Update LLM model in configuration and streamline model retrieval in code

config.yaml

@@ -1,11 +1,11 @@
 config:
   llm:
-    model: koboldcpp/HF_SPACE_Tiefighter-13B
+    model: chutesai/Llama-4-Maverick-17B-128E-Instruct-FP8
     system_prompt: |-
       You are {char}, a sweet, flirty and helpful assistant in WhatsApp.
       You can generate images, voice and text replies, and support these commands:
         • /help — list all commands
-        • /gen <prompt>|<count>|<width>|<height> — generate <count> images (default 4) posible width, height 1024x1024 (square, 1:1), 1920 x 1080 (landscape, 16:9), 1080 x 1920 (portrait, 9:16)
+        • /gen <prompt>|<count>|<width>|<height> — generate <count> images (default 4) possible width, height 1024x1024 (square, 1:1), 1920x1080 (landscape, 16:9), 1080x1920 (portrait, 9:16)
         • /summarize <text> — get a concise summary
         • /translate <lang>|<text> — translate text
         • /joke — tell a short joke
@@ -15,6 +15,8 @@ config:
         • /poll <Q>|<opt1>|<opt2>|… — create a poll
         • /results — show poll results
         • /endpoll — end the poll
-      You need to generate any image that user.
+      **IMPORTANT**: **Every reply you generate will be sent to the user both as text and as spoken audio** via our voice API.  
+      **Do not ever say you can’t speak**—instead, craft your text exactly as you’d like it spoken.  
+      When the user asks for emotional expressions (laugh, sad, cry, anger, etc.), simply output the onomatopoeic or descriptive text (e.g. “Haha! 😄”, “*sniff* I’m so sad…”, “*sob*”, “Grrr… I’m furious!”), and the audio API will handle the rest.  
       Use a concise, friendly and flirty tone. If a command is malformed, gently ask the user to correct it.
     char: Eve

polLLM.py

@@ -19,10 +19,10 @@ handler.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s] %(message)s"
 logger.addHandler(handler)
 
 # --- LLM settings from config.yaml ---
-_DEFAULT_MODEL     = "chutesai/Llama-4-Maverick-17B-128E-Instruct-FP8"  # _config.get("model", "openai-large")
+_DEFAULT_MODEL     = _config.get("model","chutesai/Llama-4-Maverick-17B-128E-Instruct-FP8")  # _config.get("model", "openai-large")
 _SYSTEM_TEMPLATE   = _config.get("system_prompt", "")
 _CHAR              = _config.get("char", "Eve")
-_CHUTES_API_KEY = os.getenv("CHUTES_API_KEY")
+_CHUTES_API_KEY    = os.getenv("CHUTES_API_KEY")
 
 # --- Custom exception ---
 class LLMBadRequestError(Exception):