write logs to file

backend/llm.py

@@ -22,7 +22,10 @@ async def stream_openai_response(messages, callback: Callable[[str], Awaitable[N
         params["temperature"] = 0
 
     completion = await client.chat.completions.create(**params)
-
+    full_response = ""
     async for chunk in completion:
         content = chunk.choices[0].delta.content or ""
+        full_response += content
         await callback(content)
+
+    return full_response

backend/main.py

@@ -1,4 +1,5 @@
 # Load environment variables first
+import json
 from dotenv import load_dotenv
 import os
 from datetime import datetime
@@ -14,6 +15,19 @@ from llm import stream_openai_response
 app = FastAPI()
 
 
+def write_logs(prompt_messages, completion):
+    # Create run_logs directory if it doesn't exist
+    if not os.path.exists("run_logs"):
+        os.makedirs("run_logs")
+
+    # Generate a unique filename using the current timestamp
+    filename = datetime.now().strftime("run_logs/messages_%Y%m%d_%H%M%S.json")
+
+    # Write the messages dict into a new file for each run
+    with open(filename, "w") as f:
+        f.write(json.dumps({"prompt": prompt_messages, "completion": completion}))
+
+
 @app.websocket("/generate-code")
 async def stream_code_test(websocket: WebSocket):
     await websocket.accept()
@@ -23,21 +37,12 @@ async def stream_code_test(websocket: WebSocket):
     async def process_chunk(content):
         await websocket.send_json({"type": "chunk", "value": content})
 
-    messages = assemble_prompt("")
-    print(messages)
+    prompt_messages = assemble_prompt("")
 
-    # Create run_logs directory if it doesn't exist
-    if not os.path.exists('run_logs'):
-        os.makedirs('run_logs')
-
-    # Generate a unique filename using the current timestamp
-    filename = datetime.now().strftime('run_logs/messages_%Y%m%d_%H%M%S.json')
-
-    # Write the messages dict into a new file for each run
-    with open(filename, "w") as f:
-        f.write(str(messages))
-
-    await stream_openai_response(
-        messages,
+    completion = await stream_openai_response(
+        prompt_messages,
         lambda x: process_chunk(x),
     )
+
+    # Write the messages dict into a log so that we can debug later
+    write_logs(prompt_messages, completion)