file: UploadFile = File(...), question: str = Form(...), in
     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}")
+    print(f"大模型名称:{LLM_MODEL_NAME}")
 
     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())
 
-        # 数据标准化
-        result = await standard_agent.run_workflow_task(full_path)
-        if result["status"] == "success":
-            print(f"🎯 Workflow 任务完成!")
-            # 标准化后的文件
-            standard_file_path = result['file_path']
-            standard_file_name = os.path.basename(standard_file_path)
-            print(f"📂 文件全路径: {standard_file_path}, 文件名:{standard_file_name}")
-
-            # 读取文件内容,标准化文件
-            data_set = DataManager.load_data_from_csv_file(standard_file_path)
-            # 执行测试
-            result = await run_complete_agent_flow(
-                question=question,
-                industry=industry,
-                data=data_set,
-                file_name=standard_file_name,
-                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"]
-            }
-        else:
-            print(f"❌ 任务失败: {result['message']}")
-            return {
-                "status": 1,
-                "message": result['message'],
-                "report": {}
-            }
+        # 执行测试
+        result = await run_flow(
+            question=question,
+            industry=industry,
+            original_file_path=full_path,
+            api_key=LLM_API_KEY,
+            base_url=LLM_BASE_URL,
+            model_name=LLM_MODEL_NAME,
+            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