into services

ai_service/main.py

@@ -2,13 +2,27 @@ import os
 from dotenv import load_dotenv
 from fastapi import FastAPI, Header, HTTPException
 from pydantic import BaseModel
-import openai
+from openai import OpenAI
+import logging
 import redis
 
+LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO").upper()
 AI_TOKEN = os.environ["AI_HANDLER_TOKEN"]
-openai.api_key = os.environ["OPENAI_API_KEY"]
+
+AIclient = OpenAI(
+    api_key=os.environ["OPENAI_API_KEY"],
+)
+
 r = redis.Redis.from_url(os.environ.get("REDIS_URL", "redis://redis:6379"))
 
+# --- Logging Setup ---
+numeric_level = getattr(logging, LOG_LEVEL, logging.INFO)
+logging.basicConfig(
+    level=numeric_level,
+    format="%(asctime)s %(levelname)s %(name)s: %(message)s"
+)
+logger = logging.getLogger(__name__)
+
 class MessagePayload(BaseModel):
     roomId:          str
     userId:          str
@@ -32,15 +46,19 @@ async def message(
 
     # Build prompt (very simple example)
     prompt = f"User {payload.userId} said: {payload.content}\nBot:"
-    chat_response = opeai.ChatCompletion.create(
+    chat_response = AIclient.chat.completions.create(
         model="gpt-3.5-turbo",
         messages=[
-            {"role": "system", "content": "-"},
+            {"role": "system", "content": "You are a helpful assistant."},
             {"role": "user", "content": prompt}
-        ]
+        ],
+        max_tokens=150,
+        n=1,
+        stop=None,
         temperature=0.7,
     )
-    reply = chat_response.choices[0].text.strip()
+
+    reply = chat_response.choices[0].message.content.strip()
 
     # Cache reply for idempotency
     r.set(payload.eventId, reply, ex=3600)