Update app.py

app.py

@@ -143,16 +143,40 @@ def initialize_llm():
 
     llm = ChatGroq(
         model="meta-llama/llama-4-scout-17b-16e-instruct",
-        temperature=0.25,
+        temperature=0.05,
         max_tokens=200,
         max_retries=0,
         groq_api_key=groq_api_key
     )
     return llm
 
+@st.cache_resource
+def initialize_llm_high():
+    """Initialize the LLM with Groq API"""
+    # Get API key from Streamlit secrets or environment variable
+    try:
+        groq_api_key = st.secrets["GROQ_API_KEY"]
+    except:
+        groq_api_key = os.getenv("GROQ_API_KEY")
+
+    if not groq_api_key:
+        st.error("⚠️ GROQ_API_KEY not found! Please set it in .streamlit/secrets.toml or as an environment variable.")
+        st.info("Create a file `.streamlit/secrets.toml` with:\n```\nGROQ_API_KEY = \"your-api-key-here\"\n```")
+        st.stop()
+
+    llm = ChatGroq(
+        model="meta-llama/llama-4-scout-17b-16e-instruct",
+        temperature=0.8,
+        max_tokens=200,
+        max_retries=0,
+        groq_api_key=groq_api_key
+    )
+    return llm
+    
 # Initialize database and LLM
 db = setup_database()
 llm = initialize_llm()
+llmhigh = initialize_llm_high()
 
 # Database agent setup
 system_message = """
@@ -497,7 +521,7 @@ def format_customer_response(input_string: str) -> str:
     user_prompt = f"User Query: {user_query}"
 
     # 🔹 Generate response using LLM (system + user messages)
-    response_msg = llm.predict_messages([
+    response_msg = llmhigh.predict_messages([
         SystemMessage(content=system_prompt),
         HumanMessage(content=user_prompt)
     ])