background

bot.py

@@ -494,66 +494,58 @@ async def on_message(message):
 
     # Prüfen, ob der Live-Chat aktiv ist
     if channel_id in live_chats and live_chats[channel_id]["active"]:
-        timestamp = int(time.time())  # Unix-Timestamp
+        
-        message_data = {
+        await live_chat_queue.put((message, message.id, message.author.id, message.author.display_name, message.content))
-            "timestamp": timestamp,
-            "message_id": message.id,
-            "user_id": message.author.id,
-            "nickname": message.author.display_name,
-            "content": message.content
-        }
 
-        # Historie aktualisieren
-        live_chats[channel_id]["messages"].append(message_data)
-        save_chat_history(channel_id, live_chats[channel_id]["messages"])
-
-        # Nachricht zur AI-Warteschlange hinzufügen
-        await live_chat_queue.put((channel_id, message_data))
 
     await client.process_commands(message)
 
 async def process_live_chat_queue():
     while True:
         try:
-            channel_id, message_data = await live_chat_queue.get()
+            if not askmultus_queue.empty():
 
-            # Kontext zusammenstellen: Introduction + Chat-Historie
+                message, message.id, message.author.id, message.author.display_name, message.content = await live_chat_queue.get()
-            introduction = read_file("chat_intro.txt")  # Neue Introduction-Datei
-            chat_history = load_chat_history(channel_id)
-            formatted_history = "\n".join(
-                f"{msg['timestamp']} | {msg['user_id']} | {msg['nickname']}: {msg['content']}"
-                for msg in chat_history
-            )
 
-            # Anfrage an die AI senden
+                live_introduction = read_file("live_introduction.txt")
-            ai_response = await send_to_ai(introduction, formatted_history)
+                live_background_data = read_file("live_background_data.txt")
+                message_data = live_introduction + " background data:" + live_background_data
+                chat_history = load_chat_history(message.channel.id)
 
-            if ai_response.strip() != "::null::":
+                timestamp = int(time.time())  # Unix-Timestamp
-                channel = client.get_channel(channel_id)
-                if channel:
-                    await channel.send(f"**AI:** {ai_response}")
 
-            live_chat_queue.task_done()
+                content = timestamp + "/" + message.id + "/" + message.author.id + "/" + message.author.display_name + ":" + message.content
 
+                chat_history.append({"role": "user", "content": f"{content}"})
+
+                messages = [
+                {"role": "system", "content": message_data},
+                *chat_history
+                ]
+
+                ai_answer = await loop.run_in_executor(executor, lambda: openai_instance.chat.completions.create(
+                        model="model",
+                        messages=messages,
+                        temperature=0.8,
+                        timeout=15,  # Limit waiting time for response
+                ))
+
+                ai_message = ai_answer.choices[0].message.content
+                chat_history.append({"role": "assistant", "content": ai_message})
+                save_chat_history(message.channel.id, chat_history)
+                channel = message.channel
+
+                if ai_message.strip() != "::null::":
+                    if channel:
+                        await channel.send(f"**AI:** {ai_message}")
+
+                live_chat_queue.task_done()
+
+        except asyncio.CancelledError:
+            break
         except Exception as e:
             logger.error(f"Error processing live chat queue: {e}")
-
+        await asyncio.sleep(5)
-async def send_to_ai(introduction, chat_history):
-    """Sendet den Chat-Verlauf mit Introduction an die AI."""
-    try:
-        response = openai_instance.chat.completions.create(
-            model="gpt-4",
-            messages=[
-                {"role": "system", "content": introduction},
-                {"role": "user", "content": f"Hier ist der bisherige Chat-Verlauf:\n{chat_history}\nAntworte auf die letzte Nachricht."}
-            ],
-            temperature=0.7,
-            max_tokens=150
-        )
-        return response.choices[0].message.content
-    except Exception as e:
-        logger.error(f"AI processing error: {e}")
-        return "::null::"
 
 # -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------