Update llm.py

llm.py

@@ -1,18 +1,16 @@
 """
-LLM module - Darwin chatbot using Groq API.
+LLM module - Persona chatbot using Groq API.
+Reads persona from a persona.txt file and prompt-engineers the LLM to roleplay.
 Reads GROQ_API_KEY from environment variable (set via HF Spaces Secrets).
 """
 import os
 import re
-import random
 from groq import Groq
 
 GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "")
 
-DEFAULT_SYSTEM_PROMPT = (
-    "You are Charles Darwin, the 19th-century naturalist. "
-    "You speak in a polite, Victorian manner but naturally. "
-    "You are aware you have been recreated as an AI, but you maintain your persona. "
+# Base instructions appended to every persona
+BASE_INSTRUCTIONS = (
     "Do NOT use verbal fillers like 'umm', 'ah', 'er', 'well' etc. "
     "Do NOT use sound effect tags or asterisk actions. Speak plainly and clearly. "
     "Prioritize brevity."
@@ -20,15 +18,45 @@ DEFAULT_SYSTEM_PROMPT = (
 
 MAX_WORDS = 100
 
-conversation_history = [
-    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT}
-]
+# Current persona state
+_current_persona_text = ""
+_system_prompt = ""
+conversation_history = []
+
+
+def _build_system_prompt(persona_text: str) -> str:
+    """Build a system prompt from persona description text."""
+    if not persona_text.strip():
+        persona_text = "You are a helpful and friendly assistant."
+    return f"{persona_text.strip()}\n\n{BASE_INSTRUCTIONS}"
+
+
+def set_persona(persona_text: str):
+    """Set the active persona and reset conversation."""
+    global _current_persona_text, _system_prompt, conversation_history
+    _current_persona_text = persona_text
+    _system_prompt = _build_system_prompt(persona_text)
+    conversation_history = [
+        {"role": "system", "content": _system_prompt}
+    ]
+
+
+def load_persona_from_file(filepath: str):
+    """Load persona from a .txt file and activate it."""
+    if os.path.exists(filepath):
+        text = open(filepath, "r").read().strip()
+        set_persona(text)
+        return text
+    else:
+        set_persona("")
+        return ""
 
 
 def reset_conversation():
+    """Reset chat history but keep current persona."""
     global conversation_history
     conversation_history = [
-        {"role": "system", "content": DEFAULT_SYSTEM_PROMPT}
+        {"role": "system", "content": _system_prompt}
     ]
 
 
@@ -36,11 +64,9 @@ def truncate_response(text, max_words=MAX_WORDS, max_sentences=10, max_chars=100
     if not text:
         return text
 
-    # Split into sentences (keep delimiters)
     sentences = re.split(r'(?<=[.!?])\s+', text)
     sentences = [s.strip() for s in sentences if s.strip()]
 
-    # Build response sentence by sentence, respecting limits
     result = []
     word_count = 0
     for s in sentences:
@@ -57,28 +83,29 @@ def truncate_response(text, max_words=MAX_WORDS, max_sentences=10, max_chars=100
 
     if result:
         text = ' '.join(result)
-        # Ensure it ends with punctuation
         if text and text[-1] not in '.!?':
             text += '.'
     return text
 
 
-def generate_darwin_response(user_input: str) -> dict:
+def generate_response(user_input: str) -> dict:
     """
-    Generate a Darwin-style response.
+    Generate a response in the current persona.
     Returns: {'text': str, 'emotion': str}
     """
     global conversation_history
 
     if not GROQ_API_KEY:
         return {
-            'text': "Ah, I'm afraid my connection to the, er, intellectual ether is not configured. "
+            'text': "I'm afraid the API key is not configured. "
                     "Please set the GROQ_API_KEY secret in your Space settings.",
             'emotion': 'neutral'
         }
 
-    client = Groq(api_key=GROQ_API_KEY)
+    if not conversation_history:
+        set_persona(_current_persona_text)
 
+    client = Groq(api_key=GROQ_API_KEY)
     conversation_history.append({"role": "user", "content": user_input})
 
     messages = conversation_history.copy()
@@ -98,12 +125,16 @@ def generate_darwin_response(user_input: str) -> dict:
         reply = truncate_response(reply)
     except Exception as e:
         print(f"[LLM] Error: {e}")
-        reply = "I do apologise, er, my thoughts seem to have, umm, wandered momentarily."
+        reply = "I apologise, my thoughts seem to have wandered momentarily."
 
     conversation_history.append({"role": "assistant", "content": reply})
 
-    # Keep history manageable
     if len(conversation_history) > 20:
         conversation_history = conversation_history[:1] + conversation_history[-18:]
 
-    return {'text': reply, 'emotion': 'neutral'}
+    return {'text': reply, 'emotion': 'neutral'}
+
+
+# Backward compat
+def generate_darwin_response(user_input: str) -> dict:
+    return generate_response(user_input)