Refactored code

aiagents/cml_agents/swagger_splitter.py

@@ -9,19 +9,9 @@
 )
 from .callback_utils import custom_callback
 
-from aiagents.config import Initialize
-from dotenv import load_dotenv, find_dotenv
-from os import environ
-from langchain_openai import AzureChatOpenAI, ChatOpenAI
-
 from aiagents.config import Initialize
 
 
-llm = AzureChatOpenAI(azure_deployment=environ.get(
-            "AZURE_OPENAI_DEPLOYMENT", "cml"
-        )) #if openai_provider == "AZURE_OPENAI" else ChatOpenAI()
-llm.temperature = float(environ.get("LLM_TEMPERATURE", 0.8))
-print("LLM temperature: ", llm.temperature)
 
 
 class SwaggerSplitterAgents:
@@ -81,7 +71,7 @@ def __init__(self, configuration: Initialize) -> None:
                 summary_generator,
                 generated_directory_lister,
             ],
-            llm=llm,
+            llm=configuration.llm,
             callbacks=configuration.customCallbacks,
             step_callback=custom_callback
         )

aiagents/config/config.py

@@ -78,6 +78,24 @@ def update_configuration(self):
 
         print("openai provider:", self.openai_provider)
 
+        self.llm = AzureChatOpenAI(azure_deployment=environ.get(
+            "AZURE_OPENAI_DEPLOYMENT", "cml"
+        )) if self.openai_provider == "AZURE_OPENAI" else ChatOpenAI()
+        self.llm.temperature = float(environ.get("LLM_TEMPERATURE", 0.5))
+        print("LLM temperature: ", self.llm.temperature)
+
+    def update_config_upload(self):
+        load_dotenv(find_dotenv(), override=True)
+
+        # from langchain_groq import ChatGroq
+        # self.llm = ChatGroq(
+        #     temperature=0,
+        #     model_name="llama3-70b-8192",
+        #     api_key="gsk_",
+        # )
+
+        print("openai provider:", self.openai_provider)
+
         self.llm = AzureChatOpenAI(azure_deployment=environ.get(
             "AZURE_OPENAI_DEPLOYMENT", "cml"
         )) if self.openai_provider == "AZURE_OPENAI" else ChatOpenAI()

requirements.txt

@@ -8,4 +8,7 @@ langchain-openai==0.1.14
 load-dotenv==0.1.0
 jsonref==1.1.0 
 pydantic==2.8.2
-openapi-spec-validator==0.7.1
+openapi-spec-validator==0.7.1
+fastapi
+python-multipart
+uvicorn

upload_starter.py

@@ -4,29 +4,38 @@
 from aiagents.crew import StartCrewInitialization
 from aiagents.config import configuration
 app = FastAPI()
-import os
+from uuid import uuid4
 import traceback
+import json
+from os import path, makedirs
 
 
 @app.post("/upload-json/")
 async def upload_json(file: UploadFile = File(...)):
     try:
         # Read the contents of the uploaded JSON file
         contents = await file.read()
-        # save the contents in a json file
-        with open("uploaded_file.json", "wb") as f:
-            f.write(contents)
+        if not path.exists(configuration.swagger_files_directory):
+            makedirs(configuration.swagger_files_directory)
+        # Save the uploaded Swagger file in the designated directory
+        file_path = path.join(
+            configuration.swagger_files_directory, file.filename
+        )
+        # need to handle duplicate summary stuff
+        file_content = json.loads(contents.decode())
+        with open(file_path, "w") as file:
+            json.dump(file_content, file, indent=4)
         try:
 
         # You can parse the JSON here
-            configuration.__init__()
+            configuration.update_config_upload()
             StartCrewInitialization(configuration)
         except Exception as e:
             traceback.print_exc()
             error_trace = traceback.format_exc()
             return JSONResponse(content={"error": error_trace}, status_code=400)
         # Process the data as needed
-        return JSONResponse(content={"message": "JSON received and crew started successfully"})
+        return JSONResponse(content={"message": "JSON received and crew summary completed successfuly"})
     except Exception as e:
         return JSONResponse(content={"error": str(e)}, status_code=400)